code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : Optional[int] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_snake_case : str = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
_snake_case : List[str] = {
'distilbert-base-uncased': 512,
'distilbert-base-uncased-distilled-squad': 512,
'distilbert-base-cased': 512,
'distilbert-base-cased-distilled-squad': 512,
'distilbert-base-german-cased': 512,
'distilbert-base-multilingual-cased': 512,
}
_snake_case : int = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = PRETRAINED_INIT_CONFIGURATION
a_ = ["""input_ids""", """attention_mask"""]
a_ = DistilBertTokenizer
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]="[UNK]" , lowerCAmelCase_ : int="[SEP]" , lowerCAmelCase_ : Optional[int]="[PAD]" , lowerCAmelCase_ : int="[CLS]" , lowerCAmelCase_ : Dict="[MASK]" , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Optional[int] , ) -> Optional[int]:
super().__init__(
lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , tokenize_chinese_chars=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ , **lowerCAmelCase_ , )
__lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , lowerCAmelCase_ ) != do_lower_case
or normalizer_state.get('strip_accents' , lowerCAmelCase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , lowerCAmelCase_ ) != tokenize_chinese_chars
):
__lowerCAmelCase = getattr(lowerCAmelCase_ , normalizer_state.pop('type' ) )
__lowerCAmelCase = do_lower_case
__lowerCAmelCase = strip_accents
__lowerCAmelCase = tokenize_chinese_chars
__lowerCAmelCase = normalizer_class(**lowerCAmelCase_ )
__lowerCAmelCase = do_lower_case
def lowercase ( self : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any]=None ) -> Dict:
__lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase ( self : str , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ) -> List[int]:
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
__lowerCAmelCase = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
| 284 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
_snake_case : Dict = pytest.mark.integration
@require_faiss
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : List[Any] ) -> Optional[Any]:
__lowerCAmelCase = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(lowerCAmelCase_ ) for x in np.arange(3_0 ).tolist()]} )
return dset
def lowercase ( self : List[str] ) -> Tuple:
import faiss
__lowerCAmelCase = self._create_dummy_dataset()
__lowerCAmelCase = dset.map(
lambda lowerCAmelCase_ , lowerCAmelCase_ : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=lowerCAmelCase_ , keep_in_memory=lowerCAmelCase_ )
__lowerCAmelCase = dset.add_faiss_index('vecs' , batch_size=1_0_0 , metric_type=faiss.METRIC_INNER_PRODUCT )
__lowerCAmelCase , __lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def lowercase ( self : Optional[Any] ) -> str:
import faiss
__lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=1_0_0 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__lowerCAmelCase , __lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def lowercase ( self : int ) -> Optional[Any]:
import faiss
__lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=lowerCAmelCase_ ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__lowerCAmelCase , __lowerCAmelCase = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def lowercase ( self : Union[str, Any] ) -> List[Any]:
__lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(lowerCAmelCase_ , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def lowercase ( self : Union[str, Any] ) -> Tuple:
from elasticsearch import Elasticsearch
__lowerCAmelCase = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__lowerCAmelCase = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 3_0 )
__lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 2_9}]}}
__lowerCAmelCase = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : str ) -> int:
import faiss
__lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 1_0 )
# single query
__lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
__lowerCAmelCase = 1
__lowerCAmelCase , __lowerCAmelCase = index.search(lowerCAmelCase_ )
self.assertRaises(lowerCAmelCase_ , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__lowerCAmelCase = np.eye(5 , dtype=np.floataa )[::-1]
__lowerCAmelCase , __lowerCAmelCase = index.search_batch(lowerCAmelCase_ )
self.assertRaises(lowerCAmelCase_ , index.search_batch , queries[0] )
__lowerCAmelCase = [scores[0] for scores in total_scores]
__lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowerCAmelCase_ ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , lowerCAmelCase_ )
def lowercase ( self : List[Any] ) -> List[str]:
import faiss
__lowerCAmelCase = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__lowerCAmelCase = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(lowerCAmelCase_ ):
__lowerCAmelCase = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def lowercase ( self : Union[str, Any] ) -> Dict:
import faiss
__lowerCAmelCase = faiss.IndexFlat(5 )
__lowerCAmelCase = FaissIndex(custom_index=lowerCAmelCase_ )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def lowercase ( self : str ) -> Any:
import faiss
__lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=lowerCAmelCase_ ) as tmp_file:
index.save(tmp_file.name )
__lowerCAmelCase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
__lowerCAmelCase = 1
__lowerCAmelCase , __lowerCAmelCase = index.search(lowerCAmelCase_ )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( lowerCAmelCase_ : Union[str, Any] ):
import faiss
__lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
__lowerCAmelCase = 'index.faiss'
__lowerCAmelCase = F"""mock://{index_name}"""
index.save(lowerCAmelCase_, storage_options=mockfs.storage_options )
__lowerCAmelCase = FaissIndex.load(lowerCAmelCase_, storage_options=mockfs.storage_options )
__lowerCAmelCase = np.zeros(5, dtype=np.floataa )
__lowerCAmelCase = 1
__lowerCAmelCase , __lowerCAmelCase = index.search(lowerCAmelCase_ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : Any ) -> int:
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__lowerCAmelCase = Elasticsearch()
__lowerCAmelCase = {'acknowledged': True}
__lowerCAmelCase = ElasticSearchIndex(es_client=lowerCAmelCase_ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__lowerCAmelCase = 'foo'
__lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__lowerCAmelCase , __lowerCAmelCase = index.search(lowerCAmelCase_ )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__lowerCAmelCase = 'foo'
__lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__lowerCAmelCase , __lowerCAmelCase = index.search(lowerCAmelCase_ , request_timeout=3_0 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__lowerCAmelCase = ['foo', 'bar', 'foobar']
__lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__lowerCAmelCase , __lowerCAmelCase = index.search_batch(lowerCAmelCase_ )
__lowerCAmelCase = [scores[0] for scores in total_scores]
__lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowerCAmelCase_ ) , 0 )
self.assertListEqual([1, 1, 1] , lowerCAmelCase_ )
# batched queries with timeout
__lowerCAmelCase = ['foo', 'bar', 'foobar']
__lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__lowerCAmelCase , __lowerCAmelCase = index.search_batch(lowerCAmelCase_ , request_timeout=3_0 )
__lowerCAmelCase = [scores[0] for scores in total_scores]
__lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowerCAmelCase_ ) , 0 )
self.assertListEqual([1, 1, 1] , lowerCAmelCase_ )
| 284 | 1 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'spiece.model'}
_a = {
'vocab_file': {
'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
}
}
class A_ (lowercase__ ):
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_=False , lowercase_=True , lowercase_=False , lowercase_="<s>" , lowercase_="</s>" , lowercase_="<unk>" , lowercase_="<sep>" , lowercase_="<pad>" , lowercase_="<cls>" , lowercase_="<mask>" , lowercase_=["<eop>", "<eod>"] , lowercase_ = None , **lowercase_ , ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else mask_token
UpperCAmelCase_ : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowercase_ , remove_space=lowercase_ , keep_accents=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , additional_special_tokens=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
UpperCAmelCase_ : Union[str, Any] = 3
UpperCAmelCase_ : Any = do_lower_case
UpperCAmelCase_ : List[Any] = remove_space
UpperCAmelCase_ : Optional[int] = keep_accents
UpperCAmelCase_ : List[str] = vocab_file
UpperCAmelCase_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
"See https://pypi.org/project/jieba/ for installation." )
UpperCAmelCase_ : int = jieba
UpperCAmelCase_ : List[Any] = str.maketrans(" \n" , "\u2582\u2583" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def UpperCamelCase__ ( self ):
"""simple docstring"""
return len(self.sp_model )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = self.__dict__.copy()
UpperCAmelCase_ : List[Any] = None
return state
def __setstate__( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ : Any = {}
UpperCAmelCase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
if self.remove_space:
UpperCAmelCase_ : Any = " ".join(inputs.strip().split() )
else:
UpperCAmelCase_ : Union[str, Any] = inputs
UpperCAmelCase_ : str = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
UpperCAmelCase_ : int = unicodedata.normalize("NFKD" , lowercase_ )
UpperCAmelCase_ : int = "".join([c for c in outputs if not unicodedata.combining(lowercase_ )] )
if self.do_lower_case:
UpperCAmelCase_ : List[Any] = outputs.lower()
return outputs
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.preprocess_text(lowercase_ )
UpperCAmelCase_ : List[str] = self.sp_model.encode(lowercase_ , out_type=lowercase_ )
UpperCAmelCase_ : Optional[Any] = []
for piece in pieces:
if len(lowercase_ ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
UpperCAmelCase_ : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_ , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCAmelCase_ : List[str] = cur_pieces[1:]
else:
UpperCAmelCase_ : Dict = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(lowercase_ )
else:
new_pieces.append(lowercase_ )
return new_pieces
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
return self.sp_model.PieceToId(lowercase_ )
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
return self.sp_model.IdToPiece(lowercase_ )
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Dict = "".join(lowercase_ ).replace(lowercase_ , " " ).strip()
return out_string
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None , lowercase_ = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
if token_ids_a is not None:
return ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ )) + [1, 1]
return ([0] * len(lowercase_ )) + [1, 1]
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [self.sep_token_id]
UpperCAmelCase_ : Union[str, Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ):
"""simple docstring"""
if not os.path.isdir(lowercase_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ : List[Any] = os.path.join(
lowercase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , "wb" ) as fi:
UpperCAmelCase_ : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
def UpperCamelCase__ ( self , *lowercase_ , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = super()._decode(*lowercase_ , **lowercase_ )
UpperCAmelCase_ : Dict = text.replace(" " , "" ).replace("\u2582" , " " ).replace("\u2583" , "\n" )
return text
| 23 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_a = 0
_a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_a = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
_a = tuple[int, int]
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ):
"""simple docstring"""
UpperCAmelCase_ : int = pos_x
UpperCAmelCase_ : List[Any] = pos_y
UpperCAmelCase_ : Union[str, Any] = (pos_y, pos_x)
UpperCAmelCase_ : Any = goal_x
UpperCAmelCase_ : Dict = goal_y
UpperCAmelCase_ : Any = g_cost
UpperCAmelCase_ : List[str] = parent
UpperCAmelCase_ : int = self.calculate_heuristic()
UpperCAmelCase_ : Any = self.g_cost + self.h_cost
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = self.pos_x - self.goal_x
UpperCAmelCase_ : Union[str, Any] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(lowercase_ ) + abs(lowercase_ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self , lowercase_ ):
"""simple docstring"""
return self.f_cost < other.f_cost
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowercase_ )
UpperCAmelCase_ : List[Any] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , lowercase_ )
UpperCAmelCase_ : str = [self.start]
UpperCAmelCase_ : list[Node] = []
UpperCAmelCase_ : int = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
UpperCAmelCase_ : List[str] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(lowercase_ )
self.closed_nodes.append(lowercase_ )
UpperCAmelCase_ : str = self.get_successors(lowercase_ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowercase_ )
else:
# retrieve the best current path
UpperCAmelCase_ : Union[str, Any] = self.open_nodes.pop(self.open_nodes.index(lowercase_ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowercase_ )
else:
self.open_nodes.append(lowercase_ )
return [self.start.pos]
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Any = []
for action in delta:
UpperCAmelCase_ : str = parent.pos_x + action[1]
UpperCAmelCase_ : int = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowercase_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowercase_ , lowercase_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowercase_ , ) )
return successors
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = node
UpperCAmelCase_ : int = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCAmelCase_ : Optional[int] = current_node.parent
path.reverse()
return path
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Dict = AStar(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[Any] = AStar(lowercase_ , lowercase_ )
UpperCAmelCase_ : Tuple = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
UpperCAmelCase_ : List[str] = self.fwd_astar.open_nodes.pop(0 )
UpperCAmelCase_ : List[Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
lowercase_ , lowercase_ )
self.fwd_astar.closed_nodes.append(lowercase_ )
self.bwd_astar.closed_nodes.append(lowercase_ )
UpperCAmelCase_ : Tuple = current_bwd_node
UpperCAmelCase_ : str = current_fwd_node
UpperCAmelCase_ : Dict = {
self.fwd_astar: self.fwd_astar.get_successors(lowercase_ ),
self.bwd_astar: self.bwd_astar.get_successors(lowercase_ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(lowercase_ )
else:
# retrieve the best current path
UpperCAmelCase_ : List[Any] = astar.open_nodes.pop(
astar.open_nodes.index(lowercase_ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(lowercase_ )
else:
astar.open_nodes.append(lowercase_ )
return [self.fwd_astar.start.pos]
def UpperCamelCase__ ( self , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.fwd_astar.retrace_path(lowercase_ )
UpperCAmelCase_ : int = self.bwd_astar.retrace_path(lowercase_ )
bwd_path.pop()
bwd_path.reverse()
UpperCAmelCase_ : Any = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
_a = (0, 0)
_a = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_a = time.time()
_a = AStar(init, goal)
_a = a_star.search()
_a = time.time() - start_time
print(f"""AStar execution time = {end_time:f} seconds""")
_a = time.time()
_a = BidirectionalAStar(init, goal)
_a = time.time() - bd_start_time
print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 23 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W15-3049\",\n doi = \"10.18653/v1/W15-3049\",\n pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n title = \"chr{F}++: words helping character n-grams\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n month = sep,\n year = \"2017\",\n address = \"Copenhagen, Denmark\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-4770\",\n doi = \"10.18653/v1/W17-4770\",\n pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
SCREAMING_SNAKE_CASE__ : int = "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"
SCREAMING_SNAKE_CASE__ : Optional[Any] = "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n 'score' (float): The chrF (chrF++) score,\n 'char_order' (int): The character n-gram order,\n 'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n 'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
def __A ( self : Dict ) -> Tuple:
if version.parse(scb.__version__ ) < version.parse('''1.4.12''' ):
raise ImportWarning(
'''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
'''You can install it with `pip install "sacrebleu>=1.4.12"`.''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf'''] , reference_urls=[
'''https://github.com/m-popovic/chrF''',
] , )
def __A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int = CHRF.CHAR_ORDER , SCREAMING_SNAKE_CASE__ : int = CHRF.WORD_ORDER , SCREAMING_SNAKE_CASE__ : int = CHRF.BETA , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = False , ) -> Optional[Any]:
__lowerCamelCase = len(references[0] )
if any(len(SCREAMING_SNAKE_CASE__ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
__lowerCamelCase = [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__ )]
__lowerCamelCase = CHRF(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 270 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : int = [
"WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"WavLMForAudioFrameClassification",
"WavLMForCTC",
"WavLMForSequenceClassification",
"WavLMForXVector",
"WavLMModel",
"WavLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 270 | 1 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
__a , __a , __a :Union[str, Any] = False, False, False
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : Optional[int] = None
_lowerCamelCase : bool = True
_lowerCamelCase : bool = True
_lowerCamelCase : Optional[str] = None
# Automatically constructed
_lowerCamelCase : ClassVar[str] = "dict"
_lowerCamelCase : ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
_lowerCamelCase : str = field(default='Audio' , init=snake_case_ , repr=snake_case_ )
def __call__( self : List[str] ):
return self.pa_type
def __A ( self : Optional[Any] , UpperCAmelCase : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(UpperCAmelCase , UpperCAmelCase ):
return {"bytes": None, "path": value}
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
A_ = BytesIO()
sf.write(UpperCAmelCase , value["array"] , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
A_ = np.frombuffer(value["bytes"] , dtype=np.intaa ).astype(np.floataa ) / 32767
else:
A_ = np.memmap(value["path"] , dtype="h" , mode="r" ).astype(np.floataa ) / 32767
A_ = BytesIO(bytes() )
sf.write(UpperCAmelCase , UpperCAmelCase , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def __A ( self : Optional[int] , UpperCAmelCase : dict , UpperCAmelCase : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
A_ , A_ = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
A_ = xsplitext(UpperCAmelCase )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
A_ = token_per_repo_id or {}
A_ = path.split("::" )[-1]
try:
A_ = string_to_dict(UpperCAmelCase , config.HUB_DATASETS_URL )["repo_id"]
A_ = token_per_repo_id[repo_id]
except (ValueError, KeyError):
A_ = None
with xopen(UpperCAmelCase , "rb" , use_auth_token=UpperCAmelCase ) as f:
A_ , A_ = sf.read(UpperCAmelCase )
else:
A_ , A_ = sf.read(UpperCAmelCase )
A_ = array.T
if self.mono:
A_ = librosa.to_mono(UpperCAmelCase )
if self.sampling_rate and self.sampling_rate != sampling_rate:
A_ = librosa.resample(UpperCAmelCase , orig_sr=UpperCAmelCase , target_sr=self.sampling_rate )
A_ = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def __A ( self : Optional[int] ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def __A ( self : Optional[Any] , UpperCAmelCase : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
A_ = pa.array([None] * len(UpperCAmelCase ) , type=pa.binary() )
A_ = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
A_ = pa.array([None] * len(UpperCAmelCase ) , type=pa.string() )
A_ = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
A_ = pa.array([Audio().encode_example(UpperCAmelCase ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
A_ = storage.field("bytes" )
else:
A_ = pa.array([None] * len(UpperCAmelCase ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
A_ = storage.field("path" )
else:
A_ = pa.array([None] * len(UpperCAmelCase ) , type=pa.string() )
A_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
return array_cast(UpperCAmelCase , self.pa_type )
def __A ( self : Tuple , UpperCAmelCase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(UpperCAmelCase : str ):
with xopen(UpperCAmelCase , "rb" ) as f:
A_ = f.read()
return bytes_
A_ = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
A_ = pa.array(
[os.path.basename(UpperCAmelCase ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
A_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(UpperCAmelCase , self.pa_type ) | 329 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
for char in word:
A_ = ord(__UpperCamelCase )
if not _is_chinese_char(__UpperCamelCase ):
return 0
return 1
def __snake_case ( __UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = set()
for token in tokens:
A_ = len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
if chinese_word:
word_set.add(__UpperCamelCase )
A_ = list(__UpperCamelCase )
return word_list
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
A_ = max([len(__UpperCamelCase ) for w in chinese_word_set] )
A_ = bert_tokens
A_ , A_ = 0, len(__UpperCamelCase )
while start < end:
A_ = True
if is_chinese(bert_word[start] ):
A_ = min(end - start ,__UpperCamelCase )
for i in range(__UpperCamelCase ,1 ,-1 ):
A_ = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 ,start + i ):
A_ = "##" + bert_word[j]
A_ = start + i
A_ = False
break
if single_word:
start += 1
return bert_word
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : LTP ,__UpperCamelCase : BertTokenizer ):
"""simple docstring"""
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = ltp_tokenizer.seg(lines[i : i + 100] )[0]
A_ = [get_chinese_word(__UpperCamelCase ) for r in res]
ltp_res.extend(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = bert_tokenizer(lines[i : i + 100] ,add_special_tokens=__UpperCamelCase ,truncation=__UpperCamelCase ,max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for input_ids, chinese_word in zip(__UpperCamelCase ,__UpperCamelCase ):
A_ = []
for id in input_ids:
A_ = bert_tokenizer._convert_id_to_token(__UpperCamelCase )
input_tokens.append(__UpperCamelCase )
A_ = add_sub_symbol(__UpperCamelCase ,__UpperCamelCase )
A_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__UpperCamelCase ):
if token[:2] == "##":
A_ = token[2:]
# save chinese tokens' pos
if len(__UpperCamelCase ) == 1 and _is_chinese_char(ord(__UpperCamelCase ) ):
ref_id.append(__UpperCamelCase )
ref_ids.append(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
return ref_ids
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
with open(args.file_name ,"r" ,encoding="utf-8" ) as f:
A_ = f.readlines()
A_ = [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ = LTP(args.ltp ) # faster in GPU device
A_ = BertTokenizer.from_pretrained(args.bert )
A_ = prepare_ref(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
with open(args.save_path ,"w" ,encoding="utf-8" ) as f:
A_ = [json.dumps(__UpperCamelCase ) + "\n" for ref in ref_ids]
f.writelines(__UpperCamelCase )
if __name__ == "__main__":
__a :List[Any] = 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')
__a :Dict = parser.parse_args()
main(args) | 329 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
a__ = '''scheduler_config.json'''
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = 1
UpperCAmelCase__ : Dict = 2
UpperCAmelCase__ : Optional[int] = 3
UpperCAmelCase__ : Dict = 4
UpperCAmelCase__ : List[str] = 5
@dataclass
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = 42
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : Dict = SCHEDULER_CONFIG_NAME
UpperCAmelCase__ : Tuple = ["dtype"]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Optional[Any] = True
@classmethod
def __lowercase ( cls , _a = None , _a = None , _a=False , **_a , ) -> Any:
_a : List[Any] = cls.load_config(
pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE , return_unused_kwargs=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
_a : str = cls.from_config(_SCREAMING_SNAKE_CASE , return_unused_kwargs=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '''create_state''' ) and getattr(_SCREAMING_SNAKE_CASE , '''has_state''' , _SCREAMING_SNAKE_CASE ):
_a : str = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def __lowercase ( self , _a , _a = False , **_a ) -> List[str]:
self.save_config(save_directory=_SCREAMING_SNAKE_CASE , push_to_hub=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def __lowercase ( self ) -> Any:
return self._get_compatibles()
@classmethod
def __lowercase ( cls ) -> Tuple:
_a : Tuple = list(set([cls.__name__] + cls._compatibles ) )
_a : Union[str, Any] = importlib.import_module(__name__.split('''.''' )[0] )
_a : Any = [
getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
return compatible_classes
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Optional[int] ) -> List[str]:
"""simple docstring"""
assert len(__a ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__a ) - x.ndim) ) ,__a )
def __UpperCAmelCase ( __a : Optional[Any] ,__a : Tuple=0.9_99 ,__a : Optional[Any]=jnp.floataa ) -> Any:
"""simple docstring"""
def alpha_bar(__a : List[Any] ):
return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
_a : Optional[Any] = []
for i in range(__a ):
_a : Optional[Any] = i / num_diffusion_timesteps
_a : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(__a ) / alpha_bar(__a ) ,__a ) )
return jnp.array(__a ,dtype=__a )
@flax.struct.dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = 42
UpperCAmelCase__ : List[Any] = 42
UpperCAmelCase__ : Any = 42
@classmethod
def __lowercase ( cls , _a ) -> Tuple:
_a : Tuple = scheduler.config
if config.trained_betas is not None:
_a : List[Any] = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
_a : List[Any] = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_a : Tuple = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_a : Tuple = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )
_a : int = 1.0 - betas
_a : List[Any] = jnp.cumprod(_SCREAMING_SNAKE_CASE , axis=0 )
return cls(
alphas=_SCREAMING_SNAKE_CASE , betas=_SCREAMING_SNAKE_CASE , alphas_cumprod=_SCREAMING_SNAKE_CASE , )
def __UpperCAmelCase ( __a : List[Any] ,__a : int ,__a : Tuple ,__a : List[str] ) -> Union[str, Any]:
"""simple docstring"""
_a : List[Any] = state.alphas_cumprod
_a : Tuple = alphas_cumprod[timesteps] ** 0.5
_a : Optional[int] = sqrt_alpha_prod.flatten()
_a : Union[str, Any] = broadcast_to_shape_from_left(__a ,original_samples.shape )
_a : Any = (1 - alphas_cumprod[timesteps]) ** 0.5
_a : Dict = sqrt_one_minus_alpha_prod.flatten()
_a : List[str] = broadcast_to_shape_from_left(__a ,original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : Union[str, Any] ,__a : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = get_sqrt_alpha_prod(__a ,__a ,__a ,__a )
_a : Union[str, Any] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __UpperCAmelCase ( __a : Dict ,__a : str ,__a : Union[str, Any] ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = get_sqrt_alpha_prod(__a ,__a ,__a ,__a )
_a : Union[str, Any] = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 235 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
"""tokenization_roberta""": ["""RobertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""RobertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaForCausalLM""",
"""RobertaForMaskedLM""",
"""RobertaForMultipleChoice""",
"""RobertaForQuestionAnswering""",
"""RobertaForSequenceClassification""",
"""RobertaForTokenClassification""",
"""RobertaModel""",
"""RobertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaForCausalLM""",
"""TFRobertaForMaskedLM""",
"""TFRobertaForMultipleChoice""",
"""TFRobertaForQuestionAnswering""",
"""TFRobertaForSequenceClassification""",
"""TFRobertaForTokenClassification""",
"""TFRobertaMainLayer""",
"""TFRobertaModel""",
"""TFRobertaPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""FlaxRobertaForCausalLM""",
"""FlaxRobertaForMaskedLM""",
"""FlaxRobertaForMultipleChoice""",
"""FlaxRobertaForQuestionAnswering""",
"""FlaxRobertaForSequenceClassification""",
"""FlaxRobertaForTokenClassification""",
"""FlaxRobertaModel""",
"""FlaxRobertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 186 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=5_12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = seq_length
UpperCamelCase__ = is_training
UpperCamelCase__ = use_input_mask
UpperCamelCase__ = use_token_type_ids
UpperCamelCase__ = use_labels
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_multiple_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = hidden_dropout
UpperCamelCase__ = attention_dropout
UpperCamelCase__ = weight_tying
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = type_sequence_label_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = num_labels
UpperCamelCase__ = num_choices
UpperCamelCase__ = scope
def UpperCAmelCase_ (self ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ = None
if self.use_labels:
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__ = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ (self ):
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.prepare_config_and_inputs()
UpperCamelCase__ = True
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = True
UpperCamelCase__ = GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = True
UpperCamelCase__ = GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
# first forward pass
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = output_from_no_past["""hidden_states"""][0]
UpperCamelCase__ = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , )["""hidden_states"""][0]
# select random slice
UpperCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = config_and_inputs
UpperCamelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def UpperCAmelCase_ (self ):
UpperCamelCase__ = GPTNeoXJapaneseModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase_ (self ):
self.config_tester.run_common_tests()
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
# This regression test was failing with PyTorch < 1.3
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase__ = None
self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = """abeja/gpt-neox-japanese-2.7b"""
UpperCamelCase__ = ["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]
UpperCamelCase__ = [
"""データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
"""100年後に必要とされる会社は、「人」が中心の会社です。""",
"""フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
"""国境の長いトンネルを抜けると、そこは雪国だった。""",
"""美味しい日本食といえば、やっぱりお寿司ですよね。""",
]
UpperCamelCase__ = GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = []
for prompt in prompts:
UpperCamelCase__ = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_ids
UpperCamelCase__ = model.generate(SCREAMING_SNAKE_CASE_ , max_length=50 )
UpperCamelCase__ = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
predicted_outputs += generated_string
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 361 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __A:
"""simple docstring"""
@staticmethod
def UpperCAmelCase_ (*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
pass
def __magic_name__ ( __a : Image ):
'''simple docstring'''
UpperCamelCase__ = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def __magic_name__ ( __a : Image ):
'''simple docstring'''
UpperCamelCase__ = np.array(__a )
UpperCamelCase__ = npimg.shape
return {"hash": hashimage(__a ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
SCREAMING_SNAKE_CASE__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
pass
@require_tf
@unittest.skip("""Image segmentation not implemented in TF""" )
def UpperCAmelCase_ (self ):
pass
@slow
@require_torch
def UpperCAmelCase_ (self ):
UpperCamelCase__ = pipeline("""mask-generation""" , model="""facebook/sam-vit-huge""" )
UpperCamelCase__ = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""" , points_per_batch=2_56 )
# Shortening by hashing
UpperCamelCase__ = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
{"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
{"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
{"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
{"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
{"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
{"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
{"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
{"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
{"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
{"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
{"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
{"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
{"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
{"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
{"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
{"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
{"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
{"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
{"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
{"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
{"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
{"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
{"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
{"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
{"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
] , )
# fmt: on
@require_torch
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = """facebook/sam-vit-huge"""
UpperCamelCase__ = pipeline("""mask-generation""" , model=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = image_segmenter(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
UpperCamelCase__ = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
] , )
| 178 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCAmelCase : Any = _symbol_database.Default()
UpperCAmelCase : List[str] = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
UpperCAmelCase : Any = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
UpperCAmelCase : str = None
UpperCAmelCase : Optional[Any] = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
UpperCAmelCase : Tuple = 4_5
UpperCAmelCase : Dict = 1_5_8_1
UpperCAmelCase : int = 1_5_1_7
UpperCAmelCase : int = 1_5_7_0
UpperCAmelCase : List[Any] = 1_5_8_4
UpperCAmelCase : Union[str, Any] = 1_7_9_3
UpperCAmelCase : Tuple = 1_7_9_5
UpperCAmelCase : Optional[Any] = 1_9_1_6
UpperCAmelCase : str = 1_8_6_4
UpperCAmelCase : Dict = 1_9_0_5
UpperCAmelCase : Dict = 1_9_1_9
UpperCAmelCase : Dict = 2_4_2_9
UpperCAmelCase : List[Any] = 2_2_0_8
UpperCAmelCase : Union[str, Any] = 2_4_1_8
UpperCAmelCase : List[str] = 2_3_2_3
UpperCAmelCase : List[str] = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 267 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger()
def __A ( a_ :int , a_ :str , a_ :LevitConfig , a_ :Path , a_ :bool = True) -> Union[str, Any]:
print(F"""Converting {name}...""")
with torch.no_grad():
if hidden_sizes == 1_28:
if name[-1] == "S":
__a : Optional[int] = timm.create_model('''levit_128s''' , pretrained=a_)
else:
__a : List[Any] = timm.create_model('''levit_128''' , pretrained=a_)
if hidden_sizes == 1_92:
__a : List[Any] = timm.create_model('''levit_192''' , pretrained=a_)
if hidden_sizes == 2_56:
__a : Any = timm.create_model('''levit_256''' , pretrained=a_)
if hidden_sizes == 3_84:
__a : Optional[int] = timm.create_model('''levit_384''' , pretrained=a_)
from_model.eval()
__a : Dict = LevitForImageClassificationWithTeacher(a_).eval()
__a : Optional[int] = OrderedDict()
__a : Tuple = from_model.state_dict()
__a : Dict = list(from_model.state_dict().keys())
__a : str = list(our_model.state_dict().keys())
print(len(a_) , len(a_))
for i in range(len(a_)):
__a : int = weights[og_keys[i]]
our_model.load_state_dict(a_)
__a : Union[str, Any] = torch.randn((2, 3, 2_24, 2_24))
__a : Union[str, Any] = from_model(a_)
__a : Optional[int] = our_model(a_).logits
assert torch.allclose(a_ , a_), "The model logits don't match the original one."
__a : List[Any] = name
print(a_)
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name)
__a : Tuple = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name)
print(F"""Pushed {checkpoint_name}""")
def __A ( a_ :Path , a_ :str = None , a_ :bool = True) -> Optional[Any]:
__a : List[Any] = '''imagenet-1k-id2label.json'''
__a : Tuple = 10_00
__a : List[str] = (1, num_labels)
__a : Union[str, Any] = '''huggingface/label-files'''
__a : Dict = num_labels
__a : List[Any] = json.load(open(hf_hub_download(a_ , a_ , repo_type='''dataset''') , '''r'''))
__a : str = {int(a_): v for k, v in idalabel.items()}
__a : int = idalabel
__a : List[str] = {v: k for k, v in idalabel.items()}
__a : Optional[int] = partial(a_ , num_labels=a_ , idalabel=a_ , labelaid=a_)
__a : Optional[int] = {
'''levit-128S''': 1_28,
'''levit-128''': 1_28,
'''levit-192''': 1_92,
'''levit-256''': 2_56,
'''levit-384''': 3_84,
}
__a : int = {
'''levit-128S''': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-128''': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-192''': ImageNetPreTrainedConfig(
hidden_sizes=[1_92, 2_88, 3_84] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-256''': ImageNetPreTrainedConfig(
hidden_sizes=[2_56, 3_84, 5_12] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-384''': ImageNetPreTrainedConfig(
hidden_sizes=[3_84, 5_12, 7_68] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , a_ , names_to_config[model_name] , a_ , a_)
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , a_ , a_ , a_ , a_)
return config, expected_shape
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
A = parser.parse_args()
A = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub) | 160 | 0 |
from collections.abc import Callable
import numpy as np
def UpperCamelCase (lowercase_: Callable , lowercase_: float , lowercase_: float , lowercase_: float , lowercase_: float ) -> np.array:
A__ : List[str] = int(np.ceil((x_end - xa) / step_size ) )
A__ : Union[str, Any] = np.zeros((n + 1,) )
A__ : Dict = ya
A__ : Optional[int] = xa
for k in range(lowercase_ ):
A__ : Tuple = y[k] + step_size * ode_func(lowercase_ , y[k] )
A__ : Tuple = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : int = OrderedDict(
[
# Base model mapping
('albert', 'FlaxAlbertModel'),
('bart', 'FlaxBartModel'),
('beit', 'FlaxBeitModel'),
('bert', 'FlaxBertModel'),
('big_bird', 'FlaxBigBirdModel'),
('blenderbot', 'FlaxBlenderbotModel'),
('blenderbot-small', 'FlaxBlenderbotSmallModel'),
('clip', 'FlaxCLIPModel'),
('distilbert', 'FlaxDistilBertModel'),
('electra', 'FlaxElectraModel'),
('gpt-sw3', 'FlaxGPT2Model'),
('gpt2', 'FlaxGPT2Model'),
('gpt_neo', 'FlaxGPTNeoModel'),
('gptj', 'FlaxGPTJModel'),
('longt5', 'FlaxLongT5Model'),
('marian', 'FlaxMarianModel'),
('mbart', 'FlaxMBartModel'),
('mt5', 'FlaxMT5Model'),
('opt', 'FlaxOPTModel'),
('pegasus', 'FlaxPegasusModel'),
('regnet', 'FlaxRegNetModel'),
('resnet', 'FlaxResNetModel'),
('roberta', 'FlaxRobertaModel'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
('roformer', 'FlaxRoFormerModel'),
('t5', 'FlaxT5Model'),
('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
('vit', 'FlaxViTModel'),
('wav2vec2', 'FlaxWav2Vec2Model'),
('whisper', 'FlaxWhisperModel'),
('xglm', 'FlaxXGLMModel'),
('xlm-roberta', 'FlaxXLMRobertaModel'),
]
)
A_ : Tuple = OrderedDict(
[
# Model for pre-training mapping
('albert', 'FlaxAlbertForPreTraining'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForPreTraining'),
('big_bird', 'FlaxBigBirdForPreTraining'),
('electra', 'FlaxElectraForPreTraining'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('t5', 'FlaxT5ForConditionalGeneration'),
('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
A_ : Tuple = OrderedDict(
[
# Model for Masked LM mapping
('albert', 'FlaxAlbertForMaskedLM'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForMaskedLM'),
('big_bird', 'FlaxBigBirdForMaskedLM'),
('distilbert', 'FlaxDistilBertForMaskedLM'),
('electra', 'FlaxElectraForMaskedLM'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
A_ : Any = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('bart', 'FlaxBartForConditionalGeneration'),
('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
('encoder-decoder', 'FlaxEncoderDecoderModel'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('marian', 'FlaxMarianMTModel'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('pegasus', 'FlaxPegasusForConditionalGeneration'),
('t5', 'FlaxT5ForConditionalGeneration'),
]
)
A_ : Union[str, Any] = OrderedDict(
[
# Model for Image-classsification
('beit', 'FlaxBeitForImageClassification'),
('regnet', 'FlaxRegNetForImageClassification'),
('resnet', 'FlaxResNetForImageClassification'),
('vit', 'FlaxViTForImageClassification'),
]
)
A_ : Union[str, Any] = OrderedDict(
[
('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
]
)
A_ : Tuple = OrderedDict(
[
# Model for Causal LM mapping
('bart', 'FlaxBartForCausalLM'),
('bert', 'FlaxBertForCausalLM'),
('big_bird', 'FlaxBigBirdForCausalLM'),
('electra', 'FlaxElectraForCausalLM'),
('gpt-sw3', 'FlaxGPT2LMHeadModel'),
('gpt2', 'FlaxGPT2LMHeadModel'),
('gpt_neo', 'FlaxGPTNeoForCausalLM'),
('gptj', 'FlaxGPTJForCausalLM'),
('opt', 'FlaxOPTForCausalLM'),
('roberta', 'FlaxRobertaForCausalLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
('xglm', 'FlaxXGLMForCausalLM'),
('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
]
)
A_ : Optional[int] = OrderedDict(
[
# Model for Sequence Classification mapping
('albert', 'FlaxAlbertForSequenceClassification'),
('bart', 'FlaxBartForSequenceClassification'),
('bert', 'FlaxBertForSequenceClassification'),
('big_bird', 'FlaxBigBirdForSequenceClassification'),
('distilbert', 'FlaxDistilBertForSequenceClassification'),
('electra', 'FlaxElectraForSequenceClassification'),
('mbart', 'FlaxMBartForSequenceClassification'),
('roberta', 'FlaxRobertaForSequenceClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
('roformer', 'FlaxRoFormerForSequenceClassification'),
('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
]
)
A_ : Any = OrderedDict(
[
# Model for Question Answering mapping
('albert', 'FlaxAlbertForQuestionAnswering'),
('bart', 'FlaxBartForQuestionAnswering'),
('bert', 'FlaxBertForQuestionAnswering'),
('big_bird', 'FlaxBigBirdForQuestionAnswering'),
('distilbert', 'FlaxDistilBertForQuestionAnswering'),
('electra', 'FlaxElectraForQuestionAnswering'),
('mbart', 'FlaxMBartForQuestionAnswering'),
('roberta', 'FlaxRobertaForQuestionAnswering'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
('roformer', 'FlaxRoFormerForQuestionAnswering'),
('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
]
)
A_ : Dict = OrderedDict(
[
# Model for Token Classification mapping
('albert', 'FlaxAlbertForTokenClassification'),
('bert', 'FlaxBertForTokenClassification'),
('big_bird', 'FlaxBigBirdForTokenClassification'),
('distilbert', 'FlaxDistilBertForTokenClassification'),
('electra', 'FlaxElectraForTokenClassification'),
('roberta', 'FlaxRobertaForTokenClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
('roformer', 'FlaxRoFormerForTokenClassification'),
('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
]
)
A_ : List[str] = OrderedDict(
[
# Model for Multiple Choice mapping
('albert', 'FlaxAlbertForMultipleChoice'),
('bert', 'FlaxBertForMultipleChoice'),
('big_bird', 'FlaxBigBirdForMultipleChoice'),
('distilbert', 'FlaxDistilBertForMultipleChoice'),
('electra', 'FlaxElectraForMultipleChoice'),
('roberta', 'FlaxRobertaForMultipleChoice'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
('roformer', 'FlaxRoFormerForMultipleChoice'),
('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
]
)
A_ : List[str] = OrderedDict(
[
('bert', 'FlaxBertForNextSentencePrediction'),
]
)
A_ : Optional[Any] = OrderedDict(
[
('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
]
)
A_ : Optional[Any] = OrderedDict(
[
('whisper', 'FlaxWhisperForAudioClassification'),
]
)
A_ : List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A_ : int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
A_ : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
A_ : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
A_ : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A_ : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A_ : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A_ : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A_ : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
A_ : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: str = FLAX_MODEL_MAPPING
A_ : Any = auto_class_update(FlaxAutoModel)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
A_ : Union[str, Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Any = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
A_ : Union[str, Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Optional[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
A_ : Tuple = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
A_ : Dict = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Dict = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A_ : Tuple = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Any = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
A_ : int = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A_ : Dict = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
A_ : Union[str, Any] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: str = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
A_ : Optional[Any] = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: str = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
A_ : List[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='image classification'
)
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
A_ : Union[str, Any] = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class _a (_BaseAutoModelClass ):
'''simple docstring'''
UpperCAmelCase__: int = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
A_ : List[str] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)
| 141 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = LxmertConfig.from_json_file(_UpperCamelCase )
print(f"Building PyTorch model from configuration: {config}" )
__lowerCAmelCase = LxmertForPreTraining(_UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _UpperCamelCase )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained 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."
)
A : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 57 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase = input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase = False
for i in range(1 , len(_UpperCamelCase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase = input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
A : Union[str, Any] = [int(x) for x in input().split()]
# inputing elements of the list in one line
A : str = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| 57 | 1 |
from collections import Counter
from timeit import timeit
def __A ( __lowerCamelCase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __A ( __lowerCamelCase = "" ) -> bool:
if len(__lowerCamelCase ) == 0:
return True
a = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
a = {}
for character in lower_case_input_str:
a = character_freq_dict.get(__lowerCamelCase , 0 ) + 1
a = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def __A ( __lowerCamelCase = "" ) -> None:
print("""\nFor string = """ , __lowerCamelCase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(__lowerCamelCase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(__lowerCamelCase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
__UpperCamelCase : Tuple = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
__UpperCamelCase : str = can_string_be_rearranged_as_palindrome_counter(check_str)
print(F'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :List[str] , __magic_name__ :List[str] , __magic_name__ :List[Any]=13 , __magic_name__ :Any=7 , __magic_name__ :Optional[int]=True , __magic_name__ :List[Any]=True , __magic_name__ :Optional[int]=True , __magic_name__ :Union[str, Any]=True , __magic_name__ :Any=99 , __magic_name__ :List[str]=32 , __magic_name__ :List[str]=5 , __magic_name__ :str=4 , __magic_name__ :str=37 , __magic_name__ :Optional[int]="gelu" , __magic_name__ :int=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :List[str]=512 , __magic_name__ :Tuple=16 , __magic_name__ :Tuple=2 , __magic_name__ :List[str]=0.02 , __magic_name__ :Any=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_choices
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = RoFormerConfig(
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=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = FlaxRoFormerModelTester(self )
@slow
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=__magic_name__ )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
a = jnp.array([[0, 1, 2, 3, 4, 5]] )
a = model(__magic_name__ )[0]
a = 5_0000
a = (1, 6, vocab_size)
self.assertEqual(output.shape , __magic_name__ )
a = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __magic_name__ , atol=1E-4 ) )
| 347 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class __snake_case :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=False , lowercase=True , lowercase="None" , lowercase=3 , lowercase=4 , lowercase=None , ) -> Union[str, Any]:
'''simple docstring'''
a__: Union[str, Any] = parent
a__: str = batch_size
a__: List[str] = seq_length
a__: int = is_training
a__: Union[str, Any] = use_input_mask
a__: str = use_token_type_ids
a__: Union[str, Any] = use_labels
a__: Any = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: Optional[Any] = num_attention_heads
a__: Dict = intermediate_size
a__: Tuple = hidden_act
a__: List[Any] = hidden_dropout_prob
a__: Tuple = attention_probs_dropout_prob
a__: int = max_position_embeddings
a__: int = type_vocab_size
a__: Any = type_sequence_label_size
a__: Optional[int] = initializer_range
a__: List[Any] = num_labels
a__: Optional[Any] = num_choices
a__: Union[str, Any] = relative_attention
a__: Optional[int] = position_biased_input
a__: Tuple = pos_att_type
a__: Union[str, Any] = scope
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: str = None
if self.use_input_mask:
a__: List[str] = random_attention_mask([self.batch_size, self.seq_length])
a__: List[Any] = None
if self.use_token_type_ids:
a__: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__: str = None
a__: List[str] = None
a__: List[Any] = None
if self.use_labels:
a__: List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a__: List[Any] = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=lowercase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = TFDebertaVaModel(config=lowercase)
a__: Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: Optional[Any] = [input_ids, input_mask]
a__: Optional[Any] = model(lowercase)
a__: int = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = TFDebertaVaForMaskedLM(config=lowercase)
a__: Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a__: Any = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Dict = self.num_labels
a__: Union[str, Any] = TFDebertaVaForSequenceClassification(config=lowercase)
a__: Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a__: List[Any] = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.num_labels
a__: int = TFDebertaVaForTokenClassification(config=lowercase)
a__: Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a__: Any = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[str]:
'''simple docstring'''
a__: Dict = TFDebertaVaForQuestionAnswering(config=lowercase)
a__: str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a__: List[str] = model(lowercase)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: List[Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
): Tuple = config_and_inputs
a__: Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
a__ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
a__ = (
{
"""feature-extraction""": TFDebertaVaModel,
"""fill-mask""": TFDebertaVaForMaskedLM,
"""question-answering""": TFDebertaVaForQuestionAnswering,
"""text-classification""": TFDebertaVaForSequenceClassification,
"""token-classification""": TFDebertaVaForTokenClassification,
"""zero-shot""": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
a__ = False
a__ = False
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: str = TFDebertaVaModelTester(self)
a__: Any = ConfigTester(self , config_class=lowercase , hidden_size=37)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase)
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase)
@slow
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[int] = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
self.assertIsNotNone(lowercase)
@require_tf
class __snake_case ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet')
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
pass
@slow
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Tuple = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
a__: Optional[int] = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
a__: Any = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
a__: Tuple = model(lowercase , attention_mask=lowercase)[0]
a__: Optional[int] = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]])
tf.debugging.assert_near(output[:, 1:4, 1:4] , lowercase , atol=1e-4)
| 290 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class lowerCamelCase__ ( _a ):
_lowerCAmelCase = '''perceiver'''
def __init__( self : Optional[Any] , _a : int=2_5_6 , _a : Dict=1_2_8_0 , _a : Dict=7_6_8 , _a : Union[str, Any]=1 , _a : Tuple=2_6 , _a : List[Any]=8 , _a : Optional[int]=8 , _a : Any=None , _a : List[str]=None , _a : Dict="kv" , _a : int=1 , _a : Tuple=1 , _a : int="gelu" , _a : int=0.1 , _a : Tuple=0.0_2 , _a : int=1e-12 , _a : List[Any]=True , _a : Optional[Any]=2_6_2 , _a : str=2_0_4_8 , _a : Tuple=5_6 , _a : int=[3_6_8, 4_9_6] , _a : Optional[int]=1_6 , _a : str=1_9_2_0 , _a : Union[str, Any]=1_6 , _a : Optional[Any]=[1, 1_6, 2_2_4, 2_2_4] , **_a : str , ):
super().__init__(**_a )
a__: List[str] =num_latents
a__: Any =d_latents
a__: int =d_model
a__: int =num_blocks
a__: Any =num_self_attends_per_block
a__: int =num_self_attention_heads
a__: List[str] =num_cross_attention_heads
a__: Optional[Any] =qk_channels
a__: Tuple =v_channels
a__: Union[str, Any] =cross_attention_shape_for_attention
a__: Dict =self_attention_widening_factor
a__: Tuple =cross_attention_widening_factor
a__: Tuple =hidden_act
a__: Union[str, Any] =attention_probs_dropout_prob
a__: Union[str, Any] =initializer_range
a__: Dict =layer_norm_eps
a__: Optional[int] =use_query_residual
# masked language modeling attributes
a__: Optional[int] =vocab_size
a__: List[str] =max_position_embeddings
# image classification attributes
a__: str =image_size
# flow attributes
a__: Optional[Any] =train_size
# multimodal autoencoding attributes
a__: Optional[Any] =num_frames
a__: Dict =audio_samples_per_frame
a__: Union[str, Any] =samples_per_patch
a__: int =output_shape
class lowerCamelCase__ ( _a ):
@property
def _lowerCamelCase ( self : List[Any] ):
if self.task == "multiple-choice":
a__: Dict ={0: "batch", 1: "choice", 2: "sequence"}
else:
a__: Optional[Any] ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def _lowerCamelCase ( self : Any ):
return 1e-4
def _lowerCamelCase ( self : List[Any] , _a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _a : int = -1 , _a : int = -1 , _a : int = -1 , _a : bool = False , _a : Optional[TensorType] = None , _a : int = 3 , _a : int = 4_0 , _a : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(_a , _a ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
a__: Tuple =compute_effective_axis_dimension(
_a , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
a__: Union[str, Any] =preprocessor.num_special_tokens_to_add(_a )
a__: Any =compute_effective_axis_dimension(
_a , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_a )
# Generate dummy inputs according to compute batch and sequence
a__: Dict =[" ".join(["a"] ) * seq_length] * batch_size
a__: int =dict(preprocessor(_a , return_tensors=_a ) )
a__: Optional[int] =inputs.pop("input_ids" )
return inputs
elif isinstance(_a , _a ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
a__: str =compute_effective_axis_dimension(_a , fixed_dimension=OnnxConfig.default_fixed_batch )
a__: int =self._generate_dummy_images(_a , _a , _a , _a )
a__: Optional[Any] =dict(preprocessor(images=_a , return_tensors=_a ) )
a__: List[str] =inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 42 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase__ ( _a , _a ):
@register_to_config
def __init__( self : str , _a : int = 7_6_8 , ):
super().__init__()
a__: Optional[Any] =nn.Parameter(torch.zeros(1 , _a ) )
a__: List[str] =nn.Parameter(torch.ones(1 , _a ) )
def _lowerCamelCase ( self : Tuple , _a : Optional[Union[str, torch.device]] = None , _a : Optional[torch.dtype] = None , ):
a__: str =nn.Parameter(self.mean.to(_a ).to(_a ) )
a__: List[Any] =nn.Parameter(self.std.to(_a ).to(_a ) )
return self
def _lowerCamelCase ( self : List[Any] , _a : Dict ):
a__: str =(embeds - self.mean) * 1.0 / self.std
return embeds
def _lowerCamelCase ( self : List[Any] , _a : str ):
a__: Optional[Any] =(embeds * self.std) + self.mean
return embeds
| 42 | 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,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = tempfile.mkdtemp()
_lowerCAmelCase : int = BlipImageProcessor()
_lowerCAmelCase : Dict = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
_lowerCAmelCase : Tuple = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")
_lowerCAmelCase : Optional[Any] = InstructBlipProcessor(__a, __a, __a)
processor.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).tokenizer
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).image_processor
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).qformer_tokenizer
def snake_case__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta)]
_lowerCAmelCase : Any = [Image.fromarray(np.moveaxis(__a, 0, -1)) for x in image_inputs]
return image_inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = InstructBlipProcessor(
tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor(), qformer_tokenizer=self.get_qformer_tokenizer(), )
processor.save_pretrained(self.tmpdirname)
_lowerCAmelCase : str = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
_lowerCAmelCase : int = self.get_image_processor(do_normalize=__a, padding_value=1.0)
_lowerCAmelCase : str = InstructBlipProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=__a, padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, __a)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, __a)
self.assertIsInstance(processor.qformer_tokenizer, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.get_image_processor()
_lowerCAmelCase : int = self.get_tokenizer()
_lowerCAmelCase : int = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : int = self.prepare_image_inputs()
_lowerCAmelCase : Union[str, Any] = image_processor(__a, return_tensors="np")
_lowerCAmelCase : Any = processor(images=__a, 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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.get_image_processor()
_lowerCAmelCase : Optional[Any] = self.get_tokenizer()
_lowerCAmelCase : List[str] = self.get_qformer_tokenizer()
_lowerCAmelCase : Union[str, Any] = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : List[str] = "lower newer"
_lowerCAmelCase : str = processor(text=__a)
_lowerCAmelCase : Dict = tokenizer(__a, return_token_type_ids=__a)
_lowerCAmelCase : Union[str, Any] = qformer_tokenizer(__a, return_token_type_ids=__a)
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key], encoded_processor[key])
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key], encoded_processor["qformer_" + key])
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = self.get_image_processor()
_lowerCAmelCase : List[str] = self.get_tokenizer()
_lowerCAmelCase : Optional[int] = self.get_qformer_tokenizer()
_lowerCAmelCase : List[str] = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : int = "lower newer"
_lowerCAmelCase : Optional[Any] = self.prepare_image_inputs()
_lowerCAmelCase : str = processor(text=__a, images=__a)
self.assertListEqual(
list(inputs.keys()), ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"], )
# test if it raises when no input is passed
with pytest.raises(__a):
processor()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.get_image_processor()
_lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
_lowerCAmelCase : Dict = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCAmelCase : str = processor.batch_decode(__a)
_lowerCAmelCase : List[str] = tokenizer.batch_decode(__a)
self.assertListEqual(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.get_image_processor()
_lowerCAmelCase : List[str] = self.get_tokenizer()
_lowerCAmelCase : Optional[int] = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : Optional[Any] = "lower newer"
_lowerCAmelCase : Tuple = self.prepare_image_inputs()
_lowerCAmelCase : List[str] = processor(text=__a, images=__a)
self.assertListEqual(
list(inputs.keys()), ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"], )
| 36 |
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCamelCase ):
for i in range(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 36 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase_ = [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
lowerCAmelCase_ = {
"vocab_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
"funnel-transformer/small-base": (
"https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
),
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
"funnel-transformer/large-base": (
"https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
),
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
),
},
}
lowerCAmelCase_ = {f"funnel-transformer/{name}": 5_1_2 for name in _model_names}
lowerCAmelCase_ = {f"funnel-transformer/{name}": {"do_lower_case": True} for name in _model_names}
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = FunnelTokenizer
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = 2
def __init__( self, lowercase_=None, lowercase_=None, lowercase_=True, lowercase_="<unk>", lowercase_="<sep>", lowercase_="<pad>", lowercase_="<cls>", lowercase_="<mask>", lowercase_="<s>", lowercase_="</s>", lowercase_=True, lowercase_=True, lowercase_=None, lowercase_="##", **lowercase_, ) -> Dict:
super().__init__(
lowercase_, tokenizer_file=lowercase_, do_lower_case=lowercase_, unk_token=lowercase_, sep_token=lowercase_, pad_token=lowercase_, cls_token=lowercase_, mask_token=lowercase_, bos_token=lowercase_, eos_token=lowercase_, clean_text=lowercase_, tokenize_chinese_chars=lowercase_, strip_accents=lowercase_, wordpieces_prefix=lowercase_, **lowercase_, )
snake_case = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase', lowercase_ ) != do_lower_case
or normalizer_state.get('strip_accents', lowercase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars', lowercase_ ) != tokenize_chinese_chars
):
snake_case = getattr(lowercase_, normalizer_state.pop('type' ) )
snake_case = do_lower_case
snake_case = strip_accents
snake_case = tokenize_chinese_chars
snake_case = normalizer_class(**lowercase_ )
snake_case = do_lower_case
def _lowerCamelCase ( self, lowercase_, lowercase_=None ) -> Optional[int]:
snake_case = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowerCamelCase ( self, lowercase_, lowercase_ = None ) -> List[int]:
snake_case = [self.sep_token_id]
snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCamelCase ( self, lowercase_, lowercase_ = None ) -> Tuple[str]:
snake_case = self._tokenizer.model.save(lowercase_, name=lowercase_ )
return tuple(lowercase_ )
| 332 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 1_0 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(A )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
snake_case = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
snake_case = min(A , A )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(A )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
snake_case = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
snake_case = max(A , A )
# after all swaps are performed, send the values back to main
result_pipe[1].send(A )
def __magic_name__ ( A ) -> str:
snake_case = []
snake_case = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
snake_case = Pipe()
snake_case = Pipe()
process_array_.append(
Process(
target=A , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
snake_case = temp_rs
snake_case = temp_rr
for i in range(1 , len(A ) - 1 ):
snake_case = Pipe()
snake_case = Pipe()
process_array_.append(
Process(
target=A , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
snake_case = temp_rs
snake_case = temp_rr
process_array_.append(
Process(
target=A , args=(
len(A ) - 1,
arr[len(A ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(A ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(A ) ):
snake_case = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __magic_name__ ( ) -> Tuple:
snake_case = list(range(1_0 , 0 , -1 ) )
print('Initial List' )
print(*A )
snake_case = odd_even_transposition(A )
print('Sorted List\n' )
print(*A )
if __name__ == "__main__":
main()
| 332 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowercase_ ):
UpperCamelCase = ['''torch''', '''torchsde''']
def __init__( self :Optional[Any] , *__UpperCamelCase :Optional[int] , **__UpperCamelCase :Optional[Any] ):
requires_backends(self , ["torch", "torchsde"] )
@classmethod
def lowerCamelCase ( cls :Union[str, Any] , *__UpperCamelCase :List[str] , **__UpperCamelCase :Dict ):
requires_backends(cls , ["torch", "torchsde"] )
@classmethod
def lowerCamelCase ( cls :Union[str, Any] , *__UpperCamelCase :Union[str, Any] , **__UpperCamelCase :Dict ):
requires_backends(cls , ["torch", "torchsde"] )
| 292 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case : int = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case : List[Any] = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase=8 ):
A = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
A = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class _UpperCAmelCase ( lowercase_ ):
def __init__( self :Any , __UpperCamelCase :UNetaDConditionModel , __UpperCamelCase :DDPMScheduler , __UpperCamelCase :VQModel , ):
super().__init__()
self.register_modules(
unet=__UpperCamelCase , scheduler=__UpperCamelCase , movq=__UpperCamelCase , )
A = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase ( self :Union[str, Any] , __UpperCamelCase :Tuple , __UpperCamelCase :Dict , __UpperCamelCase :Dict , __UpperCamelCase :List[str] , __UpperCamelCase :Optional[int] , __UpperCamelCase :List[str] ):
if latents is None:
A = randn_tensor(__UpperCamelCase , generator=__UpperCamelCase , device=__UpperCamelCase , dtype=__UpperCamelCase )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
A = latents.to(__UpperCamelCase )
A = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase ( self :Tuple , __UpperCamelCase :Any=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
A = torch.device(f"cuda:{gpu_id}" )
A = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__UpperCamelCase , __UpperCamelCase )
def lowerCamelCase ( self :Dict , __UpperCamelCase :int=0 ):
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
A = torch.device(f"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=__UpperCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
A = None
for cpu_offloaded_model in [self.unet, self.movq]:
A, A = cpu_offload_with_hook(__UpperCamelCase , __UpperCamelCase , prev_module_hook=__UpperCamelCase )
# We'll offload the last model manually.
A = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase ( self :str ):
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__UpperCamelCase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__UpperCamelCase )
def __call__( self :List[Any] , __UpperCamelCase :Union[torch.FloatTensor, List[torch.FloatTensor]] , __UpperCamelCase :Union[torch.FloatTensor, List[torch.FloatTensor]] , __UpperCamelCase :torch.FloatTensor , __UpperCamelCase :int = 5_12 , __UpperCamelCase :int = 5_12 , __UpperCamelCase :int = 1_00 , __UpperCamelCase :float = 4.0 , __UpperCamelCase :int = 1 , __UpperCamelCase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCamelCase :Optional[torch.FloatTensor] = None , __UpperCamelCase :Optional[str] = "pil" , __UpperCamelCase :bool = True , ):
A = self._execution_device
A = guidance_scale > 1.0
if isinstance(__UpperCamelCase , __UpperCamelCase ):
A = torch.cat(__UpperCamelCase , dim=0 )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
A = torch.cat(__UpperCamelCase , dim=0 )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
A = torch.cat(__UpperCamelCase , dim=0 )
A = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
A = image_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
A = negative_image_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
A = hint.repeat_interleave(__UpperCamelCase , dim=0 )
A = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCamelCase )
A = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCamelCase )
self.scheduler.set_timesteps(__UpperCamelCase , device=__UpperCamelCase )
A = self.scheduler.timesteps
A = self.movq.config.latent_channels
A, A = downscale_height_and_width(__UpperCamelCase , __UpperCamelCase , self.movq_scale_factor )
# create initial latent
A = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the latents if we are doing classifier free guidance
A = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A = {"image_embeds": image_embeds, "hint": hint}
A = self.unet(
sample=__UpperCamelCase , timestep=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , added_cond_kwargs=__UpperCamelCase , return_dict=__UpperCamelCase , )[0]
if do_classifier_free_guidance:
A, A = noise_pred.split(latents.shape[1] , dim=1 )
A, A = noise_pred.chunk(2 )
A, A = variance_pred.chunk(2 )
A = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
A = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
A, A = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
A = self.scheduler.step(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , generator=__UpperCamelCase , )[0]
# post-processing
A = self.movq.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
A = image * 0.5 + 0.5
A = image.clamp(0 , 1 )
A = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
A = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
| 292 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__UpperCamelCase : int = 10
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : list[int] , _UpperCAmelCase : int ):
for i in range(_UpperCAmelCase , _UpperCAmelCase ):
if array[i] == target:
return i
return -1
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : int ):
lowerCAmelCase = 0
lowerCAmelCase = len(_UpperCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = (left + right) // 3 + 1
lowerCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
lowerCAmelCase = one_third - 1
elif array[two_third] < target:
lowerCAmelCase = two_third + 1
else:
lowerCAmelCase = one_third + 1
lowerCAmelCase = two_third - 1
else:
return -1
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : list[int] , _UpperCAmelCase : int ):
if left < right:
if right - left < precision:
return lin_search(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = (left + right) // 3 + 1
lowerCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_UpperCAmelCase , one_third - 1 , _UpperCAmelCase , _UpperCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _UpperCAmelCase , _UpperCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Optional[Any] = input('''Enter numbers separated by comma:\n''').strip()
__UpperCamelCase : Any = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
__UpperCamelCase : List[Any] = int(input('''Enter the number to be found in the list:\n''').strip())
__UpperCamelCase : Any = ite_ternary_search(collection, target)
__UpperCamelCase : List[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print('''Not found''')
| 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_UpperCAmelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
lowerCAmelCase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creates a copy of the matrix with swapped positions of the elements
lowerCAmelCase = [[0.0, 0.0], [0.0, 0.0]]
lowerCAmelCase ,lowerCAmelCase = matrix[1][1], matrix[0][0]
lowerCAmelCase ,lowerCAmelCase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(_UpperCAmelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(_UpperCAmelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
lowerCAmelCase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creating cofactor matrix
lowerCAmelCase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
lowerCAmelCase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
lowerCAmelCase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
lowerCAmelCase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
lowerCAmelCase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
lowerCAmelCase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
lowerCAmelCase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
lowerCAmelCase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
lowerCAmelCase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
lowerCAmelCase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
lowerCAmelCase = array(_UpperCAmelCase )
for i in range(3 ):
for j in range(3 ):
lowerCAmelCase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
lowerCAmelCase = array(_UpperCAmelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(_UpperCAmelCase )
# Calculate the inverse of the matrix
return [[float(d(_UpperCAmelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
| 309 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def snake_case_ ( _lowerCAmelCase : np.ndarray ) -> np.ndarray:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def snake_case_ ( _lowerCAmelCase : np.ndarray ) -> np.ndarray:
return (gray > 127) & (gray <= 255)
def snake_case_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ) -> np.ndarray:
UpperCAmelCase : int = np.zeros_like(_lowerCAmelCase )
UpperCAmelCase : Dict = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
UpperCAmelCase : Dict = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
UpperCAmelCase : List[str] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
UpperCAmelCase : Any = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
UpperCamelCase__: int = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
UpperCamelCase__: Optional[Any] = np.array(Image.open(lena_path))
# kernel to be applied
UpperCamelCase__: Union[str, Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
UpperCamelCase__: Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
UpperCamelCase__: Union[str, Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 23 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool:
UpperCAmelCase : str = get_failure_array(_lowerCAmelCase )
# 2) Step through text searching for pattern
UpperCAmelCase , UpperCAmelCase : Optional[Any] = 0, 0 # index into text, pattern
while i < len(_lowerCAmelCase ):
if pattern[j] == text[i]:
if j == (len(_lowerCAmelCase ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
UpperCAmelCase : Optional[Any] = failure[j - 1]
continue
i += 1
return False
def snake_case_ ( _lowerCAmelCase : str ) -> list[int]:
UpperCAmelCase : Optional[Any] = [0]
UpperCAmelCase : str = 0
UpperCAmelCase : List[str] = 1
while j < len(_lowerCAmelCase ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
UpperCAmelCase : Union[str, Any] = failure[i - 1]
continue
j += 1
failure.append(_lowerCAmelCase )
return failure
if __name__ == "__main__":
# Test 1)
UpperCamelCase__: str = "abc1abc12"
UpperCamelCase__: str = "alskfjaldsabc1abc1abc12k23adsfabcabc"
UpperCamelCase__: Any = "alskfjaldsk23adsfabcabc"
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
UpperCamelCase__: Tuple = "ABABX"
UpperCamelCase__: Union[str, Any] = "ABABZABABYABABX"
assert kmp(pattern, text)
# Test 3)
UpperCamelCase__: Any = "AAAB"
UpperCamelCase__: str = "ABAAAAAB"
assert kmp(pattern, text)
# Test 4)
UpperCamelCase__: int = "abcdabcy"
UpperCamelCase__: Any = "abcxabcdabxabcdabcdabcy"
assert kmp(pattern, text)
# Test 5)
UpperCamelCase__: List[str] = "aabaabaaa"
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 23 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[Any] = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
'''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
'''tokenization_funnel''': ['''FunnelTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = ['''FunnelTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple = [
'''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FunnelBaseModel''',
'''FunnelForMaskedLM''',
'''FunnelForMultipleChoice''',
'''FunnelForPreTraining''',
'''FunnelForQuestionAnswering''',
'''FunnelForSequenceClassification''',
'''FunnelForTokenClassification''',
'''FunnelModel''',
'''FunnelPreTrainedModel''',
'''load_tf_weights_in_funnel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : int = [
'''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFFunnelBaseModel''',
'''TFFunnelForMaskedLM''',
'''TFFunnelForMultipleChoice''',
'''TFFunnelForPreTraining''',
'''TFFunnelForQuestionAnswering''',
'''TFFunnelForSequenceClassification''',
'''TFFunnelForTokenClassification''',
'''TFFunnelModel''',
'''TFFunnelPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 359 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_lowerCAmelCase : str = logging.get_logger(__name__)
@dataclass
class __magic_name__ :
"""simple docstring"""
def __init__( self :Dict , snake_case :List[str]=False , snake_case :Optional[Any]=False , snake_case :Union[str, Any]=6.0 , snake_case :Union[str, Any]=None , snake_case :Union[str, Any]=False , snake_case :str=False , snake_case :Optional[Any]=None , snake_case :int="fp4" , snake_case :int=False , **snake_case :Optional[Any] , ):
'''simple docstring'''
A_ : int = load_in_abit
A_ : Union[str, Any] = load_in_abit
A_ : str = llm_inta_threshold
A_ : str = llm_inta_skip_modules
A_ : List[Any] = llm_inta_enable_fpaa_cpu_offload
A_ : Optional[int] = llm_inta_has_fpaa_weight
A_ : Optional[int] = bnb_abit_quant_type
A_ : Dict = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
A_ : List[Any] = torch.floataa
elif isinstance(snake_case , snake_case ):
A_ : Any = getattr(snake_case , snake_case )
elif isinstance(snake_case , torch.dtype ):
A_ : Union[str, Any] = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
if not isinstance(self.llm_inta_threshold , snake_case ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , snake_case ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , snake_case ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , snake_case ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , snake_case ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , snake_case ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
return self.load_in_abit or self.load_in_abit
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def SCREAMING_SNAKE_CASE ( cls :List[str] , snake_case :Dict , snake_case :str , **snake_case :Dict ):
'''simple docstring'''
A_ : str = cls(**snake_case )
A_ : Any = []
for key, value in kwargs.items():
if hasattr(snake_case , snake_case ):
setattr(snake_case , snake_case , snake_case )
to_remove.append(snake_case )
for key in to_remove:
kwargs.pop(snake_case , snake_case )
if return_unused_kwargs:
return config, kwargs
else:
return config
def SCREAMING_SNAKE_CASE ( self :Tuple , snake_case :Union[str, os.PathLike] ):
'''simple docstring'''
with open(snake_case , "w" , encoding="utf-8" ) as writer:
A_ : List[Any] = self.to_dict()
A_ : int = json.dumps(snake_case , indent=2 , sort_keys=snake_case ) + "\n"
writer.write(snake_case )
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : List[str] = copy.deepcopy(self.__dict__ )
A_ : Optional[int] = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self :List[str] ):
'''simple docstring'''
return f"{self.__class__.__name__} {self.to_json_string()}"
def SCREAMING_SNAKE_CASE ( self :List[str] , snake_case :bool = True ):
'''simple docstring'''
if use_diff is True:
A_ : List[str] = self.to_diff_dict()
else:
A_ : int = self.to_dict()
return json.dumps(snake_case , indent=2 , sort_keys=snake_case ) + "\n"
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
A_ : List[Any] = self.to_dict()
# get the default config dict
A_ : Optional[Any] = BitsAndBytesConfig().to_dict()
A_ : List[Any] = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
A_ : int = value
return serializable_config_dict
| 70 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __a :
_a : Any = XGLMConfig
_a : Optional[int] = {}
_a : List[str] = 'gelu'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=14 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=0.02 , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = d_model
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = ffn_dim
_UpperCAmelCase = activation_function
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = initializer_range
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = 2
_UpperCAmelCase = 1
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=_SCREAMING_SNAKE_CASE , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=_SCREAMING_SNAKE_CASE , )
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class __a ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : int = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
_a : int = (TFXGLMForCausalLM,) if is_tf_available() else ()
_a : int = (
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
_a : Dict = False
_a : int = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = TFXGLMModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , n_embd=37 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = TFXGLMModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __a ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE=True ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
_UpperCAmelCase = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_UpperCAmelCase = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
_UpperCAmelCase = model.generate(_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , _SCREAMING_SNAKE_CASE )
@slow
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
_UpperCAmelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
_UpperCAmelCase = tokenizer('Today is a nice day and' , return_tensors='tf' )
_UpperCAmelCase = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
_UpperCAmelCase = model.generate(_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , seed=[7, 0] )
_UpperCAmelCase = tokenizer.decode(output_ids[0] , skip_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
_UpperCAmelCase = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
_UpperCAmelCase = 'left'
# use different length sentences to test batching
_UpperCAmelCase = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors='tf' , padding=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = inputs['input_ids']
_UpperCAmelCase = model.generate(input_ids=_SCREAMING_SNAKE_CASE , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
_UpperCAmelCase = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
_UpperCAmelCase = model.generate(input_ids=_SCREAMING_SNAKE_CASE , max_new_tokens=12 )
_UpperCAmelCase = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
_UpperCAmelCase = model.generate(input_ids=_SCREAMING_SNAKE_CASE , max_new_tokens=12 )
_UpperCAmelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , [non_padded_sentence, padded_sentence] )
| 329 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
lowerCAmelCase__ :Optional[Any] = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class __a ( UpperCAmelCase ):
_a : str = 'data2vec-text'
def __init__( self , _SCREAMING_SNAKE_CASE=30522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1e-1_2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class __a ( UpperCAmelCase ):
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 329 | 1 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_enforce_args(_a, _a )
if n == 0:
return 0
_SCREAMING_SNAKE_CASE : int = float("-inf" )
for i in range(1, n + 1 ):
_SCREAMING_SNAKE_CASE : str = max(
_a, prices[i - 1] + naive_cut_rod_recursive(n - i, _a ) )
return max_revue
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_enforce_args(_a, _a )
_SCREAMING_SNAKE_CASE : Tuple = [float("-inf" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(_a, _a, _a )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
_SCREAMING_SNAKE_CASE : Dict = float("-inf" )
for i in range(1, n + 1 ):
_SCREAMING_SNAKE_CASE : Optional[int] = max(
_a, prices[i - 1] + _top_down_cut_rod_recursive(n - i, _a, _a ), )
_SCREAMING_SNAKE_CASE : str = max_revenue
return max_rev[n]
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_enforce_args(_a, _a )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
_SCREAMING_SNAKE_CASE : Tuple = [float("-inf" ) for _ in range(n + 1 )]
_SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1, n + 1 ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = max_rev[i]
for j in range(1, i + 1 ):
_SCREAMING_SNAKE_CASE : Optional[int] = max(_a, prices[j - 1] + max_rev[i - j] )
_SCREAMING_SNAKE_CASE : str = max_revenue_i
return max_rev[n]
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
if n < 0:
_SCREAMING_SNAKE_CASE : int = f"""n must be greater than or equal to 0. Got n = {n}"""
raise ValueError(_a )
if n > len(_a ):
_SCREAMING_SNAKE_CASE : List[str] = (
"""Each integral piece of rod must have a corresponding price. """
f"""Got n = {n} but length of prices = {len(_a )}"""
)
raise ValueError(_a )
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : Dict = [6, 10, 12, 15, 20, 23]
_SCREAMING_SNAKE_CASE : str = len(_a )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
_SCREAMING_SNAKE_CASE : Any = 36
_SCREAMING_SNAKE_CASE : Optional[Any] = top_down_cut_rod(_a, _a )
_SCREAMING_SNAKE_CASE : str = bottom_up_cut_rod(_a, _a )
_SCREAMING_SNAKE_CASE : Dict = naive_cut_rod_recursive(_a, _a )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main() | 365 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = ['image_processor', 'tokenizer']
__snake_case = 'BlipImageProcessor'
__snake_case = 'AutoTokenizer'
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__(__lowerCamelCase , __lowerCamelCase )
# add QFormer tokenizer
_SCREAMING_SNAKE_CASE : List[str] = qformer_tokenizer
def __call__( self , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase = True , __lowerCamelCase = False , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase = 0 , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase = False , __lowerCamelCase = False , __lowerCamelCase = False , __lowerCamelCase = False , __lowerCamelCase = False , __lowerCamelCase = True , __lowerCamelCase = None , **__lowerCamelCase , ) -> BatchFeature:
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
_SCREAMING_SNAKE_CASE : Any = BatchFeature()
if text is not None:
_SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
encoding.update(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = self.qformer_tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
_SCREAMING_SNAKE_CASE : str = qformer_text_encoding.pop("input_ids" )
_SCREAMING_SNAKE_CASE : List[Any] = qformer_text_encoding.pop("attention_mask" )
if images is not None:
_SCREAMING_SNAKE_CASE : Optional[int] = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase )
encoding.update(__lowerCamelCase )
return encoding
def UpperCamelCase_ ( self , *__lowerCamelCase , **__lowerCamelCase ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase_ ( self , *__lowerCamelCase , **__lowerCamelCase ) -> str:
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def UpperCamelCase_ ( self ) -> str:
_SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def UpperCamelCase_ ( self , __lowerCamelCase , **__lowerCamelCase ) -> Any:
if os.path.isfile(__lowerCamelCase ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = os.path.join(__lowerCamelCase , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(__lowerCamelCase )
return super().save_pretrained(__lowerCamelCase , **__lowerCamelCase )
@classmethod
def UpperCamelCase_ ( cls , __lowerCamelCase , **__lowerCamelCase ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained(__lowerCamelCase , subfolder="qformer_tokenizer" )
_SCREAMING_SNAKE_CASE : Optional[Any] = cls._get_arguments_from_pretrained(__lowerCamelCase , **__lowerCamelCase )
args.append(__lowerCamelCase )
return cls(*__lowerCamelCase ) | 325 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCamelCase ( snake_case_ ):
'''simple docstring'''
def __init__( self : Any , a_ : List[Any] , a_ : Union[str, Any] , a_ : int , a_ : Union[str, Any] , a_ : List[Any] , a_ : Tuple , a_ : int , a_ : int , a_ : Tuple , ):
super().__init__()
if safety_checker is None:
logger.warning(
f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
speech_model=a_ , speech_processor=a_ , vae=a_ , text_encoder=a_ , tokenizer=a_ , unet=a_ , scheduler=a_ , feature_extractor=a_ , )
def lowerCamelCase ( self : Union[str, Any] , a_ : Union[str, Any] = "auto" ):
if slice_size == "auto":
lowerCAmelCase_ : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(a_ )
def lowerCamelCase ( self : int ):
self.enable_attention_slicing(a_ )
@torch.no_grad()
def __call__( self : str , a_ : Union[str, Any] , a_ : Optional[int]=1_60_00 , a_ : Tuple = 5_12 , a_ : int = 5_12 , a_ : Optional[Any] = 50 , a_ : int = 7.5 , a_ : Tuple = None , a_ : Any = 1 , a_ : Optional[int] = 0.0 , a_ : Any = None , a_ : int = None , a_ : str = "pil" , a_ : Union[str, Any] = True , a_ : Tuple = None , a_ : List[Any] = 1 , **a_ : Optional[int] , ):
lowerCAmelCase_ : List[Any] = self.speech_processor.feature_extractor(
a_ , return_tensors="pt" , sampling_rate=a_ ).input_features.to(self.device )
lowerCAmelCase_ : Optional[Any] = self.speech_model.generate(a_ , max_length=48_00_00 )
lowerCAmelCase_ : Any = self.speech_processor.tokenizer.batch_decode(a_ , skip_special_tokens=a_ , normalize=a_ )[
0
]
if isinstance(a_ , a_ ):
lowerCAmelCase_ : List[str] = 1
elif isinstance(a_ , a_ ):
lowerCAmelCase_ : List[str] = len(a_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(a_ , a_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(a_ )}.''' )
# get prompt text embeddings
lowerCAmelCase_ : List[str] = self.tokenizer(
a_ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
lowerCAmelCase_ : Tuple = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCAmelCase_ : List[Any] = text_input_ids[:, : self.tokenizer.model_max_length]
lowerCAmelCase_ : Optional[Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = text_embeddings.shape
lowerCAmelCase_ : Tuple = text_embeddings.repeat(1 , a_ , 1 )
lowerCAmelCase_ : Optional[Any] = text_embeddings.view(bs_embed * num_images_per_prompt , a_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCAmelCase_ : Optional[int] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase_ : int = 42
if negative_prompt is None:
lowerCAmelCase_ : Optional[int] = [""] * batch_size
elif type(a_ ) is not type(a_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(a_ )} !='''
f''' {type(a_ )}.''' )
elif isinstance(a_ , a_ ):
lowerCAmelCase_ : Union[str, Any] = [negative_prompt]
elif batch_size != len(a_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(a_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
lowerCAmelCase_ : str = negative_prompt
lowerCAmelCase_ : Optional[Any] = text_input_ids.shape[-1]
lowerCAmelCase_ : Dict = self.tokenizer(
a_ , padding="max_length" , max_length=a_ , truncation=a_ , return_tensors="pt" , )
lowerCAmelCase_ : Dict = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCAmelCase_ : List[str] = uncond_embeddings.shape[1]
lowerCAmelCase_ : Tuple = uncond_embeddings.repeat(1 , a_ , 1 )
lowerCAmelCase_ : Optional[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , a_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCAmelCase_ : str = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCAmelCase_ : List[str] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCAmelCase_ : List[str] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCAmelCase_ : Dict = torch.randn(a_ , generator=a_ , device="cpu" , dtype=a_ ).to(
self.device )
else:
lowerCAmelCase_ : Optional[int] = torch.randn(a_ , generator=a_ , device=self.device , dtype=a_ )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCAmelCase_ : Tuple = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(a_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCAmelCase_ : Optional[Any] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCAmelCase_ : Dict = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCAmelCase_ : Any = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCAmelCase_ : Optional[Any] = {}
if accepts_eta:
lowerCAmelCase_ : Optional[int] = eta
for i, t in enumerate(self.progress_bar(a_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase_ : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase_ : Union[str, Any] = self.scheduler.scale_model_input(a_ , a_ )
# predict the noise residual
lowerCAmelCase_ : List[Any] = self.unet(a_ , a_ , encoder_hidden_states=a_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCAmelCase_ , lowerCAmelCase_ : Any = noise_pred.chunk(2 )
lowerCAmelCase_ : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCAmelCase_ : List[str] = self.scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(a_ , a_ , a_ )
lowerCAmelCase_ : int = 1 / 0.18215 * latents
lowerCAmelCase_ : Tuple = self.vae.decode(a_ ).sample
lowerCAmelCase_ : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCAmelCase_ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCAmelCase_ : Optional[int] = self.numpy_to_pil(a_ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=a_ , nsfw_content_detected=a_ )
| 241 |
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 (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
def __UpperCAmelCase ( a_ , a_=False):
snake_case_ = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('head'):
snake_case_ = 'segformer.encoder.' + key
if key.startswith('backbone'):
snake_case_ = key.replace('backbone' , 'segformer.encoder')
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
snake_case_ = key[key.find('patch_embed') + len('patch_embed')]
snake_case_ = key.replace(f'''patch_embed{idx}''' , f'''patch_embeddings.{int(a_)-1}''')
if "norm" in key:
snake_case_ = key.replace('norm' , 'layer_norm')
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
snake_case_ = key[key.find('segformer.encoder.layer_norm') + len('segformer.encoder.layer_norm')]
snake_case_ = key.replace(f'''layer_norm{idx}''' , f'''layer_norm.{int(a_)-1}''')
if "layer_norm1" in key:
snake_case_ = key.replace('layer_norm1' , 'layer_norm_1')
if "layer_norm2" in key:
snake_case_ = key.replace('layer_norm2' , 'layer_norm_2')
if "block" in key:
# replace for example block1 by block.0
snake_case_ = key[key.find('block') + len('block')]
snake_case_ = key.replace(f'''block{idx}''' , f'''block.{int(a_)-1}''')
if "attn.q" in key:
snake_case_ = key.replace('attn.q' , 'attention.self.query')
if "attn.proj" in key:
snake_case_ = key.replace('attn.proj' , 'attention.output.dense')
if "attn" in key:
snake_case_ = key.replace('attn' , 'attention.self')
if "fc1" in key:
snake_case_ = key.replace('fc1' , 'dense1')
if "fc2" in key:
snake_case_ = key.replace('fc2' , 'dense2')
if "linear_pred" in key:
snake_case_ = key.replace('linear_pred' , 'classifier')
if "linear_fuse" in key:
snake_case_ = key.replace('linear_fuse.conv' , 'linear_fuse')
snake_case_ = key.replace('linear_fuse.bn' , 'batch_norm')
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
snake_case_ = key[key.find('linear_c') + len('linear_c')]
snake_case_ = key.replace(f'''linear_c{idx}''' , f'''linear_c.{int(a_)-1}''')
if key.startswith('head'):
snake_case_ = key.replace('head' , 'classifier')
snake_case_ = value
return new_state_dict
def __UpperCAmelCase ( a_ , a_):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks):
for j in range(config.depths[i]):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
snake_case_ = state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight''')
snake_case_ = state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias''')
# next, add keys and values (in that order) to the state dict
snake_case_ = kv_weight[
: config.hidden_sizes[i], :
]
snake_case_ = kv_bias[: config.hidden_sizes[i]]
snake_case_ = kv_weight[
config.hidden_sizes[i] :, :
]
snake_case_ = kv_bias[
config.hidden_sizes[i] :
]
def __UpperCAmelCase ( ):
snake_case_ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case_ = Image.open(requests.get(a_ , stream=a_).raw)
return image
@torch.no_grad()
def __UpperCAmelCase ( a_ , a_ , a_):
snake_case_ = SegformerConfig()
snake_case_ = False
# set attributes based on model_name
snake_case_ = 'huggingface/label-files'
if "segformer" in model_name:
snake_case_ = model_name[len('segformer.') : len('segformer.') + 2]
if "ade" in model_name:
snake_case_ = 1_50
snake_case_ = 'ade20k-id2label.json'
snake_case_ = (1, 1_50, 1_28, 1_28)
elif "city" in model_name:
snake_case_ = 19
snake_case_ = 'cityscapes-id2label.json'
snake_case_ = (1, 19, 1_28, 1_28)
else:
raise ValueError(f'''Model {model_name} not supported''')
elif "mit" in model_name:
snake_case_ = True
snake_case_ = model_name[4:6]
snake_case_ = 10_00
snake_case_ = 'imagenet-1k-id2label.json'
snake_case_ = (1, 10_00)
else:
raise ValueError(f'''Model {model_name} not supported''')
# set config attributes
snake_case_ = json.load(open(hf_hub_download(a_ , a_ , repo_type='dataset') , 'r'))
snake_case_ = {int(a_): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
snake_case_ = [64, 1_28, 3_20, 5_12]
snake_case_ = 2_56
elif size == "b2":
snake_case_ = [64, 1_28, 3_20, 5_12]
snake_case_ = 7_68
snake_case_ = [3, 4, 6, 3]
elif size == "b3":
snake_case_ = [64, 1_28, 3_20, 5_12]
snake_case_ = 7_68
snake_case_ = [3, 4, 18, 3]
elif size == "b4":
snake_case_ = [64, 1_28, 3_20, 5_12]
snake_case_ = 7_68
snake_case_ = [3, 8, 27, 3]
elif size == "b5":
snake_case_ = [64, 1_28, 3_20, 5_12]
snake_case_ = 7_68
snake_case_ = [3, 6, 40, 3]
else:
raise ValueError(f'''Size {size} not supported''')
# load image processor (only resize + normalize)
snake_case_ = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=a_ , align=a_ , do_random_crop=a_)
# prepare image
snake_case_ = prepare_img()
snake_case_ = image_processor(images=a_ , return_tensors='pt').pixel_values
logger.info(f'''Converting model {model_name}...''')
# load original state dict
if encoder_only:
snake_case_ = torch.load(a_ , map_location=torch.device('cpu'))
else:
snake_case_ = torch.load(a_ , map_location=torch.device('cpu'))['state_dict']
# rename keys
snake_case_ = rename_keys(a_ , encoder_only=a_)
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(a_ , a_)
# create HuggingFace model and load state dict
if encoder_only:
snake_case_ = False
snake_case_ = SegformerForImageClassification(a_)
else:
snake_case_ = SegformerForSemanticSegmentation(a_)
model.load_state_dict(a_)
model.eval()
# forward pass
snake_case_ = model(a_)
snake_case_ = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
snake_case_ = torch.tensor(
[
[[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
[[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
[[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
])
elif model_name == "segformer.b1.512x512.ade.160k":
snake_case_ = torch.tensor(
[
[[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
[[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
[[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
])
elif model_name == "segformer.b2.512x512.ade.160k":
snake_case_ = torch.tensor(
[
[[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
[[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
[[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
])
elif model_name == "segformer.b3.512x512.ade.160k":
snake_case_ = torch.tensor(
[
[[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
[[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
[[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
])
elif model_name == "segformer.b4.512x512.ade.160k":
snake_case_ = torch.tensor(
[
[[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
[[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
[[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
])
elif model_name == "segformer.b5.640x640.ade.160k":
snake_case_ = torch.tensor(
[
[[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
[[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
[[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
])
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
[[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
[[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
])
elif model_name == "segformer.b0.512x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
[[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
[[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
])
elif model_name == "segformer.b0.640x1280.city.160k":
snake_case_ = torch.tensor(
[
[
[-1.1_372E01, -1.2_787E01, -1.3_477E01],
[-1.2_536E01, -1.4_194E01, -1.4_409E01],
[-1.3_217E01, -1.4_888E01, -1.5_327E01],
],
[
[-1.4_791E01, -1.7_122E01, -1.8_277E01],
[-1.7_163E01, -1.9_192E01, -1.9_533E01],
[-1.7_897E01, -1.9_991E01, -2.0_315E01],
],
[
[7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
[4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
[3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
],
])
elif model_name == "segformer.b0.768x768.city.160k":
snake_case_ = torch.tensor(
[
[[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
[[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
[[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
])
elif model_name == "segformer.b1.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
[[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
[[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
])
elif model_name == "segformer.b2.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
[[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
[[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
])
elif model_name == "segformer.b3.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
[[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
[[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
])
elif model_name == "segformer.b4.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
[[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
[[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
])
elif model_name == "segformer.b5.1024x1024.city.160k":
snake_case_ = torch.tensor(
[
[[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
[[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
[[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
])
else:
snake_case_ = logits.argmax(-1).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx])
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , a_ , atol=1E-2)
# finally, save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
Path(a_).mkdir(exist_ok=a_)
model.save_pretrained(a_)
image_processor.save_pretrained(a_)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="segformer.b0.512x512.ade.160k",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
lowercase = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 178 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = AutoConfig.from_pretrained(lowercase_ )
A__ = FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_ )
A__ = checkpoints.load_tax_checkpoint(lowercase_ )
A__ = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
A__ = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
A__ = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
A__ = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
A__ = f"""layers_{str(lowercase_ )}"""
# Self-Attention
A__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
A__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
A__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
A__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
A__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
A__ = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
A__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
A__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
A__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
A__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
A__ = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
A__ = flax_model.params['''encoder''']['''block'''][str(lowercase_ )]['''layer''']
A__ = tax_attention_key
A__ = tax_attention_out
A__ = tax_attention_query
A__ = tax_attention_value
A__ = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
A__ = tax_global_layer_norm
if split_mlp_wi:
A__ = tax_mlp_wi_a
A__ = tax_mlp_wi_a
else:
A__ = tax_mlp_wi
A__ = tax_mlp_wo
A__ = tax_mlp_layer_norm
A__ = flax_model_encoder_layer_block
# Only for layer 0:
A__ = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
A__ = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
A__ = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
A__ = tax_encoder_global_rel_embedding
# Assigning
A__ = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
A__ = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
A__ = f"""layers_{str(lowercase_ )}"""
# Self-Attention
A__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
A__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
A__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
A__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
A__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
A__ = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
A__ = tax_enc_dec_attention_module['''key''']['''kernel''']
A__ = tax_enc_dec_attention_module['''out''']['''kernel''']
A__ = tax_enc_dec_attention_module['''query''']['''kernel''']
A__ = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
A__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
A__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
A__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
A__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
A__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
A__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
A__ = flax_model.params['''decoder''']['''block'''][str(lowercase_ )]['''layer''']
A__ = tax_attention_key
A__ = tax_attention_out
A__ = tax_attention_query
A__ = tax_attention_value
A__ = tax_pre_attention_layer_norm
A__ = tax_enc_dec_attention_key
A__ = tax_enc_dec_attention_out
A__ = tax_enc_dec_attention_query
A__ = tax_enc_dec_attention_value
A__ = tax_cross_layer_norm
if split_mlp_wi:
A__ = tax_mlp_wi_a
A__ = tax_mlp_wi_a
else:
A__ = tax_mlp_wi
A__ = tax_mlp_wo
A__ = txa_mlp_layer_norm
A__ = flax_model_decoder_layer_block
# Decoder Normalization
A__ = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
A__ = txa_decoder_norm
# Only for layer 0:
A__ = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
A__ = tax_decoder_rel_embedding
# Token Embeddings
A__ = tax_model['''target''']['''token_embedder''']['''embedding''']
A__ = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
A__ = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(lowercase_ )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
)
parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
parser.add_argument(
"""--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
)
_lowerCamelCase : Tuple = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 231 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Any=14 , UpperCAmelCase__ : Any=7 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Tuple=False , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Tuple=99 , UpperCAmelCase__ : Optional[Any]=32 , UpperCAmelCase__ : Tuple=4 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : int=4 , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : Optional[Any]=512 , UpperCAmelCase__ : Union[str, Any]=0.02 , ) ->Any:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = rotary_dim
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = initializer_range
A__ = None
A__ = vocab_size - 1
A__ = vocab_size - 1
A__ = vocab_size - 1
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length])
A__ = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCAmelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[int]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any]) ->Optional[int]:
'''simple docstring'''
A__ = 20
A__ = model_class_name(UpperCAmelCase__)
A__ = model.init_cache(input_ids.shape[0] , UpperCAmelCase__)
A__ = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''')
A__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1))
A__ = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase__ , past_key_values=UpperCAmelCase__ , position_ids=UpperCAmelCase__ , )
A__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''')
A__ = model(
input_ids[:, -1:] , attention_mask=UpperCAmelCase__ , past_key_values=outputs_cache.past_key_values , position_ids=UpperCAmelCase__ , )
A__ = model(UpperCAmelCase__)
A__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""")
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int) ->Any:
'''simple docstring'''
A__ = 20
A__ = model_class_name(UpperCAmelCase__)
A__ = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))] , axis=-1 , )
A__ = model.init_cache(input_ids.shape[0] , UpperCAmelCase__)
A__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1))
A__ = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase__ , past_key_values=UpperCAmelCase__ , position_ids=UpperCAmelCase__ , )
A__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''')
A__ = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCAmelCase__ , position_ids=UpperCAmelCase__ , )
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__)
A__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""")
@require_flax
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
UpperCAmelCase__ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->List[Any]:
'''simple docstring'''
A__ = FlaxGPTJModelTester(self)
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Any:
'''simple docstring'''
for model_class_name in self.all_model_classes:
A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Any:
'''simple docstring'''
for model_class_name in self.all_model_classes:
A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
@tooslow
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Dict:
'''simple docstring'''
A__ = GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''')
A__ = tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__)
A__ = FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
A__ = False
A__ = model.config.eos_token_id
A__ = jax.jit(model.generate)
A__ = jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id).sequences
A__ = tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__)
A__ = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__)
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->int:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
A__ = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__)
A__ = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
A__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
A__ = getattr(UpperCAmelCase__ , UpperCAmelCase__)
A__ , A__ = pt_inputs['''input_ids'''].shape
A__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,))
for batch_idx, start_index in enumerate(UpperCAmelCase__):
A__ = 0
A__ = 1
A__ = 0
A__ = 1
A__ = pt_model_class(UpperCAmelCase__).eval()
A__ = model_class(UpperCAmelCase__ , dtype=jnp.floataa)
A__ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase__)
A__ = fx_state
with torch.no_grad():
A__ = pt_model(**UpperCAmelCase__).to_tuple()
A__ = fx_model(**UpperCAmelCase__).to_tuple()
self.assertEqual(len(UpperCAmelCase__) , len(UpperCAmelCase__) , '''Output lengths differ between Flax and PyTorch''')
for fx_output, pt_output in zip(UpperCAmelCase__ , UpperCAmelCase__):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase__)
A__ = model_class.from_pretrained(UpperCAmelCase__ , from_pt=UpperCAmelCase__)
A__ = fx_model_loaded(**UpperCAmelCase__).to_tuple()
self.assertEqual(
len(UpperCAmelCase__) , len(UpperCAmelCase__) , '''Output lengths differ between Flax and PyTorch''')
for fx_output_loaded, pt_output in zip(UpperCAmelCase__ , UpperCAmelCase__):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2)
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[int]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
A__ = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__)
A__ = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
A__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
A__ = getattr(UpperCAmelCase__ , UpperCAmelCase__)
A__ = pt_model_class(UpperCAmelCase__).eval()
A__ = model_class(UpperCAmelCase__ , dtype=jnp.floataa)
A__ = load_flax_weights_in_pytorch_model(UpperCAmelCase__ , fx_model.params)
A__ , A__ = pt_inputs['''input_ids'''].shape
A__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,))
for batch_idx, start_index in enumerate(UpperCAmelCase__):
A__ = 0
A__ = 1
A__ = 0
A__ = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
A__ = pt_model(**UpperCAmelCase__).to_tuple()
A__ = fx_model(**UpperCAmelCase__).to_tuple()
self.assertEqual(len(UpperCAmelCase__) , len(UpperCAmelCase__) , '''Output lengths differ between Flax and PyTorch''')
for fx_output, pt_output in zip(UpperCAmelCase__ , UpperCAmelCase__):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase__)
A__ = pt_model_class.from_pretrained(UpperCAmelCase__ , from_flax=UpperCAmelCase__)
with torch.no_grad():
A__ = pt_model_loaded(**UpperCAmelCase__).to_tuple()
self.assertEqual(
len(UpperCAmelCase__) , len(UpperCAmelCase__) , '''Output lengths differ between Flax and PyTorch''')
for fx_output, pt_output in zip(UpperCAmelCase__ , UpperCAmelCase__):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2)
@tooslow
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Optional[Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
A__ = model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
A__ = model(np.ones((1, 1)))
self.assertIsNotNone(UpperCAmelCase__)
| 231 | 1 |
from __future__ import annotations
from random import random
class __magic_name__ :
def __init__( self : List[str] , lowerCamelCase__ : int | None = None ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = value
UpperCamelCase__ : Union[str, Any] = random()
UpperCamelCase__ : List[str] = None
UpperCamelCase__ : Optional[Any] = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return F"\'{self.value}: {self.prior:.5}\'"
else:
return pformat(
{F"{self.value}: {self.prior:.5}": (self.left, self.right)} , indent=1 )
def __str__( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : int = str(self.value ) + ''' '''
UpperCamelCase__ : List[Any] = str(self.left or '''''' )
UpperCamelCase__ : Tuple = str(self.right or '''''' )
return value + left + right
def _a ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = split(root.left , lowercase__ )
return left, root
else:
UpperCamelCase__ , UpperCamelCase__ : str = split(root.right , lowercase__ )
return root, right
def _a ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : Node | None ):
"""simple docstring"""
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
UpperCamelCase__ : List[str] = merge(left.right , lowercase__ )
return left
else:
UpperCamelCase__ : Tuple = merge(lowercase__ , right.left )
return right
def _a ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : Any = Node(lowercase__ )
UpperCamelCase__ , UpperCamelCase__ : Tuple = split(lowercase__ , lowercase__ )
return merge(merge(lowercase__ , lowercase__ ) , lowercase__ )
def _a ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = split(lowercase__ , value - 1 )
UpperCamelCase__ , UpperCamelCase__ : Dict = split(lowercase__ , lowercase__ )
return merge(lowercase__ , lowercase__ )
def _a ( SCREAMING_SNAKE_CASE : Node | None ):
"""simple docstring"""
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def _a ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
for arg in args.split():
if arg[0] == "+":
UpperCamelCase__ : List[str] = insert(lowercase__ , int(arg[1:] ) )
elif arg[0] == "-":
UpperCamelCase__ : Union[str, Any] = erase(lowercase__ , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def _a ( ):
"""simple docstring"""
UpperCamelCase__ : List[str] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
UpperCamelCase__ : List[str] = input()
while args != "q":
UpperCamelCase__ : str = interact_treap(lowercase__ , lowercase__ )
print(lowercase__ )
UpperCamelCase__ : Optional[Any] = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 146 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCAmelCase ( A , unittest.TestCase ):
lowerCAmelCase_ = KandinskyVaaImgaImgPipeline
lowerCAmelCase_ = ["image_embeds", "negative_image_embeds", "image"]
lowerCAmelCase_ = [
"image_embeds",
"negative_image_embeds",
"image",
]
lowerCAmelCase_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCAmelCase_ = False
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return 32
@property
def snake_case ( self : Any ):
"""simple docstring"""
return 32
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return self.time_input_dim
@property
def snake_case ( self : str ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def snake_case ( self : Union[str, Any] ):
"""simple docstring"""
return 100
@property
def snake_case ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowercase ={
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__lowercase =UNetaDConditionModel(**__lowercase )
return model
@property
def snake_case ( self : Any ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def snake_case ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowercase =VQModel(**self.dummy_movq_kwargs )
return model
def snake_case ( self : Tuple ):
"""simple docstring"""
__lowercase =self.dummy_unet
__lowercase =self.dummy_movq
__lowercase ={
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.0_0_0_8_5,
'beta_end': 0.0_1_2,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
__lowercase =DDIMScheduler(**__lowercase )
__lowercase ={
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def snake_case ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : int=0 ):
"""simple docstring"""
__lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
__lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
__lowercase =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
__lowercase =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase =Image.fromarray(np.uinta(__lowercase ) ).convert('RGB' ).resize((256, 256) )
if str(__lowercase ).startswith('mps' ):
__lowercase =torch.manual_seed(__lowercase )
else:
__lowercase =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__lowercase ={
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def snake_case ( self : List[str] ):
"""simple docstring"""
__lowercase ='cpu'
__lowercase =self.get_dummy_components()
__lowercase =self.pipeline_class(**__lowercase )
__lowercase =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowercase =pipe(**self.get_dummy_inputs(__lowercase ) )
__lowercase =output.images
__lowercase =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
__lowercase =image[0, -3:, -3:, -1]
__lowercase =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowercase =np.array(
[0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Any ):
"""simple docstring"""
__lowercase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_img2img_frog.npy' )
__lowercase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__lowercase ='A red cartoon frog, 4k'
__lowercase =KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
__lowercase =KandinskyVaaImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder' , torch_dtype=torch.floataa )
__lowercase =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
__lowercase =torch.Generator(device='cpu' ).manual_seed(0 )
__lowercase , __lowercase =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowercase =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
__lowercase =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 141 | 0 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: str ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4)) | 358 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 64 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("0.12.2"):
raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
raise Exception("requires fairseq < v2")
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = "Hello, World!"
SCREAMING_SNAKE_CASE__ = "en_XX"
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : bool ):
'''simple docstring'''
lowerCAmelCase = Path("""data_bin""" )
lowerCAmelCase = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(SCREAMING_SNAKE_CASE ).parent ) , checkpoint_file=Path(SCREAMING_SNAKE_CASE ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(SCREAMING_SNAKE_CASE ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(SCREAMING_SNAKE_CASE )
lowerCAmelCase = xmod.model.encoder.sentence_encoder
lowerCAmelCase = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
lowerCAmelCase = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , SCREAMING_SNAKE_CASE )
lowerCAmelCase = XmodForSequenceClassification(SCREAMING_SNAKE_CASE ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowerCAmelCase = xmod_sent_encoder.embed_tokens.weight
lowerCAmelCase = xmod_sent_encoder.embed_positions.weight
lowerCAmelCase = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
lowerCAmelCase = xmod_sent_encoder.layernorm_embedding.weight
lowerCAmelCase = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowerCAmelCase = model.roberta.encoder.layer[i]
lowerCAmelCase = xmod_sent_encoder.layers[i]
# self attention
lowerCAmelCase = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
lowerCAmelCase = xmod_layer.self_attn.q_proj.weight
lowerCAmelCase = xmod_layer.self_attn.q_proj.bias
lowerCAmelCase = xmod_layer.self_attn.k_proj.weight
lowerCAmelCase = xmod_layer.self_attn.k_proj.bias
lowerCAmelCase = xmod_layer.self_attn.v_proj.weight
lowerCAmelCase = xmod_layer.self_attn.v_proj.bias
# self-attention output
lowerCAmelCase = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
lowerCAmelCase = xmod_layer.self_attn.out_proj.weight
lowerCAmelCase = xmod_layer.self_attn.out_proj.bias
lowerCAmelCase = xmod_layer.self_attn_layer_norm.weight
lowerCAmelCase = xmod_layer.self_attn_layer_norm.bias
# intermediate
lowerCAmelCase = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
lowerCAmelCase = xmod_layer.fca.weight
lowerCAmelCase = xmod_layer.fca.bias
# output
lowerCAmelCase = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
lowerCAmelCase = xmod_layer.fca.weight
lowerCAmelCase = xmod_layer.fca.bias
lowerCAmelCase = xmod_layer.final_layer_norm.weight
lowerCAmelCase = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
lowerCAmelCase = xmod_layer.adapter_layer_norm.weight
lowerCAmelCase = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
lowerCAmelCase = bert_output.adapter_modules[lang_code]
lowerCAmelCase = xmod_layer.adapter_modules[lang_code]
lowerCAmelCase = from_adapter.fca.weight
lowerCAmelCase = from_adapter.fca.bias
lowerCAmelCase = from_adapter.fca.weight
lowerCAmelCase = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
lowerCAmelCase = xmod_sent_encoder.layer_norm.weight
lowerCAmelCase = xmod_sent_encoder.layer_norm.bias
if classification_head:
lowerCAmelCase = xmod.model.classification_heads["""mnli"""].dense.weight
lowerCAmelCase = xmod.model.classification_heads["""mnli"""].dense.bias
lowerCAmelCase = xmod.model.classification_heads["""mnli"""].out_proj.weight
lowerCAmelCase = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
lowerCAmelCase = xmod.model.encoder.lm_head.dense.weight
lowerCAmelCase = xmod.model.encoder.lm_head.dense.bias
lowerCAmelCase = xmod.model.encoder.lm_head.layer_norm.weight
lowerCAmelCase = xmod.model.encoder.lm_head.layer_norm.bias
lowerCAmelCase = xmod.model.encoder.lm_head.weight
lowerCAmelCase = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowerCAmelCase = xmod.encode(SCREAMING_SNAKE_CASE ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(SCREAMING_SNAKE_CASE )
lowerCAmelCase = model(SCREAMING_SNAKE_CASE )[0]
if classification_head:
lowerCAmelCase = xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE ) )
else:
lowerCAmelCase = xmod.model(SCREAMING_SNAKE_CASE , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
lowerCAmelCase = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
lowerCAmelCase = torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(SCREAMING_SNAKE_CASE ).mkdir(parents=SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 46 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: dict , a__: str ) -> set[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = set(a__ ), [start]
while stack:
_UpperCAmelCase = stack.pop()
explored.add(a__ )
# 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(a__ )
return explored
lowerCAmelCase__ :Tuple = {
'''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'''))
| 329 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
a_ = ''
if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
class _lowercase ( tr.AbstractTransform ):
def __init__( self : str , snake_case : str = " " ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = sentence_delimiter
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , snake_case : str ) -> Optional[int]:
"""simple docstring"""
return list(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , snake_case : List[str] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Tuple = []
for sent_idx, sentence in enumerate(snake_case ):
chars.extend(self.process_string(snake_case ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case ) - 1:
chars.append(self.sentence_delimiter )
return chars
a_ = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
a_ = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
a_ = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
a_ = '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'
a_ = '\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> cer = datasets.load_metric("cer")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
] , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : Tuple , snake_case : Tuple , snake_case : List[str]=False ) -> Any:
"""simple docstring"""
if concatenate_texts:
return jiwer.compute_measures(
snake_case , snake_case , truth_transform=snake_case , hypothesis_transform=snake_case , )["wer"]
UpperCamelCase_ : Dict = 0
UpperCamelCase_ : Union[str, Any] = 0
for prediction, reference in zip(snake_case , snake_case ):
UpperCamelCase_ : str = jiwer.compute_measures(
snake_case , snake_case , truth_transform=snake_case , hypothesis_transform=snake_case , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 50 | from manim import *
class _lowercase ( snake_case_ ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ : str = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_ : Optional[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCamelCase_ : int = [mem.copy() for i in range(6 )]
UpperCamelCase_ : List[Any] = [mem.copy() for i in range(6 )]
UpperCamelCase_ : Dict = VGroup(*snake_case ).arrange(snake_case , buff=0 )
UpperCamelCase_ : List[str] = VGroup(*snake_case ).arrange(snake_case , buff=0 )
UpperCamelCase_ : int = VGroup(snake_case , snake_case ).arrange(snake_case , buff=0 )
UpperCamelCase_ : int = Text('CPU' , font_size=2_4 )
UpperCamelCase_ : List[str] = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case )
UpperCamelCase_ : Union[str, Any] = [mem.copy() for i in range(1 )]
UpperCamelCase_ : Dict = VGroup(*snake_case ).arrange(snake_case , buff=0 )
UpperCamelCase_ : Union[str, Any] = Text('GPU' , font_size=2_4 )
UpperCamelCase_ : Optional[Any] = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
gpu.align_to(snake_case , snake_case )
gpu.set_x(gpu.get_x() - 1 )
self.add(snake_case )
UpperCamelCase_ : int = [mem.copy() for i in range(6 )]
UpperCamelCase_ : int = VGroup(*snake_case ).arrange(snake_case , buff=0 )
UpperCamelCase_ : Tuple = Text('Model' , font_size=2_4 )
UpperCamelCase_ : Dict = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
model.move_to([3, -1.0, 0] )
self.play(
Create(snake_case , run_time=1 ) , Create(snake_case , run_time=1 ) , Create(snake_case , run_time=1 ) , )
UpperCamelCase_ : Union[str, Any] = MarkupText(
f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM." , font_size=2_4 , )
UpperCamelCase_ : Tuple = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCamelCase_ : Dict = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case , run_time=2.5 ) , Write(snake_case ) , Write(snake_case ) )
self.add(snake_case )
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[str] = []
UpperCamelCase_ : Tuple = []
for i, rect in enumerate(snake_case ):
UpperCamelCase_ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(snake_case , opacity=0.7 )
cpu_target.move_to(snake_case )
cpu_target.generate_target()
UpperCamelCase_ : int = 0.46 / 4
UpperCamelCase_ : Tuple = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=snake_case )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=snake_case , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=snake_case , buff=0.0 )
cpu_targs.append(snake_case )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(snake_case ) )
second_animations.append(MoveToTarget(snake_case , run_time=1.5 ) )
self.play(*snake_case )
self.play(*snake_case )
self.wait()
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowercase : Optional[int] = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowercase : Optional[Any] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> tuple[str, float]:
_snake_case = len([g for position, g in enumerate(__A ) if g == main_target[position]] )
return (item, float(__A ))
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> tuple[str, str]:
_snake_case = random.randint(0 , len(__A ) - 1 )
_snake_case = parent_a[:random_slice] + parent_a[random_slice:]
_snake_case = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> str:
_snake_case = list(__A )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
_snake_case = random.choice(__A )
return "".join(__A )
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , ) -> list[str]:
_snake_case = []
# Generate more children proportionally to the fitness score.
_snake_case = int(parent_a[1] * 100 ) + 1
_snake_case = 10 if child_n >= 10 else child_n
for _ in range(__A ):
_snake_case = population_score[random.randint(0 , __A )][0]
_snake_case , _snake_case = crossover(parent_a[0] , __A )
# Append new string to the population list.
pop.append(mutate(__A , __A ) )
pop.append(mutate(__A , __A ) )
return pop
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
_snake_case = F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(__A )
# Verify that the target contains no genes besides the ones inside genes variable.
_snake_case = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
_snake_case = F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(__A )
# Generate random starting population.
_snake_case = []
for _ in range(__A ):
population.append(''.join([random.choice(__A ) for i in range(len(__A ) )] ) )
# Just some logs to know what the algorithms is doing.
_snake_case , _snake_case = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__A )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
_snake_case = [evaluate(__A , __A ) for item in population]
# Check if there is a matching evolution.
_snake_case = sorted(__A , key=lambda __A : x[1] , reverse=__A )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
_snake_case = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__A )
# Normalize population score to be between 0 and 1.
_snake_case = [
(item, score / len(__A )) for item, score in population_score
]
# This is selection
for i in range(__A ):
population.extend(select(population_score[int(__A )] , __A , __A ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__A ) > N_POPULATION:
break
if __name__ == "__main__":
lowercase : str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
lowercase : str = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
lowercase , lowercase , lowercase : Tuple = basic(target_str, genes_list)
print(
F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 42 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter) for letter in string.ascii_lowercase]
lowercase : set[int] = {ord(char) for char in VALID_CHARS}
lowercase : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> str | None:
_snake_case = ""
_snake_case = 42
_snake_case = 42
_snake_case = 42
for keychar, cipherchar in zip(cycle(__A ) , __A ):
_snake_case = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(__A )
return decoded
def SCREAMING_SNAKE_CASE__ ( __A ) -> list[str]:
_snake_case = []
for key in product(__A , repeat=3 ):
_snake_case = try_key(__A , __A )
if encoded is not None:
possibles.append(__A )
return possibles
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> list[str]:
return [possible for possible in possibles if common_word in possible.lower()]
def SCREAMING_SNAKE_CASE__ ( __A = "p059_cipher.txt" ) -> int:
_snake_case = 42
_snake_case = 42
_snake_case = 42
_snake_case = 42
_snake_case = Path(__A ).parent.joinpath(__A ).read_text(encoding='utf-8' )
_snake_case = [int(__A ) for number in data.strip().split(',' )]
_snake_case = filter_valid_chars(__A )
for common_word in COMMON_WORDS:
_snake_case = filter_common_word(__A , __A )
if len(__A ) == 1:
break
_snake_case = possibles[0]
return sum(ord(__A ) for char in decoded_text )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
_UpperCAmelCase : int =''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__lowerCamelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 242 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase ="glpn"
def __init__( self , snake_case=3 , snake_case=4 , snake_case=[2, 2, 2, 2] , snake_case=[8, 4, 2, 1] , snake_case=[3_2, 6_4, 1_6_0, 2_5_6] , snake_case=[7, 3, 3, 3] , snake_case=[4, 2, 2, 2] , snake_case=[1, 2, 5, 8] , snake_case=[4, 4, 4, 4] , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=0.1 , snake_case=1E-6 , snake_case=6_4 , snake_case=1_0 , snake_case=-1 , **snake_case , ) -> Tuple:
'''simple docstring'''
super().__init__(**snake_case)
_UpperCAmelCase : Any =num_channels
_UpperCAmelCase : List[str] =num_encoder_blocks
_UpperCAmelCase : Optional[Any] =depths
_UpperCAmelCase : str =sr_ratios
_UpperCAmelCase : Dict =hidden_sizes
_UpperCAmelCase : List[str] =patch_sizes
_UpperCAmelCase : Any =strides
_UpperCAmelCase : List[str] =mlp_ratios
_UpperCAmelCase : Dict =num_attention_heads
_UpperCAmelCase : List[str] =hidden_act
_UpperCAmelCase : int =hidden_dropout_prob
_UpperCAmelCase : List[Any] =attention_probs_dropout_prob
_UpperCAmelCase : Union[str, Any] =initializer_range
_UpperCAmelCase : Tuple =drop_path_rate
_UpperCAmelCase : str =layer_norm_eps
_UpperCAmelCase : Optional[int] =decoder_hidden_size
_UpperCAmelCase : List[str] =max_depth
_UpperCAmelCase : Dict =head_in_index
| 242 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase : Tuple = logging.get_logger(__name__)
_lowercase : Tuple = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_lowercase : Dict = [
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
_lowercase : str = {
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
_lowercase : Optional[Any] = {f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowercase : Optional[Any] = {f"""funnel-transformer/{name}""": {'do_lower_case': True} for name in _model_names}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Dict = VOCAB_FILES_NAMES
a__ : int = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_INIT_CONFIGURATION
a__ : List[str] = FunnelTokenizer
a__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : int = 2
def __init__( self : List[str] , _lowercase : Any=None , _lowercase : Tuple=None , _lowercase : int=True , _lowercase : int="<unk>" , _lowercase : List[str]="<sep>" , _lowercase : Dict="<pad>" , _lowercase : Any="<cls>" , _lowercase : int="<mask>" , _lowercase : Optional[int]="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Tuple=True , _lowercase : List[Any]=True , _lowercase : Any=None , _lowercase : int="##" , **_lowercase : Optional[int] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , bos_token=_lowercase , eos_token=_lowercase , clean_text=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , wordpieces_prefix=_lowercase , **_lowercase , )
__UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowercase ) != tokenize_chinese_chars
):
__UpperCAmelCase = getattr(_lowercase , normalizer_state.pop('''type''' ) )
__UpperCAmelCase = do_lower_case
__UpperCAmelCase = strip_accents
__UpperCAmelCase = tokenize_chinese_chars
__UpperCAmelCase = normalizer_class(**_lowercase )
__UpperCAmelCase = do_lower_case
def a ( self : Union[str, Any] , _lowercase : str , _lowercase : Union[str, Any]=None ):
__UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
__UpperCAmelCase = [self.sep_token_id]
__UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a ( self : str , _lowercase : str , _lowercase : Optional[str] = None ):
__UpperCAmelCase = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 332 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def lowercase__ ( snake_case_ :Optional[int] ):
return EnvironmentCommand()
def lowercase__ ( snake_case_ :List[str] ):
return EnvironmentCommand(args.accelerate_config_file )
class _UpperCAmelCase ( _lowerCAmelCase ):
@staticmethod
def a ( _lowercase : ArgumentParser ):
__UpperCAmelCase = parser.add_parser('''env''' )
download_parser.set_defaults(func=_lowercase )
download_parser.add_argument(
'''--accelerate-config_file''' , default=_lowercase , help='''The accelerate config file to use for the default values in the launching script.''' , )
download_parser.set_defaults(func=_lowercase )
def __init__( self : Optional[int] , _lowercase : str , *_lowercase : Tuple ):
__UpperCAmelCase = accelerate_config_file
def a ( self : Dict ):
__UpperCAmelCase = '''not installed'''
if is_safetensors_available():
import safetensors
__UpperCAmelCase = safetensors.__version__
elif importlib.util.find_spec('''safetensors''' ) is not None:
import safetensors
__UpperCAmelCase = F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = __UpperCAmelCase = '''not found'''
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
__UpperCAmelCase = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(_lowercase ):
__UpperCAmelCase = load_config_from_file(self._accelerate_config_file ).to_dict()
__UpperCAmelCase = (
'''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(_lowercase , _lowercase )
else F'''\t{accelerate_config}'''
)
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = '''NA'''
if is_torch_available():
import torch
__UpperCAmelCase = torch.__version__
__UpperCAmelCase = torch.cuda.is_available()
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = '''NA'''
if is_tf_available():
import tensorflow as tf
__UpperCAmelCase = tf.__version__
try:
# deprecated in v2.1
__UpperCAmelCase = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
__UpperCAmelCase = bool(tf.config.list_physical_devices('''GPU''' ) )
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = '''not installed'''
__UpperCAmelCase = '''NA'''
if is_flax_available():
import flax
import jax
import jaxlib
__UpperCAmelCase = flax.__version__
__UpperCAmelCase = jax.__version__
__UpperCAmelCase = jaxlib.__version__
__UpperCAmelCase = jax.lib.xla_bridge.get_backend().platform
__UpperCAmelCase = {
'''`transformers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Huggingface_hub version''': huggingface_hub.__version__,
'''Safetensors version''': F'''{safetensors_version}''',
'''Accelerate version''': F'''{accelerate_version}''',
'''Accelerate config''': F'''{accelerate_config_str}''',
'''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
'''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
'''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
'''Jax version''': F'''{jax_version}''',
'''JaxLib version''': F'''{jaxlib_version}''',
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(_lowercase ) )
return info
@staticmethod
def a ( _lowercase : str ):
return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 332 | 1 |
import inspect
import unittest
class _a ( unittest.TestCase):
def UpperCAmelCase__( self : Union[str, Any] )-> Optional[int]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase__( self : Any )-> Any:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase__ : List[str] = inspect.getmembers(_SCREAMING_SNAKE_CASE , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase__ : Any = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase__ : Union[str, Any] = '''invisible-watermark'''
assert backend in deps, F'{backend} is not in the deps table!'
| 211 |
from math import isqrt
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _a , _a ):
lowerCAmelCase__ : int = False
return [i for i in range(2 , _a ) if is_prime[i]]
def lowerCamelCase_ ( _a = 10**8 ):
"""simple docstring"""
lowerCAmelCase__ : Any = calculate_prime_numbers(max_number // 2 )
lowerCAmelCase__ : Tuple = 0
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : Optional[int] = len(_a ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f'''{solution() = }''')
| 211 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase_ = 10
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : int ) -> int:
for i in range(_lowerCamelCase , _lowerCamelCase ):
if array[i] == target:
return i
return -1
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ) -> int:
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase )
while left <= right:
if right - left < precision:
return lin_search(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = (left + right) // 3 + 1
_lowerCAmelCase : Optional[int] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
_lowerCAmelCase : Optional[Any] = one_third - 1
elif array[two_third] < target:
_lowerCAmelCase : List[Any] = two_third + 1
else:
_lowerCAmelCase : Optional[int] = one_third + 1
_lowerCAmelCase : List[str] = two_third - 1
else:
return -1
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : int ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : List[Any] = (left + right) // 3 + 1
_lowerCAmelCase : List[str] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowerCamelCase , one_third - 1 , _lowerCamelCase , _lowerCamelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowerCamelCase , _lowerCamelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase_ = input("""Enter numbers separated by comma:\n""").strip()
UpperCamelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
UpperCamelCase_ = int(input("""Enter the number to be found in the list:\n""").strip())
UpperCamelCase_ = ite_ternary_search(collection, target)
UpperCamelCase_ = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'Iterative search: {target} found at positions: {resulta}')
print(F'Recursive search: {target} found at positions: {resulta}')
else:
print("""Not found""")
| 309 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"""vocab_file""": """vocab.txt"""}
UpperCamelCase_ = {
"""vocab_file""": {
"""YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
"""YituTech/conv-bert-medium-small""": (
"""https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
),
"""YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
}
}
UpperCamelCase_ = {
"""YituTech/conv-bert-base""": 5_12,
"""YituTech/conv-bert-medium-small""": 5_12,
"""YituTech/conv-bert-small""": 5_12,
}
UpperCamelCase_ = {
"""YituTech/conv-bert-base""": {"""do_lower_case""": True},
"""YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
"""YituTech/conv-bert-small""": {"""do_lower_case""": True},
}
class a_ (_a ):
__lowerCAmelCase : Any = VOCAB_FILES_NAMES
__lowerCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Dict = PRETRAINED_INIT_CONFIGURATION
__lowerCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Optional[int] = ConvBertTokenizer
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=True , snake_case_="[UNK]" , snake_case_="[SEP]" , snake_case_="[PAD]" , snake_case_="[CLS]" , snake_case_="[MASK]" , snake_case_=True , snake_case_=None , **snake_case_ , ):
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , tokenize_chinese_chars=snake_case_ , strip_accents=snake_case_ , **snake_case_ , )
_lowerCAmelCase : List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , snake_case_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , snake_case_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , snake_case_ ) != tokenize_chinese_chars
):
_lowerCAmelCase : Dict = getattr(snake_case_ , normalizer_state.pop("""type""" ) )
_lowerCAmelCase : List[str] = do_lower_case
_lowerCAmelCase : str = strip_accents
_lowerCAmelCase : List[Any] = tokenize_chinese_chars
_lowerCAmelCase : List[Any] = normalizer_class(**snake_case_ )
_lowerCAmelCase : str = do_lower_case
def __UpperCamelCase ( self , snake_case_ , snake_case_=None ):
_lowerCAmelCase : List[str] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None ):
_lowerCAmelCase : Optional[Any] = [self.sep_token_id]
_lowerCAmelCase : Optional[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 __UpperCamelCase ( self , snake_case_ , snake_case_ = None ):
_lowerCAmelCase : Any = self._tokenizer.model.save(snake_case_ , name=snake_case_ )
return tuple(snake_case_ )
| 309 | 1 |
"""simple docstring"""
import math
import qiskit
def __A ( a_ :int = 1 , a_ :int = 1 , a_ :int = 1) -> qiskit.result.counts.Counts:
if (
isinstance(a_ , a_)
or isinstance(a_ , a_)
or isinstance(a_ , a_)
):
raise TypeError('''inputs must be integers.''')
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''')
if (
(math.floor(a_) != input_a)
or (math.floor(a_) != input_a)
or (math.floor(a_) != carry_in)
):
raise ValueError('''inputs must be exact integers.''')
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''')
# build registers
__a : str = qiskit.QuantumRegister(4 , '''qr''')
__a : List[Any] = qiskit.ClassicalRegister(2 , '''cr''')
# list the entries
__a : Any = [input_a, input_a, carry_in]
__a : List[Any] = qiskit.QuantumCircuit(a_ , a_)
for i in range(0 , 3):
if entry[i] == 2:
quantum_circuit.h(a_) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(a_) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(a_) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3) # ccx = toffoli gate
quantum_circuit.cx(0 , 1)
quantum_circuit.ccx(1 , 2 , 3)
quantum_circuit.cx(1 , 2)
quantum_circuit.cx(0 , 1)
quantum_circuit.measure([2, 3] , a_) # measure the last two qbits
__a : int = qiskit.Aer.get_backend('''aer_simulator''')
__a : Union[str, Any] = qiskit.execute(a_ , a_ , shots=10_00)
return job.result().get_counts(a_)
if __name__ == "__main__":
print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}') | 188 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'''vocab_file''': '''spiece.model'''}
A = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
}
}
A = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
A = '''▁'''
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase="[CLS]" , _UpperCAmelCase="[SEP]" , _UpperCAmelCase="<unk>" , _UpperCAmelCase="[SEP]" , _UpperCAmelCase="<pad>" , _UpperCAmelCase="[CLS]" , _UpperCAmelCase="[MASK]" , _UpperCAmelCase = None , **_UpperCAmelCase , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__a : int = (
AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase , normalized=_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase )
else mask_token
)
__a : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
__a : Tuple = do_lower_case
__a : Optional[Any] = remove_space
__a : Optional[Any] = keep_accents
__a : Union[str, Any] = vocab_file
__a : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCAmelCase )
@property
def _lowerCamelCase ( self ):
return len(self.sp_model )
def _lowerCamelCase ( self ):
__a : Any = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__a : str = self.__dict__.copy()
__a : Tuple = None
return state
def __setstate__( self , _UpperCAmelCase ):
__a : Any = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__a : Optional[Any] = {}
__a : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCamelCase ( self , _UpperCAmelCase ):
if self.remove_space:
__a : Any = ''' '''.join(inputs.strip().split() )
else:
__a : Tuple = inputs
__a : Union[str, Any] = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__a : List[str] = unicodedata.normalize('''NFKD''' , _UpperCAmelCase )
__a : Optional[int] = ''''''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )
if self.do_lower_case:
__a : Optional[Any] = outputs.lower()
return outputs
def _lowerCamelCase ( self , _UpperCAmelCase ):
__a : int = self.preprocess_text(_UpperCAmelCase )
__a : Tuple = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
__a : int = []
for piece in pieces:
if len(_UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__a : List[str] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__a : Tuple = cur_pieces[1:]
else:
__a : Optional[Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_UpperCAmelCase )
else:
new_pieces.append(_UpperCAmelCase )
return new_pieces
def _lowerCamelCase ( self , _UpperCAmelCase ):
return self.sp_model.PieceToId(_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase ):
return self.sp_model.IdToPiece(_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase ):
__a : List[str] = []
__a : str = ''''''
__a : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCAmelCase ) + token
__a : Tuple = True
__a : Tuple = []
else:
current_sub_tokens.append(_UpperCAmelCase )
__a : Optional[int] = False
out_string += self.sp_model.decode(_UpperCAmelCase )
return out_string.strip()
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : int = [self.sep_token_id]
__a : Union[str, Any] = [self.cls_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 ):
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 not None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : Union[str, Any] = [self.sep_token_id]
__a : Tuple = [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 ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a : List[str] = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase , '''wb''' ) as fi:
__a : Any = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,) | 188 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_UpperCamelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
_UpperCamelCase = [0, 25, 50]
_UpperCamelCase = [25, 50, 75]
_UpperCamelCase = fuzz.membership.trimf(X, abca)
_UpperCamelCase = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
_UpperCamelCase = np.ones(75)
_UpperCamelCase = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
_UpperCamelCase = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
_UpperCamelCase = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
_UpperCamelCase = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
_UpperCamelCase = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
_UpperCamelCase = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
_UpperCamelCase = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
_UpperCamelCase = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
_UpperCamelCase = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 208 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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 logging
if is_vision_available():
import PIL
A__ : List[str] =logging.get_logger(__name__)
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class UpperCAmelCase ( snake_case_ ):
_lowercase: Any = ['''pixel_values''']
def __init__( self : Tuple , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_55 , __snake_case : bool = True , __snake_case : bool = True , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , **__snake_case : str , ) -> None:
super().__init__(**__snake_case )
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 2_56}
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = resample
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = offset
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : int , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" in size:
_lowerCAmelCase = get_resize_output_image_size(__snake_case , size["""shortest_edge"""] , default_to_square=__snake_case )
elif "height" in size and "width" in size:
_lowerCAmelCase = (size["""height"""], size["""width"""])
else:
raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : List[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case )
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(__snake_case , size=(size["""height"""], size["""width"""]) , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : bool = True , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> Dict:
_lowerCAmelCase = image.astype(np.floataa )
if offset:
_lowerCAmelCase = image - (scale / 2)
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Optional[int] , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Tuple , ) -> np.ndarray:
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[ChannelDimension] = 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.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
_lowerCAmelCase = to_numpy_array(__snake_case )
if do_resize:
_lowerCAmelCase = self.resize(image=__snake_case , size=__snake_case , resample=__snake_case )
if do_center_crop:
_lowerCAmelCase = self.center_crop(__snake_case , size=__snake_case )
if do_rescale:
_lowerCAmelCase = self.rescale(image=__snake_case , scale=__snake_case , offset=__snake_case )
if do_normalize:
_lowerCAmelCase = self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case )
_lowerCAmelCase = to_channel_dimension_format(__snake_case , __snake_case )
return image
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[str, TensorType]] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : List[str] , ) -> PIL.Image.Image:
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = offset if offset is not None else self.offset
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
_lowerCAmelCase = make_batched(__snake_case )
_lowerCAmelCase = [
[
self._preprocess_image(
image=__snake_case , do_resize=__snake_case , size=__snake_case , resample=__snake_case , do_center_crop=__snake_case , crop_size=__snake_case , do_rescale=__snake_case , rescale_factor=__snake_case , offset=__snake_case , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , data_format=__snake_case , )
for img in video
]
for video in videos
]
_lowerCAmelCase = {"""pixel_values""": videos}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 70 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'dandelin/vilt-b32-finetuned-vqa'
lowerCamelCase = (
'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
'image containing the information, as well as a `question` which should be the question in English. It '
'returns a text that is the answer to the question.'
)
lowerCamelCase = 'image_qa'
lowerCamelCase = AutoProcessor
lowerCamelCase = AutoModelForVisualQuestionAnswering
lowerCamelCase = ['image', 'text']
lowerCamelCase = ['text']
def __init__( self : Dict,*lowercase_ : Optional[Any],**lowercase_ : int )-> Any:
'''simple docstring'''
requires_backends(self,['vision'] )
super().__init__(*lowercase_,**lowercase_ )
def snake_case__ ( self : Dict,lowercase_ : "Image",lowercase_ : str )-> int:
'''simple docstring'''
return self.pre_processor(lowercase_,lowercase_,return_tensors='pt' )
def snake_case__ ( self : Optional[int],lowercase_ : int )-> Tuple:
'''simple docstring'''
with torch.no_grad():
return self.model(**lowercase_ ).logits
def snake_case__ ( self : List[str],lowercase_ : Optional[int] )-> Optional[int]:
'''simple docstring'''
A__ = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
# See all BART models at https://huggingface.co/models?filter=bart
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'bart'
lowerCamelCase = ['past_key_values']
lowerCamelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Tuple,lowercase_ : Optional[int]=5_0_2_6_5,lowercase_ : List[str]=1_0_2_4,lowercase_ : Any=1_2,lowercase_ : Optional[Any]=4_0_9_6,lowercase_ : str=1_6,lowercase_ : int=1_2,lowercase_ : Optional[Any]=4_0_9_6,lowercase_ : Any=1_6,lowercase_ : Any=0.0,lowercase_ : str=0.0,lowercase_ : Optional[Any]="gelu",lowercase_ : List[str]=1_0_2_4,lowercase_ : List[Any]=0.1,lowercase_ : Union[str, Any]=0.0,lowercase_ : Optional[int]=0.0,lowercase_ : List[Any]=0.02,lowercase_ : int=0.0,lowercase_ : Optional[Any]=False,lowercase_ : List[Any]=True,lowercase_ : Union[str, Any]=3,lowercase_ : int=1,lowercase_ : int=0,lowercase_ : List[str]=2,lowercase_ : Optional[int]=True,lowercase_ : Tuple=2,lowercase_ : List[str]=2,**lowercase_ : Dict,)-> List[Any]:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = encoder_ffn_dim
A__ = encoder_layers
A__ = encoder_attention_heads
A__ = decoder_ffn_dim
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = activation_function
A__ = init_std
A__ = encoder_layerdrop
A__ = decoder_layerdrop
A__ = classifier_dropout
A__ = use_cache
A__ = encoder_layers
A__ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_ ):
A__ = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'The config can simply be saved and uploaded again to be fixed.' )
class A ( _UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : Dict )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
A__ = {0: 'batch'}
A__ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
A__ = {0: 'batch', 1: 'decoder_sequence'}
A__ = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_,direction='inputs' )
elif self.task == "causal-lm":
# TODO: figure this case out.
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
A__ , A__ = self.num_layers
for i in range(lowercase_ ):
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
else:
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
] )
return common_inputs
@property
def snake_case__ ( self : Optional[Any] )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = super().outputs
else:
A__ = super(lowercase_,self ).outputs
if self.use_past:
A__ , A__ = self.num_layers
for i in range(lowercase_ ):
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def snake_case__ ( self : Tuple,lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
# Generate decoder inputs
A__ = seq_length if not self.use_past else 1
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
A__ = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
A__ = dict(**lowercase_,**lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
A__ , A__ = common_inputs['input_ids'].shape
A__ = common_inputs['decoder_input_ids'].shape[1]
A__ , A__ = self.num_attention_heads
A__ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
A__ = decoder_seq_length + 3
A__ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
A__ = torch.cat(
[common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_ )],dim=1 )
A__ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
A__ , A__ = self.num_layers
A__ = min(lowercase_,lowercase_ )
A__ = max(lowercase_,lowercase_ ) - min_num_layers
A__ = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
A__ = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(lowercase_,lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def snake_case__ ( self : List[str],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
A__ , A__ = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
A__ = seqlen + 2
A__ , A__ = self.num_layers
A__ , A__ = self.num_attention_heads
A__ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
A__ = common_inputs['attention_mask'].dtype
A__ = torch.cat(
[common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_ )],dim=1 )
A__ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def snake_case__ ( self : Union[str, Any],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = compute_effective_axis_dimension(
lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
A__ = tokenizer.num_special_tokens_to_add(lowercase_ )
A__ = compute_effective_axis_dimension(
lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
A__ = [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
A__ = dict(tokenizer(lowercase_,return_tensors=lowercase_ ) )
return common_inputs
def snake_case__ ( self : Union[str, Any],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
elif self.task == "causal-lm":
A__ = self._generate_dummy_inputs_for_causal_lm(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
else:
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
return common_inputs
def snake_case__ ( self : int,lowercase_ : Tuple,lowercase_ : int,lowercase_ : int,lowercase_ : str )-> str:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_ )
else:
A__ = super(lowercase_,self )._flatten_past_key_values_(
lowercase_,lowercase_,lowercase_,lowercase_ )
| 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__lowerCamelCase = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ["""HerbertTokenizerFast"""]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 59 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Dict ) -> List[str]:
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class A__ ( nn.Module ):
def __init__( self : Any , _UpperCAmelCase : nn.Module , _UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
super().__init__()
__lowercase = module
__lowercase = nn.Sequential(
nn.Linear(module.in_features , _UpperCAmelCase , bias=_UpperCAmelCase ) , nn.Linear(_UpperCAmelCase , module.out_features , bias=_UpperCAmelCase ) , )
__lowercase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=_UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def a__ ( self : str , _UpperCAmelCase : List[str] , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.module(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase ) + self.adapter(_UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A__ ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ : int = "bigscience/bloom-1b7"
# Constant values
lowerCAmelCase__ : Any = 2.109659552692574
lowerCAmelCase__ : str = "Hello my name is"
lowerCAmelCase__ : Any = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
lowerCAmelCase__ : List[Any] = 10
def a__ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = AutoTokenizer.from_pretrained(self.model_name )
class A__ ( lowerCAmelCase__ ):
def a__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# Models and tokenizer
__lowercase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
__lowercase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
def a__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def a__ ( self : str ) -> int:
"""simple docstring"""
__lowercase = self.model_abit.config
self.assertTrue(hasattr(_UpperCAmelCase , 'quantization_config' ) )
__lowercase = config.to_dict()
__lowercase = config.to_diff_dict()
__lowercase = config.to_json_string()
def a__ ( self : Dict ) -> Tuple:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
__lowercase = self.model_fpaa.get_memory_footprint()
__lowercase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowercase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def a__ ( self : Tuple ) -> str:
"""simple docstring"""
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(_UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def a__ ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' )
__lowercase = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def a__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = BitsAndBytesConfig()
__lowercase = True
__lowercase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_UpperCAmelCase , device_map='auto' )
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' )
__lowercase = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
with self.assertRaises(_UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(_UpperCAmelCase )
def a__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = BitsAndBytesConfig()
with self.assertRaises(_UpperCAmelCase ):
__lowercase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_UpperCAmelCase , load_in_abit=_UpperCAmelCase , device_map='auto' , bnb_abit_quant_type='nf4' , )
def a__ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
with self.assertRaises(_UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' )
__lowercase = self.model_fpaa.to(torch.floataa )
__lowercase = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowercase = self.model_fpaa.to('cpu' )
# Check this does not throw an error
__lowercase = self.model_fpaa.half()
# Check this does not throw an error
__lowercase = self.model_fpaa.float()
def a__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=_UpperCAmelCase , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A__ ( unittest.TestCase ):
@classmethod
def a__ ( cls : int ) -> Tuple:
"""simple docstring"""
__lowercase = 't5-small'
__lowercase = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
__lowercase = AutoTokenizer.from_pretrained(cls.model_name )
__lowercase = 'Translate in German: Hello, my dog is cute'
def a__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
def a__ ( self : int ) -> int:
"""simple docstring"""
from transformers import TaForConditionalGeneration
__lowercase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowercase = None
# test with `t5-small`
__lowercase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__lowercase = model.generate(**_UpperCAmelCase )
# test with `flan-t5-small`
__lowercase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__lowercase = model.generate(**_UpperCAmelCase )
__lowercase = modules
def a__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowercase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__lowercase = model.generate(**_UpperCAmelCase )
# test with `flan-t5-small`
__lowercase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__lowercase = model.generate(**_UpperCAmelCase )
class A__ ( lowerCAmelCase__ ):
def a__ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().setUp()
# model_name
__lowercase = 'bigscience/bloom-560m'
__lowercase = 't5-small'
# Different types of model
__lowercase = AutoModel.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
# Sequence classification model
__lowercase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
# CausalLM model
__lowercase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
# Seq2seq model
__lowercase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=_UpperCAmelCase , device_map='auto' )
def a__ ( self : int ) -> List[str]:
"""simple docstring"""
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def a__ ( self : Tuple ) -> str:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class A__ ( lowerCAmelCase__ ):
def a__ ( self : str ) -> str:
"""simple docstring"""
super().setUp()
def a__ ( self : Dict ) -> Any:
"""simple docstring"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def a__ ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowercase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class A__ ( lowerCAmelCase__ ):
def a__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
def a__ ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=_UpperCAmelCase , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowercase = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
__lowercase = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class A__ ( lowerCAmelCase__ ):
def a__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = 'facebook/opt-350m'
super().setUp()
def a__ ( self : Dict ) -> List[str]:
"""simple docstring"""
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
__lowercase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowercase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowercase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(_UpperCAmelCase ) ):
__lowercase = LoRALayer(module.q_proj , rank=16 )
__lowercase = LoRALayer(module.k_proj , rank=16 )
__lowercase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowercase = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowercase = model.forward(**_UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(_UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class A__ ( lowerCAmelCase__ ):
lowerCAmelCase__ : Any = "gpt2-xl"
lowerCAmelCase__ : str = 3.3191854854152187
| 325 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ :
def __init__( self : str ,__lowerCamelCase : Any ,__lowerCamelCase : Optional[Any]=13 ,__lowerCamelCase : int=32 ,__lowerCamelCase : List[str]=3 ,__lowerCamelCase : int=4 ,__lowerCamelCase : Tuple=[10, 20, 30, 40] ,__lowerCamelCase : Optional[int]=[2, 2, 3, 2] ,__lowerCamelCase : int=True ,__lowerCamelCase : Tuple=True ,__lowerCamelCase : Union[str, Any]=37 ,__lowerCamelCase : Dict="gelu" ,__lowerCamelCase : int=10 ,__lowerCamelCase : Any=0.02 ,__lowerCamelCase : Dict=["stage2", "stage3", "stage4"] ,__lowerCamelCase : Tuple=[2, 3, 4] ,__lowerCamelCase : Dict=None ,):
'''simple docstring'''
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] ,self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,num_stages=self.num_stages ,hidden_act=self.hidden_act ,is_decoder=__lowerCamelCase ,initializer_range=self.initializer_range ,out_features=self.out_features ,out_indices=self.out_indices ,num_labels=self.num_labels ,)
def SCREAMING_SNAKE_CASE_ ( self : List[str] ,__lowerCamelCase : int ,__lowerCamelCase : int ,__lowerCamelCase : Optional[int] ):
'''simple docstring'''
a = ConvNextVaModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ,__lowerCamelCase : Tuple ,__lowerCamelCase : Dict ,__lowerCamelCase : int ):
'''simple docstring'''
a = ConvNextVaForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase ,labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : str ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : List[str] ,__lowerCamelCase : Optional[int] ):
'''simple docstring'''
a = ConvNextVaBackbone(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[1], 4, 4] )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,1 )
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a = config_and_inputs
a = {"""pixel_values""": pixel_values}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a = config_and_inputs
a = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
a = ConvNextVaModelTester(self )
a = ConfigTester(self ,config_class=__lowerCamelCase ,has_text_modality=__lowerCamelCase ,hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCamelCase ),
*get_values(__lowerCamelCase ),
]:
continue
a = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
a = self._prepare_for_class(__lowerCamelCase ,__lowerCamelCase ,return_labels=__lowerCamelCase )
a = model(**__lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCamelCase ), *get_values(__lowerCamelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCamelCase ,__lowerCamelCase ,return_labels=__lowerCamelCase )
a = model(**__lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCamelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
def check_hidden_states_output(__lowerCamelCase : Optional[int] ,__lowerCamelCase : List[Any] ,__lowerCamelCase : int ):
a = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCamelCase ,__lowerCamelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCamelCase ) ,expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( ) -> int:
"""simple docstring"""
a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
a = ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(__lowerCamelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCamelCase ,return_tensors='''pt''' ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCamelCase )
# verify the logits
a = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape ,__lowerCamelCase )
a = torch.tensor([0.9_996, 0.1_966, -0.4_386] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__lowerCamelCase ,atol=1e-4 ) )
| 351 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase_ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
a = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
a = '''The dog is cute and lives in the garden house'''
a = jnp.array([tokenizer.encode(__lowerCamelCase )] )
a = (1, 12, 7_68) # batch_size, sequence_length, embedding_vector_dim
a = jnp.array(
[[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]] )
a = model(__lowerCamelCase )['''last_hidden_state''']
self.assertEqual(output.shape ,__lowerCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,__lowerCamelCase ,atol=1e-3 ) )
| 330 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _lowerCAmelCase ( __a ):
_lowercase ='''gpt_neox'''
def __init__( self , _UpperCamelCase=50_432 , _UpperCamelCase=6_144 , _UpperCamelCase=44 , _UpperCamelCase=64 , _UpperCamelCase=24_576 , _UpperCamelCase="gelu" , _UpperCamelCase=0.25 , _UpperCamelCase=10_000 , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=0.1 , _UpperCamelCase=2_048 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-5 , _UpperCamelCase=True , _UpperCamelCase=0 , _UpperCamelCase=2 , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=None , **_UpperCamelCase , ) -> Dict:
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = rotary_pct
lowerCAmelCase_ = rotary_emb_base
lowerCAmelCase_ = attention_dropout
lowerCAmelCase_ = hidden_dropout
lowerCAmelCase_ = classifier_dropout
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = use_cache
lowerCAmelCase_ = tie_word_embeddings
lowerCAmelCase_ = use_parallel_residual
lowerCAmelCase_ = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!" )
def __a ( self ) -> Tuple:
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}""" )
lowerCAmelCase_ = self.rope_scaling.get("type" , _UpperCamelCase )
lowerCAmelCase_ = 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}""" )
| 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"DistilBertConfig",
"DistilBertOnnxConfig",
],
"tokenization_distilbert": ["DistilBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["DistilBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DistilBertForMaskedLM",
"DistilBertForMultipleChoice",
"DistilBertForQuestionAnswering",
"DistilBertForSequenceClassification",
"DistilBertForTokenClassification",
"DistilBertModel",
"DistilBertPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"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:
_A = [
"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
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 231 | 1 |
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def __lowerCamelCase ( lowerCAmelCase__=None ):
lowerCAmelCase__ = argparse.ArgumentParser(add_help=lowerCAmelCase__ , allow_abbrev=lowerCAmelCase__ )
# The main config parser
lowerCAmelCase__ = config_command_parser(lowerCAmelCase__ )
# The subparser to add commands to
lowerCAmelCase__ = config_parser.add_subparsers(title='subcommands' , dest='subcommand' )
# Then add other parsers with the parent parser
default_command_parser(lowerCAmelCase__ , parents=[parent_parser] )
update_command_parser(lowerCAmelCase__ , parents=[parent_parser] )
return config_parser
def __lowerCamelCase ( ):
lowerCAmelCase__ = get_config_parser()
lowerCAmelCase__ = config_parser.parse_args()
if not hasattr(lowerCAmelCase__ , 'func' ):
config_parser.print_help()
exit(1 )
# Run
args.func(lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 352 | import numpy as np
def __lowerCamelCase ( lowerCAmelCase__ ):
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( lowerCAmelCase__ ):
return vector * sigmoid(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 | 0 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
def A ( self : Optional[int] , UpperCamelCase__ : Dict=None , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Any=None , **UpperCamelCase__ : List[str] ):
"""simple docstring"""
if tokenize_kwargs is None:
UpperCamelCase = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
UpperCamelCase = truncation
UpperCamelCase = tokenize_kwargs
UpperCamelCase = {}
if return_tensors is not None:
UpperCamelCase = return_tensors
return preprocess_params, {}, postprocess_params
def A ( self : List[Any] , UpperCamelCase__ : int , **UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.framework
UpperCamelCase = self.tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
return model_inputs
def A ( self : Any , UpperCamelCase__ : str ):
"""simple docstring"""
UpperCamelCase = self.model(**UpperCamelCase__ )
return model_outputs
def A ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str]=False ):
"""simple docstring"""
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self : Tuple , *UpperCamelCase__ : Tuple , **UpperCamelCase__ : str ):
"""simple docstring"""
return super().__call__(*UpperCamelCase__ , **UpperCamelCase__ )
| 28 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
A_ = logging.get_logger(__name__)
A_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A_ = {
'''vocab_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
},
'''merges_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''Salesforce/codegen-350M-mono''': (
'''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
),
},
}
A_ = {
'''Salesforce/codegen-350M-mono''': 20_48,
}
class lowercase( __a ):
'''simple docstring'''
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = ["input_ids", "attention_mask"]
lowercase__ = CodeGenTokenizer
def __init__( self: Union[str, Any], a_: List[Any]=None, a_: str=None, a_: str=None, a_: Dict="<|endoftext|>", a_: Tuple="<|endoftext|>", a_: str="<|endoftext|>", a_: List[Any]=False, **a_: List[str], ):
'''simple docstring'''
super().__init__(
a_, a_, tokenizer_file=a_, unk_token=a_, bos_token=a_, eos_token=a_, add_prefix_space=a_, **a_, )
if kwargs.pop("""add_bos_token""", a_ ):
_snake_case : str = kwargs.pop("""name_or_path""", """""" )
raise ValueError(
"""Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
"""Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
"""This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
""" so that the fast tokenizer works correctly.""" )
_snake_case : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""", a_ ) != add_prefix_space:
_snake_case : Dict = getattr(a_, pre_tok_state.pop("""type""" ) )
_snake_case : Dict = add_prefix_space
_snake_case : str = pre_tok_class(**a_ )
_snake_case : List[Any] = add_prefix_space
def UpperCamelCase_ ( self: Any, *a_: Any, **a_: int ):
'''simple docstring'''
_snake_case : Optional[int] = kwargs.get("""is_split_into_words""", a_ )
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*a_, **a_ )
def UpperCamelCase_ ( self: Optional[Any], *a_: Any, **a_: List[str] ):
'''simple docstring'''
_snake_case : Dict = kwargs.get("""is_split_into_words""", a_ )
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*a_, **a_ )
def UpperCamelCase_ ( self: Optional[int], a_: str, a_: Optional[str] = None ):
'''simple docstring'''
_snake_case : List[Any] = self._tokenizer.model.save(a_, name=a_ )
return tuple(a_ )
def UpperCamelCase_ ( self: str, a_: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], a_: bool = False, a_: bool = None, a_: Optional[List[str]] = None, **a_: List[str], ):
'''simple docstring'''
_snake_case : Any = super().decode(
token_ids=a_, skip_special_tokens=a_, clean_up_tokenization_spaces=a_, **a_, )
if truncate_before_pattern is not None and len(a_ ) > 0:
_snake_case : List[str] = self.truncate(a_, a_ )
return decoded_text
def UpperCamelCase_ ( self: Dict, a_: Tuple, a_: Optional[Any] ):
'''simple docstring'''
def find_re(a_: Dict, a_: str, a_: Union[str, Any] ):
_snake_case : Any = pattern.search(a_, a_ )
return m.start() if m else -1
_snake_case : Tuple = [re.compile(a_, re.MULTILINE ) for pattern in truncate_before_pattern]
_snake_case : List[Any] = list(re.finditer("""^print""", a_, re.MULTILINE ) )
if len(a_ ) > 1:
_snake_case : int = completion[: prints[1].start()]
_snake_case : List[str] = list(re.finditer("""^def""", a_, re.MULTILINE ) )
if len(a_ ) > 1:
_snake_case : List[Any] = completion[: defs[1].start()]
_snake_case : int = 0
_snake_case : List[Any] = [
pos for pos in [find_re(a_, a_, a_ ) for terminal in terminals] if pos != -1
]
if len(a_ ) > 0:
return completion[: min(a_ )]
else:
return completion
| 64 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE = TypeVar("""T""")
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
return (position - 1) // 2
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
return (2 * position) + 1
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
return (2 * position) + 2
class SCREAMING_SNAKE_CASE_ ( Generic[T] ):
def __init__( self : List[str] ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = {}
UpperCamelCase = 0
def __len__( self : Dict ):
"""simple docstring"""
return self.elements
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return str(self.heap )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
return self.elements == 0
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : T , lowerCamelCase_ : int ):
"""simple docstring"""
self.heap.append((elem, weight) )
UpperCamelCase = self.elements
self.elements += 1
self._bubble_up(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
UpperCamelCase , UpperCamelCase = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
UpperCamelCase , UpperCamelCase = self.heap[0]
self._bubble_down(lowerCamelCase_ )
return elem
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : T , lowerCamelCase_ : int ):
"""simple docstring"""
UpperCamelCase = self.position_map[elem]
UpperCamelCase = (elem, weight)
if position > 0:
UpperCamelCase = get_parent_position(lowerCamelCase_ )
UpperCamelCase , UpperCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(lowerCamelCase_ )
else:
self._bubble_down(lowerCamelCase_ )
else:
self._bubble_down(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : T ):
"""simple docstring"""
UpperCamelCase = self.position_map[elem]
if curr_pos == 0:
return None
UpperCamelCase = get_parent_position(lowerCamelCase_ )
UpperCamelCase , UpperCamelCase = self.heap[curr_pos]
UpperCamelCase , UpperCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(lowerCamelCase_ , lowerCamelCase_ )
return self._bubble_up(lowerCamelCase_ )
return None
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : T ):
"""simple docstring"""
UpperCamelCase = self.position_map[elem]
UpperCamelCase , UpperCamelCase = self.heap[curr_pos]
UpperCamelCase = get_child_left_position(lowerCamelCase_ )
UpperCamelCase = get_child_right_position(lowerCamelCase_ )
if child_left_position < self.elements and child_right_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_left_position]
UpperCamelCase , UpperCamelCase = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(lowerCamelCase_ , lowerCamelCase_ )
return self._bubble_down(lowerCamelCase_ )
if child_left_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(lowerCamelCase_ , lowerCamelCase_ )
return self._bubble_down(lowerCamelCase_ )
else:
return None
if child_right_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(lowerCamelCase_ , lowerCamelCase_ )
return self._bubble_down(lowerCamelCase_ )
return None
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
UpperCamelCase = self.heap[nodea_pos][0]
UpperCamelCase = self.heap[nodea_pos][0]
UpperCamelCase , UpperCamelCase = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
UpperCamelCase = nodea_pos
UpperCamelCase = nodea_pos
class SCREAMING_SNAKE_CASE_ ( Generic[T] ):
def __init__( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = {}
UpperCamelCase = 0
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return str(self.connections )
def __len__( self : int ):
"""simple docstring"""
return self.nodes
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : T ):
"""simple docstring"""
if node not in self.connections:
UpperCamelCase = {}
self.nodes += 1
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : T , lowerCamelCase_ : T , lowerCamelCase_ : int ):
"""simple docstring"""
self.add_node(lowerCamelCase_ )
self.add_node(lowerCamelCase_ )
UpperCamelCase = weight
UpperCamelCase = weight
def lowercase( UpperCamelCase_ , ) -> tuple[dict[T, int], dict[T, T | None]]:
'''simple docstring'''
UpperCamelCase = {node: maxsize for node in graph.connections}
UpperCamelCase = {node: None for node in graph.connections}
UpperCamelCase = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(UpperCamelCase_ , UpperCamelCase_ )
if priority_queue.is_empty():
return dist, parent
# initialization
UpperCamelCase = priority_queue.extract_min()
UpperCamelCase = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(UpperCamelCase_ , dist[neighbour] )
UpperCamelCase = node
# running prim's algorithm
while not priority_queue.is_empty():
UpperCamelCase = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(UpperCamelCase_ , dist[neighbour] )
UpperCamelCase = node
return dist, parent
| 359 | def lowercase( UpperCamelCase_ ) -> list[list]:
'''simple docstring'''
UpperCamelCase = current_set.copy()
for row_index, row in enumerate(UpperCamelCase_ ):
UpperCamelCase = row[0]
for column_index, column in enumerate(UpperCamelCase_ ):
if magnitude == 0:
UpperCamelCase = column
continue
UpperCamelCase = column / magnitude
# Subtract to cancel term
UpperCamelCase = current_set[0]
UpperCamelCase = [first_row]
UpperCamelCase = current_set[1::]
for row in current_set:
UpperCamelCase = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(UpperCamelCase_ )
continue
for column_index in range(len(UpperCamelCase_ ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(UpperCamelCase_ )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
UpperCamelCase = final_set[0]
UpperCamelCase = []
UpperCamelCase = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
UpperCamelCase = simplify(UpperCamelCase_ )
for i in range(len(UpperCamelCase_ ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , UpperCamelCase_ )
UpperCamelCase = resultant
return final_set
def lowercase( UpperCamelCase_ ) -> list:
'''simple docstring'''
if len(UpperCamelCase_ ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
UpperCamelCase = len(UpperCamelCase_ ) + 1
if any(len(UpperCamelCase_ ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(UpperCamelCase_ , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(UpperCamelCase_ ) == 1:
return [equations[0][-1] / equations[0][0]]
UpperCamelCase = equations.copy()
if any(0 in row for row in data_set ):
UpperCamelCase = data_set.copy()
UpperCamelCase = []
for row_index, row in enumerate(UpperCamelCase_ ):
if 0 not in row:
UpperCamelCase = data_set.pop(UpperCamelCase_ )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , UpperCamelCase_ )
UpperCamelCase = data_set.copy()
UpperCamelCase = simplify(UpperCamelCase_ )
UpperCamelCase = simplified[::-1]
UpperCamelCase = []
for row in simplified:
UpperCamelCase = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
UpperCamelCase = row.copy()[: len(UpperCamelCase_ ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(UpperCamelCase_ ) == 0:
solutions.append(0 )
continue
UpperCamelCase = temp_row[1::]
UpperCamelCase = temp_row[::-1]
for column_index, column in enumerate(UpperCamelCase_ ):
current_solution -= column * solutions[column_index]
solutions.append(UpperCamelCase_ )
UpperCamelCase = []
for item in solutions:
final.append(float(round(UpperCamelCase_ , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
_SCREAMING_SNAKE_CASE = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 165 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = 42
class lowerCAmelCase ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase : Dict=3 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : Optional[Any]=("DownEncoderBlock2D",) , UpperCAmelCase : List[str]=(64,) , UpperCAmelCase : Any=2 , UpperCAmelCase : List[Any]=32 , UpperCAmelCase : List[str]="silu" , UpperCAmelCase : Tuple=True , ) -> Tuple:
super().__init__()
lowerCamelCase__ : Optional[Any] = layers_per_block
lowerCamelCase__ : Dict = torch.nn.Convad(
UpperCAmelCase , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : Union[str, Any] = nn.ModuleList([] )
# down
lowerCamelCase__ : Tuple = block_out_channels[0]
for i, down_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : List[Any] = output_channel
lowerCamelCase__ : Dict = block_out_channels[i]
lowerCamelCase__ : int = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : int = get_down_block(
UpperCAmelCase , num_layers=self.layers_per_block , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=UpperCAmelCase , resnet_groups=UpperCAmelCase , attention_head_dim=UpperCAmelCase , temb_channels=UpperCAmelCase , )
self.down_blocks.append(UpperCAmelCase )
# mid
lowerCamelCase__ : Tuple = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift='default' , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase , temb_channels=UpperCAmelCase , )
# out
lowerCamelCase__ : Optional[Any] = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=UpperCAmelCase , eps=1e-6 )
lowerCamelCase__ : List[Any] = nn.SiLU()
lowerCamelCase__ : List[str] = 2 * out_channels if double_z else out_channels
lowerCamelCase__ : Any = nn.Convad(block_out_channels[-1] , UpperCAmelCase , 3 , padding=1 )
lowerCamelCase__ : List[Any] = False
def A_ ( self : Dict , UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
lowerCamelCase__ : Dict = x
lowerCamelCase__ : Dict = self.conv_in(UpperCAmelCase )
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCAmelCase : Dict ):
def custom_forward(*UpperCAmelCase : Any ):
return module(*UpperCAmelCase )
return custom_forward
# down
if is_torch_version('>=' , '1.11.0' ):
for down_block in self.down_blocks:
lowerCamelCase__ : Optional[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , use_reentrant=UpperCAmelCase )
# middle
lowerCamelCase__ : Tuple = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , use_reentrant=UpperCAmelCase )
else:
for down_block in self.down_blocks:
lowerCamelCase__ : str = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase ) , UpperCAmelCase )
# middle
lowerCamelCase__ : Dict = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , UpperCAmelCase )
else:
# down
for down_block in self.down_blocks:
lowerCamelCase__ : Optional[Any] = down_block(UpperCAmelCase )
# middle
lowerCamelCase__ : List[str] = self.mid_block(UpperCAmelCase )
# post-process
lowerCamelCase__ : str = self.conv_norm_out(UpperCAmelCase )
lowerCamelCase__ : List[Any] = self.conv_act(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = self.conv_out(UpperCAmelCase )
return sample
class lowerCAmelCase ( nn.Module ):
def __init__( self : Any , UpperCAmelCase : Optional[int]=3 , UpperCAmelCase : List[Any]=3 , UpperCAmelCase : int=("UpDecoderBlock2D",) , UpperCAmelCase : Optional[Any]=(64,) , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=32 , UpperCAmelCase : int="silu" , UpperCAmelCase : List[str]="group" , ) -> Any:
super().__init__()
lowerCamelCase__ : List[str] = layers_per_block
lowerCamelCase__ : Optional[Any] = nn.Convad(
UpperCAmelCase , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
lowerCamelCase__ : Dict = None
lowerCamelCase__ : List[str] = nn.ModuleList([] )
lowerCamelCase__ : Dict = in_channels if norm_type == 'spatial' else None
# mid
lowerCamelCase__ : List[Any] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift='default' if norm_type == 'group' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase , temb_channels=UpperCAmelCase , )
# up
lowerCamelCase__ : Tuple = list(reversed(UpperCAmelCase ) )
lowerCamelCase__ : Dict = reversed_block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : Dict = output_channel
lowerCamelCase__ : List[Any] = reversed_block_out_channels[i]
lowerCamelCase__ : Union[str, Any] = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : Union[str, Any] = get_up_block(
UpperCAmelCase , num_layers=self.layers_per_block + 1 , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , prev_output_channel=UpperCAmelCase , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , resnet_groups=UpperCAmelCase , attention_head_dim=UpperCAmelCase , temb_channels=UpperCAmelCase , resnet_time_scale_shift=UpperCAmelCase , )
self.up_blocks.append(UpperCAmelCase )
lowerCamelCase__ : str = output_channel
# out
if norm_type == "spatial":
lowerCamelCase__ : Any = SpatialNorm(block_out_channels[0] , UpperCAmelCase )
else:
lowerCamelCase__ : Optional[Any] = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=UpperCAmelCase , eps=1e-6 )
lowerCamelCase__ : Tuple = nn.SiLU()
lowerCamelCase__ : Union[str, Any] = nn.Convad(block_out_channels[0] , UpperCAmelCase , 3 , padding=1 )
lowerCamelCase__ : Dict = False
def A_ ( self : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Any=None ) -> List[str]:
lowerCamelCase__ : Any = z
lowerCamelCase__ : Union[str, Any] = self.conv_in(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCAmelCase : Any ):
def custom_forward(*UpperCAmelCase : Any ):
return module(*UpperCAmelCase )
return custom_forward
if is_torch_version('>=' , '1.11.0' ):
# middle
lowerCamelCase__ : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , UpperCAmelCase , use_reentrant=UpperCAmelCase )
lowerCamelCase__ : Tuple = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowerCamelCase__ : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , UpperCAmelCase , use_reentrant=UpperCAmelCase )
else:
# middle
lowerCamelCase__ : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Dict = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowerCamelCase__ : Tuple = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , UpperCAmelCase )
else:
# middle
lowerCamelCase__ : Any = self.mid_block(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Optional[int] = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowerCamelCase__ : List[Any] = up_block(UpperCAmelCase , UpperCAmelCase )
# post-process
if latent_embeds is None:
lowerCamelCase__ : str = self.conv_norm_out(UpperCAmelCase )
else:
lowerCamelCase__ : Optional[Any] = self.conv_norm_out(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : List[str] = self.conv_act(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = self.conv_out(UpperCAmelCase )
return sample
class lowerCAmelCase ( nn.Module ):
def __init__( self : Dict , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=None , UpperCAmelCase : Optional[Any]="random" , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : List[Any]=True ) -> Optional[int]:
super().__init__()
lowerCamelCase__ : int = n_e
lowerCamelCase__ : Any = vq_embed_dim
lowerCamelCase__ : Optional[int] = beta
lowerCamelCase__ : Optional[Any] = legacy
lowerCamelCase__ : Any = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
lowerCamelCase__ : Any = remap
if self.remap is not None:
self.register_buffer('used' , torch.tensor(np.load(self.remap ) ) )
lowerCamelCase__ : str = self.used.shape[0]
lowerCamelCase__ : Any = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
lowerCamelCase__ : List[Any] = self.re_embed
lowerCamelCase__ : List[Any] = self.re_embed + 1
print(
F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
F"""Using {self.unknown_index} for unknown indices.""" )
else:
lowerCamelCase__ : List[Any] = n_e
lowerCamelCase__ : Optional[Any] = sane_index_shape
def A_ ( self : Union[str, Any] , UpperCAmelCase : Any ) -> Dict:
lowerCamelCase__ : Any = inds.shape
assert len(UpperCAmelCase ) > 1
lowerCamelCase__ : str = inds.reshape(ishape[0] , -1 )
lowerCamelCase__ : str = self.used.to(UpperCAmelCase )
lowerCamelCase__ : Dict = (inds[:, :, None] == used[None, None, ...]).long()
lowerCamelCase__ : Optional[int] = match.argmax(-1 )
lowerCamelCase__ : Dict = match.sum(2 ) < 1
if self.unknown_index == "random":
lowerCamelCase__ : Optional[int] = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
lowerCamelCase__ : int = self.unknown_index
return new.reshape(UpperCAmelCase )
def A_ ( self : List[str] , UpperCAmelCase : Tuple ) -> Optional[int]:
lowerCamelCase__ : List[Any] = inds.shape
assert len(UpperCAmelCase ) > 1
lowerCamelCase__ : List[Any] = inds.reshape(ishape[0] , -1 )
lowerCamelCase__ : str = self.used.to(UpperCAmelCase )
if self.re_embed > self.used.shape[0]: # extra token
lowerCamelCase__ : Any = 0 # simply set to zero
lowerCamelCase__ : Any = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , UpperCAmelCase )
return back.reshape(UpperCAmelCase )
def A_ ( self : Tuple , UpperCAmelCase : Union[str, Any] ) -> Optional[int]:
# reshape z -> (batch, height, width, channel) and flatten
lowerCamelCase__ : Tuple = z.permute(0 , 2 , 3 , 1 ).contiguous()
lowerCamelCase__ : List[Any] = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
lowerCamelCase__ : List[str] = torch.argmin(torch.cdist(UpperCAmelCase , self.embedding.weight ) , dim=1 )
lowerCamelCase__ : Optional[Any] = self.embedding(UpperCAmelCase ).view(z.shape )
lowerCamelCase__ : int = None
lowerCamelCase__ : Union[str, Any] = None
# compute loss for embedding
if not self.legacy:
lowerCamelCase__ : Dict = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
lowerCamelCase__ : List[str] = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
lowerCamelCase__ : Union[str, Any] = z + (z_q - z).detach()
# reshape back to match original input shape
lowerCamelCase__ : Any = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
lowerCamelCase__ : int = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
lowerCamelCase__ : Union[str, Any] = self.remap_to_used(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
lowerCamelCase__ : Any = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def A_ ( self : int , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] ) -> List[Any]:
# shape specifying (batch, height, width, channel)
if self.remap is not None:
lowerCamelCase__ : str = indices.reshape(shape[0] , -1 ) # add batch axis
lowerCamelCase__ : Optional[Any] = self.unmap_to_all(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
lowerCamelCase__ : str = self.embedding(UpperCAmelCase )
if shape is not None:
lowerCamelCase__ : List[str] = z_q.view(UpperCAmelCase )
# reshape back to match original input shape
lowerCamelCase__ : Union[str, Any] = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : Any , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> List[str]:
lowerCamelCase__ : List[str] = parameters
lowerCamelCase__ , lowerCamelCase__ : List[str] = torch.chunk(UpperCAmelCase , 2 , dim=1 )
lowerCamelCase__ : Union[str, Any] = torch.clamp(self.logvar , -3_0.0 , 2_0.0 )
lowerCamelCase__ : List[Any] = deterministic
lowerCamelCase__ : Optional[int] = torch.exp(0.5 * self.logvar )
lowerCamelCase__ : Optional[Any] = torch.exp(self.logvar )
if self.deterministic:
lowerCamelCase__ : str = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def A_ ( self : List[str] , UpperCAmelCase : Optional[torch.Generator] = None ) -> torch.FloatTensor:
# make sure sample is on the same device as the parameters and has same dtype
lowerCamelCase__ : Any = randn_tensor(
self.mean.shape , generator=UpperCAmelCase , device=self.parameters.device , dtype=self.parameters.dtype )
lowerCamelCase__ : Optional[int] = self.mean + self.std * sample
return x
def A_ ( self : Dict , UpperCAmelCase : Optional[Any]=None ) -> Optional[Any]:
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def A_ ( self : str , UpperCAmelCase : int , UpperCAmelCase : List[Any]=[1, 2, 3] ) -> List[str]:
if self.deterministic:
return torch.Tensor([0.0] )
lowerCamelCase__ : int = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=UpperCAmelCase )
def A_ ( self : Optional[int] ) -> List[str]:
return self.mean
| 50 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = 42
class lowerCAmelCase ( __UpperCamelCase, __UpperCamelCase ):
@register_to_config
def __init__( self : List[str] , UpperCAmelCase : int = 65536 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 0 , UpperCAmelCase : str = "fourier" , UpperCAmelCase : bool = True , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , UpperCAmelCase : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , UpperCAmelCase : Tuple[str] = "UNetMidBlock1D" , UpperCAmelCase : str = None , UpperCAmelCase : Tuple[int] = (32, 32, 64) , UpperCAmelCase : str = None , UpperCAmelCase : int = 8 , UpperCAmelCase : int = 1 , UpperCAmelCase : bool = False , ) -> List[Any]:
super().__init__()
lowerCamelCase__ : Optional[int] = sample_size
# time
if time_embedding_type == "fourier":
lowerCamelCase__ : Optional[Any] = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=UpperCAmelCase , log=UpperCAmelCase , flip_sin_to_cos=UpperCAmelCase )
lowerCamelCase__ : Any = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
lowerCamelCase__ : List[Any] = Timesteps(
block_out_channels[0] , flip_sin_to_cos=UpperCAmelCase , downscale_freq_shift=UpperCAmelCase )
lowerCamelCase__ : Dict = block_out_channels[0]
if use_timestep_embedding:
lowerCamelCase__ : str = block_out_channels[0] * 4
lowerCamelCase__ : List[Any] = TimestepEmbedding(
in_channels=UpperCAmelCase , time_embed_dim=UpperCAmelCase , act_fn=UpperCAmelCase , out_dim=block_out_channels[0] , )
lowerCamelCase__ : Any = nn.ModuleList([] )
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : List[str] = nn.ModuleList([] )
lowerCamelCase__ : Optional[int] = None
# down
lowerCamelCase__ : Optional[int] = in_channels
for i, down_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : Union[str, Any] = output_channel
lowerCamelCase__ : Tuple = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
lowerCamelCase__ : Union[str, Any] = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : Optional[int] = get_down_block(
UpperCAmelCase , num_layers=UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(UpperCAmelCase )
# mid
lowerCamelCase__ : Optional[int] = get_mid_block(
UpperCAmelCase , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=UpperCAmelCase , add_downsample=UpperCAmelCase , )
# up
lowerCamelCase__ : Optional[int] = list(reversed(UpperCAmelCase ) )
lowerCamelCase__ : Optional[int] = reversed_block_out_channels[0]
if out_block_type is None:
lowerCamelCase__ : List[str] = out_channels
else:
lowerCamelCase__ : Any = block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : Tuple = output_channel
lowerCamelCase__ : Union[str, Any] = (
reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase ) - 1 else final_upsample_channels
)
lowerCamelCase__ : List[str] = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : Dict = get_up_block(
UpperCAmelCase , num_layers=UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(UpperCAmelCase )
lowerCamelCase__ : int = output_channel
# out
lowerCamelCase__ : int = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
lowerCamelCase__ : List[Any] = get_out_block(
out_block_type=UpperCAmelCase , num_groups_out=UpperCAmelCase , embed_dim=block_out_channels[0] , out_channels=UpperCAmelCase , act_fn=UpperCAmelCase , fc_dim=block_out_channels[-1] // 4 , )
def A_ ( self : List[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Union[torch.Tensor, float, int] , UpperCAmelCase : bool = True , ) -> Union[UNetaDOutput, Tuple]:
lowerCamelCase__ : Optional[Any] = timestep
if not torch.is_tensor(UpperCAmelCase ):
lowerCamelCase__ : Optional[int] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(UpperCAmelCase ) and len(timesteps.shape ) == 0:
lowerCamelCase__ : List[str] = timesteps[None].to(sample.device )
lowerCamelCase__ : Optional[int] = self.time_proj(UpperCAmelCase )
if self.config.use_timestep_embedding:
lowerCamelCase__ : str = self.time_mlp(UpperCAmelCase )
else:
lowerCamelCase__ : List[str] = timestep_embed[..., None]
lowerCamelCase__ : str = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
lowerCamelCase__ : str = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
lowerCamelCase__ : str = ()
for downsample_block in self.down_blocks:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = downsample_block(hidden_states=UpperCAmelCase , temb=UpperCAmelCase )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
lowerCamelCase__ : Optional[Any] = self.mid_block(UpperCAmelCase , UpperCAmelCase )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
lowerCamelCase__ : Dict = down_block_res_samples[-1:]
lowerCamelCase__ : Optional[Any] = down_block_res_samples[:-1]
lowerCamelCase__ : Any = upsample_block(UpperCAmelCase , res_hidden_states_tuple=UpperCAmelCase , temb=UpperCAmelCase )
# 5. post-process
if self.out_block:
lowerCamelCase__ : Any = self.out_block(UpperCAmelCase , UpperCAmelCase )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=UpperCAmelCase )
| 50 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''JukeboxVQVAEConfig''',
],
'''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ :Any = [
'''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''JukeboxModel''',
'''JukeboxPreTrainedModel''',
'''JukeboxVQVAE''',
'''JukeboxPrior''',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
A_ :Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 370 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ :List[str] = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
A_ :int = logging.get_logger(__name__)
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] ="""mask2former"""
UpperCamelCase__ : Tuple =["""swin"""]
UpperCamelCase__ : Dict ={"""hidden_size""": """hidden_dim"""}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 1024 , lowerCamelCase__ = "relu" , lowerCamelCase__ = 6 , lowerCamelCase__ = 10 , lowerCamelCase__ = 8 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 2048 , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 4 , lowerCamelCase__ = 255 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 12544 , lowerCamelCase__ = 3.0 , lowerCamelCase__ = 0.75 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = True , lowerCamelCase__ = [4, 8, 16, 32] , lowerCamelCase__ = None , **lowerCamelCase__ , ):
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__UpperCamelCase : Optional[int] =CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__UpperCamelCase : List[str] =backbone_config.pop('model_type' )
__UpperCamelCase : str =CONFIG_MAPPING[backbone_model_type]
__UpperCamelCase : List[Any] =config_class.from_dict(lowerCamelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
f'Supported model types: {",".join(self.backbones_supported )}' )
__UpperCamelCase : Dict =backbone_config
__UpperCamelCase : Optional[int] =feature_size
__UpperCamelCase : Union[str, Any] =mask_feature_size
__UpperCamelCase : Tuple =hidden_dim
__UpperCamelCase : Optional[int] =encoder_feedforward_dim
__UpperCamelCase : Optional[int] =activation_function
__UpperCamelCase : Dict =encoder_layers
__UpperCamelCase : List[Any] =decoder_layers
__UpperCamelCase : int =num_attention_heads
__UpperCamelCase : Optional[Any] =dropout
__UpperCamelCase : int =dim_feedforward
__UpperCamelCase : Any =pre_norm
__UpperCamelCase : Union[str, Any] =enforce_input_projection
__UpperCamelCase : str =common_stride
__UpperCamelCase : List[str] =ignore_value
__UpperCamelCase : Optional[int] =num_queries
__UpperCamelCase : Any =no_object_weight
__UpperCamelCase : int =class_weight
__UpperCamelCase : str =mask_weight
__UpperCamelCase : Dict =dice_weight
__UpperCamelCase : str =train_num_points
__UpperCamelCase : str =oversample_ratio
__UpperCamelCase : int =importance_sample_ratio
__UpperCamelCase : List[str] =init_std
__UpperCamelCase : Union[str, Any] =init_xavier_std
__UpperCamelCase : Any =use_auxiliary_loss
__UpperCamelCase : Tuple =feature_strides
__UpperCamelCase : Dict =output_auxiliary_logits
__UpperCamelCase : Union[str, Any] =decoder_layers
super().__init__(**lowerCamelCase__ )
@classmethod
def __lowercase ( cls , lowerCamelCase__ , **lowerCamelCase__ ):
"""simple docstring"""
return cls(
backbone_config=lowerCamelCase__ , **lowerCamelCase__ , )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =copy.deepcopy(self.__dict__ )
__UpperCamelCase : List[Any] =self.backbone_config.to_dict()
__UpperCamelCase : Union[str, Any] =self.__class__.model_type
return output
| 245 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=a_ ):
_lowerCamelCase :str = ["note_seq"]
def __init__( self : Optional[Any] , *UpperCamelCase : List[Any] , **UpperCamelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""note_seq"""] )
@classmethod
def _lowerCAmelCase ( cls : Optional[Any] , *UpperCamelCase : str , **UpperCamelCase : str ) -> str:
"""simple docstring"""
requires_backends(cls , ["""note_seq"""] )
@classmethod
def _lowerCAmelCase ( cls : int , *UpperCamelCase : Optional[Any] , **UpperCamelCase : List[str] ) -> int:
"""simple docstring"""
requires_backends(cls , ["""note_seq"""] )
| 242 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[Any] = "levit"
def __init__( self : List[Any] , UpperCamelCase : List[str]=2_24 , UpperCamelCase : Any=3 , UpperCamelCase : Optional[Any]=3 , UpperCamelCase : Union[str, Any]=2 , UpperCamelCase : Any=1 , UpperCamelCase : int=16 , UpperCamelCase : List[str]=[1_28, 2_56, 3_84] , UpperCamelCase : Optional[Any]=[4, 8, 12] , UpperCamelCase : Optional[int]=[4, 4, 4] , UpperCamelCase : str=[16, 16, 16] , UpperCamelCase : Tuple=0 , UpperCamelCase : List[str]=[2, 2, 2] , UpperCamelCase : Optional[int]=[2, 2, 2] , UpperCamelCase : Optional[int]=0.02 , **UpperCamelCase : Dict , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : int = image_size
lowerCAmelCase__ : Any = num_channels
lowerCAmelCase__ : int = kernel_size
lowerCAmelCase__ : Any = stride
lowerCAmelCase__ : List[str] = padding
lowerCAmelCase__ : Tuple = hidden_sizes
lowerCAmelCase__ : str = num_attention_heads
lowerCAmelCase__ : List[Any] = depths
lowerCAmelCase__ : List[str] = key_dim
lowerCAmelCase__ : List[str] = drop_path_rate
lowerCAmelCase__ : List[Any] = patch_size
lowerCAmelCase__ : Dict = attention_ratio
lowerCAmelCase__ : Tuple = mlp_ratio
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Dict = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Tuple = version.parse("1.11" )
@property
def _lowerCAmelCase ( self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowerCAmelCase ( self : List[str] ) -> float:
"""simple docstring"""
return 1E-4
| 242 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=True , _a=False , _a=False , _a=False , _a=2 , _a=99 , _a=0 , _a=32 , _a=5 , _a=4 , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.0_2 , _a=2 , _a=4 , _a="last" , _a=True , _a=None , _a=0 , ) -> Union[str, Any]:
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = seq_length
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_input_lengths
lowerCAmelCase_ = use_token_type_ids
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = gelu_activation
lowerCAmelCase_ = sinusoidal_embeddings
lowerCAmelCase_ = causal
lowerCAmelCase_ = asm
lowerCAmelCase_ = n_langs
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = n_special
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_choices
lowerCAmelCase_ = summary_type
lowerCAmelCase_ = use_proj
lowerCAmelCase_ = scope
lowerCAmelCase_ = bos_token_id
def __a ( self ) -> Dict:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ = None
if self.use_input_lengths:
lowerCAmelCase_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCAmelCase_ = None
if self.use_token_type_ids:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ = ids_tensor([self.batch_size] , 2 ).float()
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __a ( self ) -> List[str]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> List[str]:
lowerCAmelCase_ = XLMModel(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , lengths=_a , langs=_a )
lowerCAmelCase_ = model(_a , langs=_a )
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Optional[int]:
lowerCAmelCase_ = XLMWithLMHeadModel(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> List[Any]:
lowerCAmelCase_ = XLMForQuestionAnsweringSimple(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(_a , start_positions=_a , end_positions=_a )
lowerCAmelCase_ = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
lowerCAmelCase_ = XLMForQuestionAnswering(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(
_a , start_positions=_a , end_positions=_a , cls_index=_a , is_impossible=_a , p_mask=_a , )
lowerCAmelCase_ = model(
_a , start_positions=_a , end_positions=_a , cls_index=_a , is_impossible=_a , )
((lowerCAmelCase_) , ) = result_with_labels.to_tuple()
lowerCAmelCase_ = model(_a , start_positions=_a , end_positions=_a )
((lowerCAmelCase_) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> int:
lowerCAmelCase_ = XLMForSequenceClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(_a , labels=_a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Any:
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = XLMForTokenClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , attention_mask=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Tuple:
lowerCAmelCase_ = self.num_choices
lowerCAmelCase_ = XLMForMultipleChoice(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) = config_and_inputs
lowerCAmelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ (__lowercase , __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase__ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowerCamelCase__ = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __a ( self , _a , _a , _a , _a , _a ) -> Optional[int]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __a ( self , _a , _a , _a=False ) -> str:
lowerCAmelCase_ = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
lowerCAmelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
lowerCAmelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = XLMModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=_a , emb_dim=37 )
def __a ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_a )
def __a ( self ) -> Tuple:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_a )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_a )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_a )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_a )
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_a )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_a )
def __a ( self , _a , _a , _a , _a , _a , _a=False , _a=1 ) -> List[str]:
self.assertIsInstance(_a , _a )
self.assertListEqual(
[isinstance(_a , _a ) for iter_attentions in attentions] , [True] * len(_a ) )
self.assertEqual(len(_a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_a ):
# adds PAD dummy token
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_a ) )
def __a ( self , _a , _a , _a , _a , _a , _a=False , _a=1 ) -> str:
self.assertIsInstance(_a , _a )
self.assertListEqual(
[isinstance(_a , _a ) for iter_hidden_states in hidden_states] , [True] * len(_a ) , )
self.assertEqual(len(_a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_a ):
# adds PAD dummy token
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_a ) , )
pass
@slow
def __a ( self ) -> Dict:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = XLMModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __magic_name__ (unittest.TestCase ):
@slow
def __a ( self ) -> List[str]:
lowerCAmelCase_ = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(_a )
lowerCAmelCase_ = torch.tensor([[14, 447]] , dtype=torch.long , device=_a ) # the president
lowerCAmelCase_ = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
lowerCAmelCase_ = model.generate(_a , do_sample=_a )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _a )
| 22 |
import math
from collections.abc import Iterator
from itertools import takewhile
def A(__a: int ):
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(__a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A():
lowerCAmelCase_ = 2
while True:
if is_prime(__a ):
yield num
num += 1
def A(__a: int = 200_0000 ):
return sum(takewhile(lambda __a : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 22 | 1 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = RobertaPreLayerNormConfig.from_pretrained(
__A , architectures=['''RobertaPreLayerNormForMaskedLM'''])
# convert state_dict
_a = torch.load(hf_hub_download(repo_id=__A , filename='''pytorch_model.bin'''))
_a = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith('''roberta.'''):
_a = '''roberta_prelayernorm.''' + tensor_key[len('''roberta.''') :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith('''.self.LayerNorm.weight''') or tensor_key.endswith('''.self.LayerNorm.bias'''):
continue
_a = tensor_value
_a = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=__A , config=__A , state_dict=__A)
model.save_pretrained(__A)
# convert tokenizer
_a = AutoTokenizer.from_pretrained(__A)
tokenizer.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint-repo",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowercase_ = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 211 |
'''simple docstring'''
import random
def lowerCAmelCase (__A):
"""simple docstring"""
_a = num - 1
_a = 0
while s % 2 == 0:
_a = s // 2
t += 1
for _ in range(5):
_a = random.randrange(2 , num - 1)
_a = pow(__A , __A , __A)
if v != 1:
_a = 0
while v != (num - 1):
if i == t - 1:
return False
else:
_a = i + 1
_a = (v**2) % num
return True
def lowerCAmelCase (__A):
"""simple docstring"""
if num < 2:
return False
_a = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(__A)
def lowerCAmelCase (__A = 1_024):
"""simple docstring"""
while True:
_a = random.randrange(2 ** (keysize - 1) , 2 ** (keysize))
if is_prime_low_num(__A):
return num
if __name__ == "__main__":
lowercase_ = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| 211 | 1 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowercase__ : Dict = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'output.dense',
'attention.self_mask': 'attn_mask',
'mlp.fc1': 'intermediate.dense',
'mlp.fc2': 'output.dense',
'norm1': 'layernorm_before',
'norm2': 'layernorm_after',
'bn0': 'batch_norm',
}
lowercase__ : Optional[Any] = AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')
def a__ ( lowercase : List[str], lowercase : Any=False ) -> Tuple:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = create_model(
'''HTSAT-tiny''', '''roberta''', lowercase, precision='''fp32''', device='''cuda:0''' if torch.cuda.is_available() else '''cpu''', enable_fusion=lowercase, fusion_type='''aff_2d''' if enable_fusion else None, )
return model, model_cfg
def a__ ( lowercase : Tuple ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = {}
_UpperCamelCase = r'''.*sequential.(\d+).*'''
_UpperCamelCase = r'''.*_projection.(\d+).*'''
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
_UpperCamelCase = key.replace(lowercase, lowercase )
if re.match(lowercase, lowercase ):
# replace sequential layers with list
_UpperCamelCase = re.match(lowercase, lowercase ).group(1 )
_UpperCamelCase = key.replace(F"""sequential.{sequential_layer}.""", F"""layers.{int(lowercase )//3}.linear.""" )
elif re.match(lowercase, lowercase ):
_UpperCamelCase = int(re.match(lowercase, lowercase ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
_UpperCamelCase = 1 if projecton_layer == 0 else 2
_UpperCamelCase = key.replace(F"""_projection.{projecton_layer}.""", F"""_projection.linear{transformers_projection_layer}.""" )
if "audio" and "qkv" in key:
# split qkv into query key and value
_UpperCamelCase = value
_UpperCamelCase = mixed_qkv.size(0 ) // 3
_UpperCamelCase = mixed_qkv[:qkv_dim]
_UpperCamelCase = mixed_qkv[qkv_dim : qkv_dim * 2]
_UpperCamelCase = mixed_qkv[qkv_dim * 2 :]
_UpperCamelCase = query_layer
_UpperCamelCase = key_layer
_UpperCamelCase = value_layer
else:
_UpperCamelCase = value
return model_state_dict
def a__ ( lowercase : int, lowercase : Dict, lowercase : Any, lowercase : Union[str, Any]=False ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = init_clap(lowercase, enable_fusion=lowercase )
clap_model.eval()
_UpperCamelCase = clap_model.state_dict()
_UpperCamelCase = rename_state_dict(lowercase )
_UpperCamelCase = ClapConfig()
_UpperCamelCase = enable_fusion
_UpperCamelCase = ClapModel(lowercase )
# ignore the spectrogram embedding layer
model.load_state_dict(lowercase, strict=lowercase )
model.save_pretrained(lowercase )
transformers_config.save_pretrained(lowercase )
if __name__ == "__main__":
lowercase__ : List[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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
lowercase__ : int = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 287 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(lowercase ):
for j in range(lowercase ):
_UpperCamelCase = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
lowercase__ : Optional[int] = imread('image_data/lena.jpg', 1)
# convert to its negative
lowercase__ : Union[str, Any] = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 287 | 1 |
def UpperCAmelCase__ ( _A : list ):
'''simple docstring'''
if len(_A ) <= 1:
return lst
a__ =1
while i < len(_A ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a__, a__ =lst[i], lst[i - 1]
i -= 1
if i == 0:
a__ =1
return lst
if __name__ == "__main__":
lowerCamelCase = input('''Enter numbers separated by a comma:\n''').strip()
lowerCamelCase = [int(item) for item in user_input.split(''',''')]
print(gnome_sort(unsorted))
| 188 |
from __future__ import annotations
def UpperCAmelCase__ ( _A : float , _A : float , _A : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 188 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCAmelCase :
def __init__( self: Tuple , UpperCAmelCase_: Optional[int] , UpperCAmelCase_: Union[str, Any]=3 , UpperCAmelCase_: Optional[int]=32 , UpperCAmelCase_: Tuple=3 , UpperCAmelCase_: int=10 , UpperCAmelCase_: int=[10, 20, 30, 40] , UpperCAmelCase_: Union[str, Any]=[1, 1, 2, 1] , UpperCAmelCase_: Dict=True , UpperCAmelCase_: Dict=True , UpperCAmelCase_: Optional[Any]="relu" , UpperCAmelCase_: int=3 , UpperCAmelCase_: List[str]=None , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = embeddings_size
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = len(UpperCAmelCase_ )
def UpperCamelCase ( self: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def UpperCamelCase ( self: int , UpperCAmelCase_: int , UpperCAmelCase_: Optional[Any] , UpperCAmelCase_: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = RegNetModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCamelCase ( self: int , UpperCAmelCase_: List[Any] , UpperCAmelCase_: Any , UpperCAmelCase_: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = RegNetForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase (_UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Dict =(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
__snake_case : Tuple =(
{"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
if is_torch_available()
else {}
)
__snake_case : Optional[int] =False
__snake_case : Optional[int] =False
__snake_case : int =False
__snake_case : Optional[Any] =False
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = RegNetModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=UpperCAmelCase_ , has_text_modality=UpperCAmelCase_ )
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_ )
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(config=UpperCAmelCase_ )
for name, module in model.named_modules():
if isinstance(UpperCAmelCase_ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase_: Optional[int] , UpperCAmelCase_: int , UpperCAmelCase_: Union[str, Any] ):
_SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_SCREAMING_SNAKE_CASE = layer_type
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def UpperCamelCase ( self: int ):
'''simple docstring'''
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = RegNetModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCAmelCase (unittest.TestCase ):
@cached_property
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=UpperCAmelCase_ , return_tensors="""pt""" ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**UpperCAmelCase_ )
# verify the logits
_SCREAMING_SNAKE_CASE = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = torch.tensor([-0.41_80, -1.50_51, -3.48_36] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1E-4 ) )
| 367 |
UpperCamelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCamelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case__ ,snake_case__ ,snake_case__ )
order.append(snake_case__ )
return order
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case__ ,snake_case__ ,snake_case__ )
return component
def __lowerCamelCase ( snake_case__ ) -> list[list[int]]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ ) * [False]
_SCREAMING_SNAKE_CASE = {vert: [] for vert in range(len(snake_case__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case__ )
_SCREAMING_SNAKE_CASE = []
for i, was_visited in enumerate(snake_case__ ):
if not was_visited:
order += topology_sort(snake_case__ ,snake_case__ ,snake_case__ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = len(snake_case__ ) * [False]
for i in range(len(snake_case__ ) ):
_SCREAMING_SNAKE_CASE = order[len(snake_case__ ) - i - 1]
if not visited[vert]:
_SCREAMING_SNAKE_CASE = find_components(snake_case__ ,snake_case__ ,snake_case__ )
components_list.append(snake_case__ )
return components_list
| 125 | 0 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ ):
if not input_list:
return []
_lowerCamelCase : Dict = [input_list.count(__lowercase ) for value in input_list]
_lowerCamelCase : int = max(__lowercase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(__lowercase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A ( self : int ):
'''simple docstring'''
_snake_case = FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base' )
_snake_case = AutoTokenizer.from_pretrained('xlm-roberta-base' )
_snake_case = 'The dog is cute and lives in the garden house'
_snake_case = jnp.array([tokenizer.encode(lowercase )] )
_snake_case = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
_snake_case = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
_snake_case = model(lowercase )['last_hidden_state']
self.assertEqual(output.shape , lowercase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , lowercase , atol=1E-3 ) ) | 282 | 0 |
'''simple docstring'''
def UpperCAmelCase ( a_ , a_ = " " ) -> list:
"""simple docstring"""
A_ : List[str] = []
A_ : List[str] = 0
for index, char in enumerate(__lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
A_ : Union[str, Any] = index + 1
elif index + 1 == len(__lowerCAmelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 362 |
'''simple docstring'''
UpperCamelCase__ : int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def UpperCAmelCase ( ) -> int:
"""simple docstring"""
return sum(
number
for number in range(1_0_0_0 , 1_0_0_0_0_0_0 )
if number == digits_fifth_powers_sum(a_ ) )
if __name__ == "__main__":
print(solution())
| 164 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {'''vocab_file''': '''spiece.model'''}
__lowerCAmelCase = {
'''vocab_file''': {
'''bert_for_seq_generation''': (
'''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
),
}
}
__lowerCAmelCase = {'''bert_for_seq_generation''': 512}
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : Optional[int] = VOCAB_FILES_NAMES
lowerCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : List[int] = []
lowerCAmelCase : Optional[Any] = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[int]="<s>" ,_UpperCAmelCase : Optional[Any]="</s>" ,_UpperCAmelCase : Optional[Any]="<unk>" ,_UpperCAmelCase : Dict="<pad>" ,_UpperCAmelCase : str="<::::>" ,_UpperCAmelCase : Optional[Dict[str, Any]] = None ,**_UpperCAmelCase : Any ,):
_a : int = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=_UpperCAmelCase ,eos_token=_UpperCAmelCase ,unk_token=_UpperCAmelCase ,pad_token=_UpperCAmelCase ,sep_token=_UpperCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**_UpperCAmelCase ,)
_a : Dict = vocab_file
_a : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCAmelCase )
@property
def __lowercase ( self : Tuple ):
return self.sp_model.get_piece_size()
def __lowercase ( self : str ):
_a : List[Any] = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
_a : Tuple = self.__dict__.copy()
_a : str = None
return state
def __setstate__( self : Optional[Any] ,_UpperCAmelCase : Optional[int] ):
_a : Optional[int] = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
_a : Dict = {}
_a : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowercase ( self : Any ,_UpperCAmelCase : str ):
return self.sp_model.encode(_UpperCAmelCase ,out_type=_UpperCAmelCase )
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : Tuple ):
return self.sp_model.piece_to_id(_UpperCAmelCase )
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : str ):
_a : Union[str, Any] = self.sp_model.IdToPiece(_UpperCAmelCase )
return token
def __lowercase ( self : str ,_UpperCAmelCase : Tuple ):
_a : Any = []
_a : Any = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_UpperCAmelCase ) + token
_a : Any = []
else:
current_sub_tokens.append(_UpperCAmelCase )
out_string += self.sp_model.decode(_UpperCAmelCase )
return out_string.strip()
def __lowercase ( self : Any ,_UpperCAmelCase : str ,_UpperCAmelCase : Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[Any] = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase ,'wb' ) as fi:
_a : Tuple = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,)
| 89 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
a_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase )
if weight_type is not None:
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,_UpperCamelCase )
if "weight_g" in name:
__lowerCamelCase = '''weight_g'''
elif "weight_v" in name:
__lowerCamelCase = '''weight_v'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase = '''weight'''
else:
__lowerCamelCase = None
set_recursively(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = full_name.split('''conv_layers.''' )[-1]
__lowerCamelCase = name.split('''.''' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : List[Any]=True ):
if config_path is not None:
__lowerCamelCase = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatConfig()
__lowerCamelCase = ''''''
if is_finetuned:
__lowerCamelCase = UniSpeechSatForCTC(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatForPreTraining(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowerCamelCase = model[0].eval()
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 330 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowerCAmelCase ( lowercase ,lowercase ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Any = IFImgaImgSuperResolutionPipeline
__UpperCAmelCase : str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
__UpperCAmelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"} )
__UpperCAmelCase : Optional[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def _lowercase ( self : Tuple ):
return self._get_superresolution_dummy_components()
def _lowercase ( self : Optional[Any], UpperCAmelCase__ : Union[str, Any], UpperCAmelCase__ : List[Any]=0 ):
if str(UpperCAmelCase__ ).startswith("mps" ):
__lowercase = torch.manual_seed(UpperCAmelCase__ )
else:
__lowercase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
__lowercase = floats_tensor((1, 3, 3_2, 3_2), rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
__lowercase = floats_tensor((1, 3, 1_6, 1_6), rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
__lowercase = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", )
def _lowercase ( self : Optional[Any] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _lowercase ( self : Union[str, Any] ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA" )
def _lowercase ( self : List[str] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _lowercase ( self : List[str] ):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _lowercase ( self : Optional[int] ):
self._test_save_load_local()
def _lowercase ( self : Union[str, Any] ):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2, )
| 357 |
"""simple docstring"""
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
_a = pytest.mark.integration
_a = {'comet'}
_a = importlib.util.find_spec('fairseq') is not None
_a = {'code_eval'}
_a = os.name == 'nt'
_a = {'bertscore', 'frugalscore', 'perplexity'}
_a = importlib.util.find_spec('transformers') is not None
def _A ( UpperCamelCase_ : Dict) -> Any:
'''simple docstring'''
@wraps(UpperCamelCase_)
def wrapper(self : Dict, UpperCamelCase_ : Dict):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"")
else:
test_case(self, UpperCamelCase_)
return wrapper
def _A ( UpperCamelCase_ : Dict) -> int:
'''simple docstring'''
@wraps(UpperCamelCase_)
def wrapper(self : int, UpperCamelCase_ : str):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"")
else:
test_case(self, UpperCamelCase_)
return wrapper
def _A ( UpperCamelCase_ : Tuple) -> str:
'''simple docstring'''
@wraps(UpperCamelCase_)
def wrapper(self : Optional[int], UpperCamelCase_ : Optional[Any]):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"")
else:
test_case(self, UpperCamelCase_)
return wrapper
def _A ( ) -> str:
'''simple docstring'''
__lowercase = [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(
lowercase ,lowercase ,lowercase )
@local
class _lowerCAmelCase ( parameterized.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = {}
__UpperCAmelCase : Tuple = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def _lowercase ( self : Dict, UpperCAmelCase__ : int ):
__lowercase = "[...]"
__lowercase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics", UpperCAmelCase__ ) ).module_path )
__lowercase = datasets.load.import_main_class(metric_module.__name__, dataset=UpperCAmelCase__ )
# check parameters
__lowercase = 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:
__lowercase = 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 _lowercase ( self : List[Any], UpperCAmelCase__ : Optional[Any] ):
__lowercase = "[...]"
__lowercase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics", UpperCAmelCase__ ) ).module_path )
# run doctest
with self.use_local_metrics():
__lowercase = doctest.testmod(UpperCAmelCase__, verbose=UpperCAmelCase__, raise_on_error=UpperCAmelCase__ )
self.assertEqual(results.failed, 0 )
self.assertGreater(results.attempted, 1 )
@contextmanager
def _lowercase ( self : List[Any], UpperCAmelCase__ : Any, UpperCAmelCase__ : Tuple ):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCAmelCase__ ):
yield
else:
yield
@contextmanager
def _lowercase ( self : List[Any] ):
def load_local_metric(UpperCAmelCase__ : Any, *UpperCAmelCase__ : List[Any], **UpperCAmelCase__ : Any ):
return load_metric(os.path.join("metrics", UpperCAmelCase__ ), *UpperCAmelCase__, **UpperCAmelCase__ )
with patch("datasets.load_metric" ) as mock_load_metric:
__lowercase = load_local_metric
yield
@classmethod
def _lowercase ( cls : Optional[Any], UpperCAmelCase__ : List[Any] ):
def wrapper(UpperCAmelCase__ : Tuple ):
__lowercase = contextmanager(UpperCAmelCase__ )
__lowercase = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt")
def _A ( UpperCamelCase_ : Any) -> Optional[Any]:
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv", "", "") # handle pytest cli flags
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
def _lowercase ( self : Tuple, UpperCAmelCase__ : Tuple ):
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:
__lowercase = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore")
def _A ( UpperCamelCase_ : Tuple) -> int:
'''simple docstring'''
import torch
def bert_cos_score_idf(UpperCamelCase_ : Tuple, UpperCamelCase_ : str, *UpperCamelCase_ : Optional[Any], **UpperCamelCase_ : Dict):
return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase_))
# 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:
__lowercase = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet")
def _A ( UpperCamelCase_ : Tuple) -> List[Any]:
'''simple docstring'''
def load_from_checkpoint(UpperCamelCase_ : Tuple):
class _lowerCAmelCase :
"""simple docstring"""
def _lowercase ( self : str, UpperCAmelCase__ : int, *UpperCAmelCase__ : Dict, **UpperCAmelCase__ : Dict ):
assert len(UpperCAmelCase__ ) == 2
__lowercase = [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:
__lowercase = None
with patch("comet.load_from_checkpoint") as mock_load_from_checkpoint:
__lowercase = load_from_checkpoint
yield
def _A ( ) -> Tuple:
'''simple docstring'''
__lowercase = load_metric(os.path.join("metrics", "seqeval"))
__lowercase = "ERROR"
__lowercase = F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(UpperCamelCase_, match=re.escape(UpperCamelCase_)):
metric.compute(predictions=[], references=[], scheme=UpperCamelCase_)
| 144 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Union[str, Any] = """wav2vec2"""
def __init__( self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=0 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="sum" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , __lowercase=None , **__lowercase , ) -> int:
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
__UpperCamelCase :Any = hidden_size
__UpperCamelCase :int = feat_extract_norm
__UpperCamelCase :Tuple = feat_extract_activation
__UpperCamelCase :Union[str, Any] = list(__lowercase)
__UpperCamelCase :List[Any] = list(__lowercase)
__UpperCamelCase :int = list(__lowercase)
__UpperCamelCase :List[Any] = conv_bias
__UpperCamelCase :Optional[int] = num_conv_pos_embeddings
__UpperCamelCase :Dict = num_conv_pos_embedding_groups
__UpperCamelCase :Any = len(self.conv_dim)
__UpperCamelCase :List[str] = num_hidden_layers
__UpperCamelCase :int = intermediate_size
__UpperCamelCase :str = hidden_act
__UpperCamelCase :Any = num_attention_heads
__UpperCamelCase :int = hidden_dropout
__UpperCamelCase :Tuple = attention_dropout
__UpperCamelCase :List[str] = activation_dropout
__UpperCamelCase :Optional[Any] = feat_proj_dropout
__UpperCamelCase :Any = final_dropout
__UpperCamelCase :Any = layerdrop
__UpperCamelCase :str = layer_norm_eps
__UpperCamelCase :Optional[Any] = initializer_range
__UpperCamelCase :List[str] = vocab_size
__UpperCamelCase :str = do_stable_layer_norm
__UpperCamelCase :Union[str, Any] = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__UpperCamelCase :List[Any] = apply_spec_augment
__UpperCamelCase :Tuple = mask_time_prob
__UpperCamelCase :int = mask_time_length
__UpperCamelCase :Dict = mask_time_min_masks
__UpperCamelCase :str = mask_feature_prob
__UpperCamelCase :List[str] = mask_feature_length
__UpperCamelCase :Union[str, Any] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__UpperCamelCase :Optional[Any] = num_codevectors_per_group
__UpperCamelCase :List[Any] = num_codevector_groups
__UpperCamelCase :Tuple = contrastive_logits_temperature
__UpperCamelCase :Optional[int] = feat_quantizer_dropout
__UpperCamelCase :Optional[int] = num_negatives
__UpperCamelCase :List[Any] = codevector_dim
__UpperCamelCase :str = proj_codevector_dim
__UpperCamelCase :List[str] = diversity_loss_weight
# ctc loss
__UpperCamelCase :Tuple = ctc_loss_reduction
__UpperCamelCase :Tuple = ctc_zero_infinity
# adapter
__UpperCamelCase :List[str] = add_adapter
__UpperCamelCase :Tuple = adapter_kernel_size
__UpperCamelCase :str = adapter_stride
__UpperCamelCase :Tuple = num_adapter_layers
__UpperCamelCase :Tuple = output_hidden_size or hidden_size
__UpperCamelCase :Optional[Any] = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__UpperCamelCase :Optional[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__UpperCamelCase :Optional[int] = list(__lowercase)
__UpperCamelCase :List[Any] = list(__lowercase)
__UpperCamelCase :List[Any] = list(__lowercase)
__UpperCamelCase :str = xvector_output_dim
@property
def UpperCamelCase__ ( self) -> List[str]:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 43 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Optional[Any] = "speech_to_text_2"
UpperCAmelCase__ : List[Any] = ["past_key_values"]
UpperCAmelCase__ : Any = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__( self, SCREAMING_SNAKE_CASE_=1_0000, SCREAMING_SNAKE_CASE_=6, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="relu", SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1024, **SCREAMING_SNAKE_CASE_, ) -> int:
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : List[str] = d_model
UpperCamelCase : List[str] = decoder_ffn_dim
UpperCamelCase : Optional[Any] = decoder_layers
UpperCamelCase : Any = decoder_attention_heads
UpperCamelCase : Tuple = dropout
UpperCamelCase : str = attention_dropout
UpperCamelCase : str = activation_dropout
UpperCamelCase : Union[str, Any] = activation_function
UpperCamelCase : Optional[int] = init_std
UpperCamelCase : Tuple = decoder_layerdrop
UpperCamelCase : Dict = use_cache
UpperCamelCase : Any = decoder_layers
UpperCamelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Union[str, Any] = max_target_positions
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_, bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, decoder_start_token_id=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
| 119 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : Tuple , _lowerCamelCase : Any=7 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=30 , _lowerCamelCase : Dict=400 , _lowerCamelCase : Dict=True , _lowerCamelCase : str=None , _lowerCamelCase : List[Any]=True , _lowerCamelCase : List[str]=1 / 255 , _lowerCamelCase : Any=True , _lowerCamelCase : str=[0.5, 0.5, 0.5] , _lowerCamelCase : List[str]=[0.5, 0.5, 0.5] , _lowerCamelCase : Any=True , ):
"""simple docstring"""
A_ : Any = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
A_ : Any = parent
A_ : int = batch_size
A_ : str = num_channels
A_ : str = min_resolution
A_ : Dict = max_resolution
A_ : List[Any] = do_resize
A_ : str = size
A_ : str = do_rescale
A_ : List[Any] = rescale_factor
A_ : List[str] = do_normalize
A_ : Dict = image_mean
A_ : int = image_std
A_ : int = do_pad
def _a ( self : Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def _a ( self : Optional[int] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any]=False ):
"""simple docstring"""
if not batched:
A_ : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
A_ ,A_ : Optional[int] = image.size
else:
A_ ,A_ : int = image.shape[1], image.shape[2]
if w < h:
A_ : Optional[int] = int(self.size['''shortest_edge'''] * h / w )
A_ : Any = self.size['''shortest_edge''']
elif w > h:
A_ : Dict = self.size['''shortest_edge''']
A_ : List[Any] = int(self.size['''shortest_edge'''] * w / h )
else:
A_ : int = self.size['''shortest_edge''']
A_ : List[Any] = self.size['''shortest_edge''']
else:
A_ : List[Any] = []
for image in image_inputs:
A_ ,A_ : List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A_ : Any = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
A_ : Any = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = DetrImageProcessor if is_vision_available() else None
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Any = DetrImageProcessingTester(self )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_rescale''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''rescale_factor''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_pad''' ) )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
A_ : Any = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
def _a ( self : Dict ):
"""simple docstring"""
A_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
A_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ ,A_ : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ ,A_ : int = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
A_ : Tuple = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
A_ : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ ,A_ : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ : Optional[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
A_ ,A_ : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
A_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ ,A_ : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ : Dict = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
A_ ,A_ : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
A_ : Union[str, Any] = json.loads(f.read() )
A_ : Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
A_ : Union[str, Any] = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
A_ : List[str] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
A_ : Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
A_ : int = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
A_ : Dict = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
A_ : int = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
A_ : Dict = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
A_ : Union[str, Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
A_ : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
A_ : Tuple = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
A_ : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
A_ : Optional[int] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def _a ( self : Any ):
"""simple docstring"""
A_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
A_ : Tuple = json.loads(f.read() )
A_ : Union[str, Any] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
A_ : Optional[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
A_ : str = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
A_ : Tuple = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
A_ : Dict = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
A_ : Optional[Any] = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
A_ : Dict = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
A_ : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
A_ : Any = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
A_ : Tuple = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
A_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
A_ : Union[str, Any] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
A_ : int = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
A_ : Dict = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
A_ : Any = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 4 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ ,A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 4 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Dict , _UpperCamelCase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
_SCREAMING_SNAKE_CASE =_modexpt(snake_case__ , exponent // 2 , snake_case__ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(snake_case__ , exponent - 1 , snake_case__ )) % modulo_value
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] = 17_77 , _UpperCamelCase : int = 18_55 , _UpperCamelCase : Any = 8 ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =base
for _ in range(1 , snake_case__ ):
_SCREAMING_SNAKE_CASE =_modexpt(snake_case__ , snake_case__ , 10**digits )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 47 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : List[Any] = "▁"
A_ : str = {"vocab_file": "sentencepiece.bpe.model"}
A_ : Union[str, Any] = {
"vocab_file": {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
),
}
}
A_ : List[str] = {
"xlm-roberta-base": 512,
"xlm-roberta-large": 512,
"xlm-roberta-large-finetuned-conll02-dutch": 512,
"xlm-roberta-large-finetuned-conll02-spanish": 512,
"xlm-roberta-large-finetuned-conll03-english": 512,
"xlm-roberta-large-finetuned-conll03-german": 512,
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase__ : Any = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple="<s>" , __UpperCAmelCase : str="</s>" , __UpperCAmelCase : int="</s>" , __UpperCAmelCase : Tuple="<s>" , __UpperCAmelCase : Union[str, Any]="<unk>" , __UpperCAmelCase : List[str]="<pad>" , __UpperCAmelCase : Dict="<mask>" , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : Optional[int] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
SCREAMING_SNAKE_CASE__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__UpperCAmelCase ) )
SCREAMING_SNAKE_CASE__ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE__ = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = len(self.sp_model ) + self.fairseq_offset
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Tuple , __UpperCAmelCase : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = 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, 1] + ([0] * len(__UpperCAmelCase )) + [1]
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[int]:
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE ( self : List[Any] , __UpperCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : List[str] ) -> Tuple:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE__ = self.sp_model.PieceToId(__UpperCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE ( self : List[Any] , __UpperCAmelCase : int ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = """""".join(__UpperCAmelCase ).replace(__UpperCAmelCase , """ """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE__ = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 165 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
_lowerCAmelCase : Tuple = logging.get_logger('''transformers.models.speecht5''')
_lowerCAmelCase : int = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
_lowerCAmelCase : str = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
_lowerCAmelCase : int = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
_lowerCAmelCase : Union[str, Any] = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
_lowerCAmelCase : Union[str, Any] = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
_lowerCAmelCase : int = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
_lowerCAmelCase : Any = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
_lowerCAmelCase : List[str] = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
_lowerCAmelCase : Optional[Any] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase : Dict = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase : Union[str, Any] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase : Optional[Any] = []
_lowerCAmelCase : Tuple = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
_lowerCAmelCase : Tuple = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
_lowerCAmelCase : int = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
_lowerCAmelCase : Optional[int] = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ) -> Optional[Any]:
for attribute in key.split("." ):
A_ : List[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
A_ : Tuple = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
A_ : List[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}" )
if weight_type == "weight":
A_ : Dict = value
elif weight_type == "weight_g":
A_ : int = value
elif weight_type == "weight_v":
A_ : str = value
elif weight_type == "bias":
A_ : int = value
elif weight_type == "running_mean":
A_ : str = value
elif weight_type == "running_var":
A_ : Any = value
elif weight_type == "num_batches_tracked":
A_ : str = value
else:
A_ : int = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}." )
def __snake_case ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int ) -> Union[str, Any]:
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
A_ , A_ : Tuple = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
A_ : Tuple = []
if task == "s2t":
A_ : Union[str, Any] = hf_model.speechta.encoder.prenet.feature_encoder
A_ : str = MAPPING_S2T
A_ : Union[str, Any] = IGNORE_KEYS_S2T
elif task == "t2s":
A_ : Optional[int] = None
A_ : Dict = MAPPING_T2S
A_ : Any = IGNORE_KEYS_T2S
elif task == "s2s":
A_ : Optional[int] = hf_model.speechta.encoder.prenet.feature_encoder
A_ : Dict = MAPPING_S2S
A_ : List[str] = IGNORE_KEYS_S2S
else:
raise ValueError(f"Unsupported task: {task}" )
for name, value in fairseq_dict.items():
if should_ignore(_lowerCAmelCase , _lowerCAmelCase ):
logger.info(f"{name} was ignored" )
continue
A_ : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == "group" , )
A_ : Tuple = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
A_ , A_ : Optional[Any] = key.split(".*." )
if prefix in name and suffix in name:
A_ : int = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
A_ : str = True
if "*" in mapped_key:
A_ : List[str] = name.split(_lowerCAmelCase )[0].split("." )[-2]
A_ : Optional[int] = mapped_key.replace("*" , _lowerCAmelCase )
if "weight_g" in name:
A_ : Union[str, Any] = "weight_g"
elif "weight_v" in name:
A_ : List[Any] = "weight_v"
elif "bias" in name:
A_ : Tuple = "bias"
elif "weight" in name:
A_ : List[Any] = "weight"
elif "running_mean" in name:
A_ : Union[str, Any] = "running_mean"
elif "running_var" in name:
A_ : Union[str, Any] = "running_var"
elif "num_batches_tracked" in name:
A_ : List[Any] = "num_batches_tracked"
else:
A_ : Optional[Any] = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"Unused weights: {unused_weights}" )
def __snake_case ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
A_ : int = full_name.split("conv_layers." )[-1]
A_ : Optional[Any] = name.split("." )
A_ : List[Any] = int(items[0] )
A_ : int = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
A_ : Optional[int] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
A_ : Optional[Any] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
A_ : Tuple = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
A_ : Union[str, Any] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def __snake_case ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : str=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : int=None , ) -> Optional[Any]:
if config_path is not None:
A_ : Dict = SpeechTaConfig.from_pretrained(_lowerCAmelCase )
else:
A_ : Optional[int] = SpeechTaConfig()
if task == "s2t":
A_ : Optional[Any] = config.max_text_positions
A_ : Optional[int] = SpeechTaForSpeechToText(_lowerCAmelCase )
elif task == "t2s":
A_ : str = 1876
A_ : List[str] = 600
A_ : List[str] = config.max_speech_positions
A_ : Tuple = SpeechTaForTextToSpeech(_lowerCAmelCase )
elif task == "s2s":
A_ : Optional[int] = 1876
A_ : int = config.max_speech_positions
A_ : Union[str, Any] = SpeechTaForSpeechToSpeech(_lowerCAmelCase )
else:
raise ValueError(f"Unknown task name: {task}" )
if vocab_path:
A_ : int = SpeechTaTokenizer(_lowerCAmelCase , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
A_ : str = AddedToken("<mask>" , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase )
A_ : int = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
A_ : int = SpeechTaFeatureExtractor()
A_ : Optional[Any] = SpeechTaProcessor(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
A_ : Union[str, Any] = torch.load(_lowerCAmelCase )
recursively_load_weights(fairseq_checkpoint["model"] , _lowerCAmelCase , _lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if repo_id:
print("Pushing to the hub..." )
processor.push_to_hub(_lowerCAmelCase )
model.push_to_hub(_lowerCAmelCase )
if __name__ == "__main__":
_lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
_lowerCAmelCase : Tuple = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 70 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase = '''falcon'''
__UpperCamelCase = ['''past_key_values''']
def __init__( self :List[Any] , snake_case :Optional[int]=65_024 , snake_case :Tuple=4_544 , snake_case :Dict=32 , snake_case :Union[str, Any]=71 , snake_case :List[Any]=1e-5 , snake_case :Union[str, Any]=0.02 , snake_case :List[Any]=True , snake_case :Union[str, Any]=0.0 , snake_case :int=0.0 , snake_case :Union[str, Any]=None , snake_case :Dict=False , snake_case :int=False , snake_case :Tuple=True , snake_case :str=True , snake_case :List[Any]=False , snake_case :Optional[Any]=11 , snake_case :Tuple=11 , **snake_case :List[Any] , ):
'''simple docstring'''
A_ : Optional[int] = vocab_size
# Backward compatibility with n_embed kwarg
A_ : Any = kwargs.pop("n_embed" , snake_case )
A_ : str = hidden_size if n_embed is None else n_embed
A_ : List[str] = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : List[str] = layer_norm_epsilon
A_ : Optional[Any] = initializer_range
A_ : Optional[int] = use_cache
A_ : str = hidden_dropout
A_ : str = attention_dropout
A_ : str = bos_token_id
A_ : List[str] = eos_token_id
A_ : Union[str, Any] = num_attention_heads if num_kv_heads is None else num_kv_heads
A_ : int = alibi
A_ : str = new_decoder_architecture
A_ : Dict = multi_query # Ignored when new_decoder_architecture is True
A_ : Any = parallel_attn
A_ : Optional[Any] = bias
super().__init__(bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
return self.hidden_size // self.num_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
return not self.alibi
| 70 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowercase_ = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def lowerCamelCase ( __lowerCamelCase : Optional[int] ) ->Optional[int]:
_SCREAMING_SNAKE_CASE = test_results.split(""" """ )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
_SCREAMING_SNAKE_CASE = expressions[-2] if """=""" in expressions[-1] else expressions[-1]
for i, expression in enumerate(_A ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowerCamelCase ( __lowerCamelCase : Union[str, Any] ) ->Dict:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = False
for line in failures_short_lines.split("""\n""" ):
if re.search(R"""_ \[doctest\]""" , _A ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = line.split(""" """ )[2]
elif in_error and not line.split(""" """ )[0].isdigit():
_SCREAMING_SNAKE_CASE = line
_SCREAMING_SNAKE_CASE = False
return failures
class a_ :
'''simple docstring'''
def __init__( self , A , A ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = title
_SCREAMING_SNAKE_CASE = doc_test_results["""time_spent"""].split(""",""" )[0]
_SCREAMING_SNAKE_CASE = doc_test_results["""success"""]
_SCREAMING_SNAKE_CASE = doc_test_results["""failures"""]
_SCREAMING_SNAKE_CASE = self.n_success + self.n_failures
# Failures and success of the modeling tests
_SCREAMING_SNAKE_CASE = doc_test_results
@property
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = [self._time_spent]
_SCREAMING_SNAKE_CASE = 0
for time in time_spent:
_SCREAMING_SNAKE_CASE = time.split(""":""" )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(UpperCAmelCase__ ) == 1:
_SCREAMING_SNAKE_CASE = [0, 0, time_parts[0]]
_SCREAMING_SNAKE_CASE = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3600 + minutes * 60 + seconds
_SCREAMING_SNAKE_CASE = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
return f'{int(UpperCAmelCase__ )}h{int(UpperCAmelCase__ )}m{int(UpperCAmelCase__ )}s'
@property
def snake_case_( self ) -> Dict:
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def snake_case_( self ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def snake_case_( self ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
f' {self.time}.'
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = 40
_SCREAMING_SNAKE_CASE = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(UpperCAmelCase__ , UpperCAmelCase__ )}
_SCREAMING_SNAKE_CASE = """"""
for category, failures in category_failures.items():
if len(UpperCAmelCase__ ) == 0:
continue
if report != "":
report += "\n\n"
report += f'*{category} failures*:'.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(UpperCAmelCase__ )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'The following examples had failures:\n\n\n{report}\n',
},
}
@property
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(UpperCAmelCase__ )
@staticmethod
def snake_case_( ) -> Tuple:
_SCREAMING_SNAKE_CASE = [
{
"""type""": """section""",
"""text""": {
"""type""": """plain_text""",
"""text""": """There was an issue running the tests.""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
"""url""": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
]
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(UpperCAmelCase__ )} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text="""There was an issue running the tests.""" , blocks=UpperCAmelCase__ , )
def snake_case_( self ) -> Optional[int]:
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(self.payload )} ) )
_SCREAMING_SNAKE_CASE = f'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else """All tests passed."""
_SCREAMING_SNAKE_CASE = client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , blocks=self.payload , text=UpperCAmelCase__ , )
def snake_case_( self , A , A , A , A ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = """"""
for key, value in failures.items():
_SCREAMING_SNAKE_CASE = value[:200] + """ [Truncated]""" if len(UpperCAmelCase__ ) > 250 else value
failures_text += f'*{key}*\n_{value}_\n\n'
_SCREAMING_SNAKE_CASE = job_name
_SCREAMING_SNAKE_CASE = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}
if job_link is not None:
_SCREAMING_SNAKE_CASE = {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
"""url""": job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def snake_case_( self ) -> Tuple:
if self.thread_ts is None:
raise ValueError("""Can only post reply if a post has been made.""" )
_SCREAMING_SNAKE_CASE = self.doc_test_results.pop("""job_link""" )
self.doc_test_results.pop("""failures""" )
self.doc_test_results.pop("""success""" )
self.doc_test_results.pop("""time_spent""" )
_SCREAMING_SNAKE_CASE = sorted(self.doc_test_results.items() , key=lambda A : t[0] )
for job, job_result in sorted_dict:
if len(job_result["""failures"""] ):
_SCREAMING_SNAKE_CASE = f'*Num failures* :{len(job_result["failed"] )} \n'
_SCREAMING_SNAKE_CASE = job_result["""failures"""]
_SCREAMING_SNAKE_CASE = self.get_reply_blocks(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , text=UpperCAmelCase__ )
print("""Sending the following reply""" )
print(json.dumps({"""blocks""": blocks} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text=f'Results for {job}' , blocks=UpperCAmelCase__ , thread_ts=self.thread_ts["""ts"""] , )
time.sleep(1 )
def lowerCamelCase ( ) ->Union[str, Any]:
_SCREAMING_SNAKE_CASE = os.environ["""GITHUB_RUN_ID"""]
_SCREAMING_SNAKE_CASE = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
_SCREAMING_SNAKE_CASE = requests.get(_A ).json()
_SCREAMING_SNAKE_CASE = {}
try:
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
_SCREAMING_SNAKE_CASE = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(_A ):
_SCREAMING_SNAKE_CASE = requests.get(url + F'&page={i + 2}' ).json()
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return jobs
except Exception as e:
print("""Unknown error, could not fetch links.""" , _A )
return {}
def lowerCamelCase ( __lowerCamelCase : List[str] ) ->Tuple:
_SCREAMING_SNAKE_CASE = {}
if os.path.exists(_A ):
_SCREAMING_SNAKE_CASE = os.listdir(_A )
for file in files:
try:
with open(os.path.join(_A , _A ) , encoding="""utf-8""" ) as f:
_SCREAMING_SNAKE_CASE = f.read()
except UnicodeDecodeError as e:
raise ValueError(F'Could not open {os.path.join(_A , _A )}.' ) from e
return _artifact
def lowerCamelCase ( ) ->str:
class a_ :
'''simple docstring'''
def __init__( self , A ) -> Tuple:
_SCREAMING_SNAKE_CASE = name
_SCREAMING_SNAKE_CASE = []
def __str__( self ) -> List[str]:
return self.name
def snake_case_( self , A ) -> int:
self.paths.append({"""name""": self.name, """path""": path} )
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = filter(os.path.isdir , os.listdir() )
for directory in directories:
_SCREAMING_SNAKE_CASE = directory
if artifact_name not in _available_artifacts:
_SCREAMING_SNAKE_CASE = Artifact(_A )
_available_artifacts[artifact_name].add_path(_A )
return _available_artifacts
if __name__ == "__main__":
lowercase_ = get_job_links()
lowercase_ = retrieve_available_artifacts()
lowercase_ = collections.OrderedDict(
[
("""*.py""", """API Examples"""),
("""*.md""", """MD Examples"""),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
lowercase_ = {
v: {
"""failed""": [],
"""failures""": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
lowercase_ = github_actions_job_links.get("""run_doctests""")
lowercase_ = available_artifacts["""doc_tests_gpu_test_reports"""].paths[0]
lowercase_ = retrieve_artifact(artifact_path["""name"""])
if "stats" in artifact:
lowercase_ = handle_test_results(artifact["""stats"""])
lowercase_ = failed
lowercase_ = success
lowercase_ = time_spent[1:-1] + """, """
lowercase_ = extract_first_line_failure(artifact["""failures_short"""])
for line in artifact["summary_short"].split("""\n"""):
if re.search("""FAILED""", line):
lowercase_ = line.replace("""FAILED """, """""")
lowercase_ = line.split()[0].replace("""\n""", """""")
if "::" in line:
lowercase_ = line.split("""::""")
else:
lowercase_ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
lowercase_ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
lowercase_ = all_failures[test] if test in all_failures else """N/A"""
lowercase_ = failure
break
lowercase_ = Message("""🤗 Results of the doc tests.""", doc_test_results)
message.post()
message.post_reply()
| 58 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase__ : bool = True ) ->None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : dict[T, list[T]] = {} # dictionary of lists
SCREAMING_SNAKE_CASE : Dict = directed
def _lowercase ( self : int , UpperCAmelCase__ : T , UpperCAmelCase__ : T ) ->GraphAdjacencyList[T]:
"""simple docstring"""
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(UpperCAmelCase__ )
self.adj_list[destination_vertex].append(UpperCAmelCase__ )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : int = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : str = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
SCREAMING_SNAKE_CASE : Tuple = [destination_vertex]
SCREAMING_SNAKE_CASE : str = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(UpperCAmelCase__ )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
SCREAMING_SNAKE_CASE : Optional[Any] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
SCREAMING_SNAKE_CASE : Dict = [destination_vertex]
SCREAMING_SNAKE_CASE : List[Any] = []
return self
def __repr__( self : Dict ) ->str:
"""simple docstring"""
return pformat(self.adj_list )
| 245 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : Tuple = logging.get_logger(__name__)
lowercase : List[Any] = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = """table-transformer"""
__lowercase = ["""past_key_values"""]
__lowercase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , lowerCAmelCase_=True , lowerCAmelCase_=None , lowerCAmelCase_=3 , lowerCAmelCase_=1_00 , lowerCAmelCase_=6 , lowerCAmelCase_=20_48 , lowerCAmelCase_=8 , lowerCAmelCase_=6 , lowerCAmelCase_=20_48 , lowerCAmelCase_=8 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=True , lowerCAmelCase_="relu" , lowerCAmelCase_=2_56 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.02 , lowerCAmelCase_=1.0 , lowerCAmelCase_=False , lowerCAmelCase_="sine" , lowerCAmelCase_="resnet50" , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=1 , lowerCAmelCase_=5 , lowerCAmelCase_=2 , lowerCAmelCase_=1 , lowerCAmelCase_=1 , lowerCAmelCase_=5 , lowerCAmelCase_=2 , lowerCAmelCase_=0.1 , **lowerCAmelCase_ , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
_snake_case = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = backbone_config.get('model_type' )
_snake_case = CONFIG_MAPPING[backbone_model_type]
_snake_case = config_class.from_dict(lowerCAmelCase_ )
# set timm attributes to None
_snake_case , _snake_case , _snake_case = None, None, None
_snake_case = use_timm_backbone
_snake_case = backbone_config
_snake_case = num_channels
_snake_case = num_queries
_snake_case = d_model
_snake_case = encoder_ffn_dim
_snake_case = encoder_layers
_snake_case = encoder_attention_heads
_snake_case = decoder_ffn_dim
_snake_case = decoder_layers
_snake_case = decoder_attention_heads
_snake_case = dropout
_snake_case = attention_dropout
_snake_case = activation_dropout
_snake_case = activation_function
_snake_case = init_std
_snake_case = init_xavier_std
_snake_case = encoder_layerdrop
_snake_case = decoder_layerdrop
_snake_case = encoder_layers
_snake_case = auxiliary_loss
_snake_case = position_embedding_type
_snake_case = backbone
_snake_case = use_pretrained_backbone
_snake_case = dilation
# Hungarian matcher
_snake_case = class_cost
_snake_case = bbox_cost
_snake_case = giou_cost
# Loss coefficients
_snake_case = mask_loss_coefficient
_snake_case = dice_loss_coefficient
_snake_case = bbox_loss_coefficient
_snake_case = giou_loss_coefficient
_snake_case = eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self.d_model
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return 1E-5
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return 12
| 369 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : Any = logging.get_logger(__name__)
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=None , lowerCAmelCase_=None ):
"""simple docstring"""
if not conversation_id:
_snake_case = uuid.uuida()
if past_user_inputs is None:
_snake_case = []
if generated_responses is None:
_snake_case = []
_snake_case = conversation_id
_snake_case = past_user_inputs
_snake_case = generated_responses
_snake_case = text
def __eq__( self , lowerCAmelCase_ ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = False ):
"""simple docstring"""
if self.new_user_input:
if overwrite:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
F'with: "{text}".' )
_snake_case = text
else:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' )
else:
_snake_case = text
def lowerCamelCase ( self ):
"""simple docstring"""
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
_snake_case = None
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
self.generated_responses.append(lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
"""simple docstring"""
_snake_case = F'Conversation id: {self.uuid} \n'
for is_user, text in self.iter_texts():
_snake_case = 'user' if is_user else 'bot'
output += F'{name} >> {text} \n'
return output
@add_end_docstrings(
_lowerCamelCase , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
super().__init__(*lowerCAmelCase_ , **lowerCAmelCase_ )
if self.tokenizer.pad_token_id is None:
_snake_case = self.tokenizer.eos_token
def lowerCamelCase ( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = {}
_snake_case = {}
_snake_case = {}
if min_length_for_response is not None:
_snake_case = min_length_for_response
if minimum_tokens is not None:
_snake_case = minimum_tokens
if "max_length" in generate_kwargs:
_snake_case = generate_kwargs['max_length']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
_snake_case = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(lowerCAmelCase_ )
return preprocess_params, forward_params, postprocess_params
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_=0 , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = super().__call__(lowerCAmelCase_ , num_workers=lowerCAmelCase_ , **lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and len(lowerCAmelCase_ ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=32 ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
_snake_case = self.tokenizer._build_conversation_input_ids(lowerCAmelCase_ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
_snake_case = self._legacy_parse_and_tokenize(lowerCAmelCase_ )
if self.framework == "pt":
_snake_case = torch.LongTensor([input_ids] )
elif self.framework == "tf":
_snake_case = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=10 , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = generate_kwargs.get('max_length' , self.model.config.max_length )
_snake_case = model_inputs['input_ids'].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
_snake_case = max_length - minimum_tokens
_snake_case = model_inputs['input_ids'][:, -trim:]
if "attention_mask" in model_inputs:
_snake_case = model_inputs['attention_mask'][:, -trim:]
_snake_case = model_inputs.pop('conversation' )
_snake_case = max_length
_snake_case = self.model.generate(**lowerCAmelCase_ , **lowerCAmelCase_ )
if self.model.config.is_encoder_decoder:
_snake_case = 1
else:
_snake_case = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=True ):
"""simple docstring"""
_snake_case = model_outputs['output_ids']
_snake_case = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ , )
_snake_case = model_outputs['conversation']
conversation.mark_processed()
conversation.append_response(lowerCAmelCase_ )
return conversation
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.tokenizer.eos_token_id
_snake_case = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) )
if len(lowerCAmelCase_ ) > self.tokenizer.model_max_length:
_snake_case = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 160 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class A_ :
def __init__( self : List[str] ):
_UpperCAmelCase = {}
def lowercase ( self : int , snake_case_ : str ):
_UpperCAmelCase = {}
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : str , snake_case_ : float ):
if nodea not in self.connections:
self.add_node(snake_case_ )
if nodea not in self.connections:
self.add_node(snake_case_ )
_UpperCAmelCase = probability
def lowercase ( self : Tuple ):
return list(self.connections )
def lowercase ( self : Dict , snake_case_ : str ):
_UpperCAmelCase = 0
_UpperCAmelCase = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def UpperCAmelCase_ ( __lowercase : str , __lowercase : list[tuple[str, str, float]] , __lowercase : int ) -> dict[str, int]:
'''simple docstring'''
_UpperCAmelCase = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__lowercase , __lowercase , __lowercase )
_UpperCAmelCase = Counter(graph.get_nodes() )
_UpperCAmelCase = start
for _ in range(__lowercase ):
_UpperCAmelCase = graph.transition(__lowercase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 | 1 |
'''simple docstring'''
import os
import sys
import unittest
__snake_case =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__snake_case =os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
__snake_case =os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self : int ) -> Optional[int]:
lowerCAmelCase = get_test_to_tester_mapping(UpperCAmelCase__ )
lowerCAmelCase = get_test_to_tester_mapping(UpperCAmelCase__ )
lowerCAmelCase = {'BertModelTest': 'BertModelTester'}
lowerCAmelCase = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[int] ) -> Tuple:
lowerCAmelCase = get_model_to_test_mapping(UpperCAmelCase__ )
lowerCAmelCase = get_model_to_test_mapping(UpperCAmelCase__ )
lowerCAmelCase = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
lowerCAmelCase = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[Any] ) -> Any:
lowerCAmelCase = get_model_to_tester_mapping(UpperCAmelCase__ )
lowerCAmelCase = get_model_to_tester_mapping(UpperCAmelCase__ )
lowerCAmelCase = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
lowerCAmelCase = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase__ ) , UpperCAmelCase__ )
| 55 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__snake_case =TypeVar("""T""")
class UpperCAmelCase_ ( Generic[T] ):
def __init__( self : int , UpperCAmelCase__ : T ) -> List[str]:
lowerCAmelCase = data
lowerCAmelCase = None
def __str__( self : Optional[int] ) -> str:
return F'''{self.data}'''
class UpperCAmelCase_ ( Generic[T] ):
def __init__( self : Optional[Any] ) -> None:
lowerCAmelCase = None
def __iter__( self : Any ) -> Iterator[T]:
lowerCAmelCase = self.top
while node:
yield node.data
lowerCAmelCase = node.next
def __str__( self : str ) -> str:
return "->".join([str(UpperCAmelCase__ ) for item in self] )
def __len__( self : Optional[int] ) -> int:
return len(tuple(iter(self ) ) )
def __UpperCAmelCase ( self : Optional[Any] ) -> bool:
return self.top is None
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : T ) -> None:
lowerCAmelCase = Node(UpperCAmelCase__ )
if not self.is_empty():
lowerCAmelCase = self.top
lowerCAmelCase = node
def __UpperCAmelCase ( self : str ) -> T:
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , UpperCAmelCase__ )
lowerCAmelCase = self.top
lowerCAmelCase = self.top.next
return pop_node.data
def __UpperCAmelCase ( self : List[Any] ) -> T:
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def __UpperCAmelCase ( self : str ) -> None:
lowerCAmelCase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 55 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Union[str, Any] = """conditional_detr"""
_UpperCAmelCase : Optional[int] = ["""past_key_values"""]
_UpperCAmelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , __magic_name__=True , __magic_name__=None , __magic_name__=3 , __magic_name__=3_0_0 , __magic_name__=6 , __magic_name__=2_0_4_8 , __magic_name__=8 , __magic_name__=6 , __magic_name__=2_0_4_8 , __magic_name__=8 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=True , __magic_name__="relu" , __magic_name__=2_5_6 , __magic_name__=0.1 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=False , __magic_name__="sine" , __magic_name__="resnet50" , __magic_name__=True , __magic_name__=False , __magic_name__=2 , __magic_name__=5 , __magic_name__=2 , __magic_name__=1 , __magic_name__=1 , __magic_name__=2 , __magic_name__=5 , __magic_name__=2 , __magic_name__=0.25 , **__magic_name__ , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowerCamelCase : Optional[int] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__magic_name__ , __magic_name__ ):
lowerCamelCase : List[Any] = backbone_config.get("""model_type""" )
lowerCamelCase : Dict = CONFIG_MAPPING[backbone_model_type]
lowerCamelCase : str = config_class.from_dict(__magic_name__ )
lowerCamelCase : Dict = use_timm_backbone
lowerCamelCase : str = backbone_config
lowerCamelCase : Tuple = num_channels
lowerCamelCase : Dict = num_queries
lowerCamelCase : Any = d_model
lowerCamelCase : Optional[Any] = encoder_ffn_dim
lowerCamelCase : List[str] = encoder_layers
lowerCamelCase : Union[str, Any] = encoder_attention_heads
lowerCamelCase : Any = decoder_ffn_dim
lowerCamelCase : Dict = decoder_layers
lowerCamelCase : Union[str, Any] = decoder_attention_heads
lowerCamelCase : Dict = dropout
lowerCamelCase : List[str] = attention_dropout
lowerCamelCase : Union[str, Any] = activation_dropout
lowerCamelCase : Optional[int] = activation_function
lowerCamelCase : int = init_std
lowerCamelCase : str = init_xavier_std
lowerCamelCase : Tuple = encoder_layerdrop
lowerCamelCase : str = decoder_layerdrop
lowerCamelCase : Tuple = encoder_layers
lowerCamelCase : Optional[int] = auxiliary_loss
lowerCamelCase : Optional[Any] = position_embedding_type
lowerCamelCase : Optional[int] = backbone
lowerCamelCase : Union[str, Any] = use_pretrained_backbone
lowerCamelCase : str = dilation
# Hungarian matcher
lowerCamelCase : Optional[Any] = class_cost
lowerCamelCase : Dict = bbox_cost
lowerCamelCase : Tuple = giou_cost
# Loss coefficients
lowerCamelCase : Union[str, Any] = mask_loss_coefficient
lowerCamelCase : Dict = dice_loss_coefficient
lowerCamelCase : Optional[int] = cls_loss_coefficient
lowerCamelCase : Optional[int] = bbox_loss_coefficient
lowerCamelCase : Optional[int] = giou_loss_coefficient
lowerCamelCase : Optional[int] = focal_alpha
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def UpperCamelCase__ ( self ):
return self.encoder_attention_heads
@property
def UpperCamelCase__ ( self ):
return self.d_model
def UpperCamelCase__ ( self ):
lowerCamelCase : Optional[int] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowerCamelCase : Optional[int] = self.backbone_config.to_dict()
lowerCamelCase : Optional[Any] = self.__class__.model_type
return output
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = version.parse("""1.11""")
@property
def UpperCamelCase__ ( self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def UpperCamelCase__ ( self ):
return 1e-5
@property
def UpperCamelCase__ ( self ):
return 1_2
| 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
_lowerCamelCase =get_logger(__name__)
class A__ :
def __init__( self , __magic_name__ = None ):
lowerCamelCase : Dict = (
os.path.join(__magic_name__ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
lowerCamelCase : List[str] = Extractor
def UpperCamelCase__ ( self , __magic_name__ ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
lowerCamelCase : int = os.path.abspath(__magic_name__ )
return os.path.join(self.extract_dir , hash_url_to_filename(__magic_name__ ) )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ ):
return force_extract or (
not os.path.isfile(__magic_name__ ) and not (os.path.isdir(__magic_name__ ) and os.listdir(__magic_name__ ))
)
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ = False ):
lowerCamelCase : Union[str, Any] = self.extractor.infer_extractor_format(__magic_name__ )
if not extractor_format:
return input_path
lowerCamelCase : int = self._get_output_path(__magic_name__ )
if self._do_extract(__magic_name__ , __magic_name__ ):
self.extractor.extract(__magic_name__ , __magic_name__ , __magic_name__ )
return output_path
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
@abstractmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
...
@staticmethod
@abstractmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
...
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[bytes] = []
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with open(__magic_name__ , """rb""" ) as f:
return f.read(__magic_name__ )
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if not magic_number:
lowerCamelCase : Optional[Any] = max(len(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
try:
lowerCamelCase : Tuple = cls.read_magic_number(__magic_name__ , __magic_name__ )
except OSError:
return False
return any(magic_number.startswith(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
return tarfile.is_tarfile(__magic_name__ )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
def resolved(__magic_name__ ) -> str:
return os.path.realpath(os.path.abspath(__magic_name__ ) )
def badpath(__magic_name__ , __magic_name__ ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__magic_name__ , __magic_name__ ) ).startswith(__magic_name__ )
def badlink(__magic_name__ , __magic_name__ ) -> bool:
# Links are interpreted relative to the directory containing the link
lowerCamelCase : List[str] = resolved(os.path.join(__magic_name__ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=__magic_name__ )
lowerCamelCase : Optional[Any] = resolved(__magic_name__ )
for finfo in members:
if badpath(finfo.name , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)''' )
elif finfo.issym() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''' )
elif finfo.islnk() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''' )
else:
yield finfo
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Dict = tarfile.open(__magic_name__ )
tar_file.extractall(__magic_name__ , members=TarExtractor.safemembers(__magic_name__ , __magic_name__ ) )
tar_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = [B"""\x1F\x8B"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with gzip.open(__magic_name__ , """rb""" ) as gzip_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = [
B"""PK\x03\x04""",
B"""PK\x05\x06""", # empty archive
B"""PK\x07\x08""", # spanned archive
]
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if super().is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__magic_name__ , """rb""" ) as fp:
lowerCamelCase : List[str] = _EndRecData(__magic_name__ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
lowerCamelCase : List[Any] = fp.read(__magic_name__ ) # CD is where we expect it to be
if len(__magic_name__ ) == sizeCentralDir:
lowerCamelCase : str = struct.unpack(__magic_name__ , __magic_name__ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with zipfile.ZipFile(__magic_name__ , """r""" ) as zip_file:
zip_file.extractall(__magic_name__ )
zip_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[str] = [B"""\xFD\x37\x7A\x58\x5A\x00"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with lzma.open(__magic_name__ ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""] # RAR_ID # RAR5_ID
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Union[str, Any] = rarfile.RarFile(__magic_name__ )
rf.extractall(__magic_name__ )
rf.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Tuple = [B"""\x28\xb5\x2F\xFD"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
lowerCamelCase : int = zstd.ZstdDecompressor()
with open(__magic_name__ , """rb""" ) as ifh, open(__magic_name__ , """wb""" ) as ofh:
dctx.copy_stream(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""\x42\x5A\x68"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with bza.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x37\x7A\xBC\xAF\x27\x1C"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with pyazr.SevenZipFile(__magic_name__ , """r""" ) as archive:
archive.extractall(__magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x04\x22\x4D\x18"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
_UpperCAmelCase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def UpperCamelCase__ ( cls ):
return max(
len(__magic_name__ )
for extractor in cls.extractors.values()
if issubclass(__magic_name__ , __magic_name__ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
try:
return MagicNumberBaseExtractor.read_magic_number(__magic_name__ , magic_number_length=__magic_name__ )
except OSError:
return b""
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = False ):
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = cls.infer_extractor_format(__magic_name__ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ ): # <Added version="2.4.0"/>
lowerCamelCase : Dict = cls._get_magic_number_max_length()
lowerCamelCase : Optional[Any] = cls._read_magic_number(__magic_name__ , __magic_name__ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return extractor_format
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = "deprecated" , ):
os.makedirs(os.path.dirname(__magic_name__ ) , exist_ok=__magic_name__ )
# Prevent parallel extractions
lowerCamelCase : Tuple = str(Path(__magic_name__ ).with_suffix(""".lock""" ) )
with FileLock(__magic_name__ ):
shutil.rmtree(__magic_name__ , ignore_errors=__magic_name__ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__magic_name__ , __magic_name__ ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = extractor if extractor != """deprecated""" else extractor_format
else:
lowerCamelCase : Optional[int] = cls.extractors[extractor_format]
return extractor.extract(__magic_name__ , __magic_name__ )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=__magic_name__ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__magic_name__ ):
return extractor.extract(__magic_name__ , __magic_name__ )
| 287 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = 'Hello, World!'
lowercase_ = 'en_XX'
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : int = Path('data_bin' )
__lowerCamelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__ ).parent ) , checkpoint_file=Path(SCREAMING_SNAKE_CASE__ ).name , _name='xmod_base' , arch='xmod_base' , task='multilingual_masked_lm' , data_name_or_path=str(SCREAMING_SNAKE_CASE__ ) , bpe='sentencepiece' , sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__ ).parent / 'sentencepiece.bpe.model' ) , src_dict=str(data_dir / 'dict.txt' ) , )
xmod.eval() # disable dropout
print(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Dict = xmod.model.encoder.sentence_encoder
__lowerCamelCase : List[Any] = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , 'bottleneck' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
__lowerCamelCase : List[Any] = xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]
print('Our X-MOD config:' , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Tuple = XmodForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
__lowerCamelCase : Optional[int] = xmod_sent_encoder.embed_tokens.weight
__lowerCamelCase : Dict = xmod_sent_encoder.embed_positions.weight
__lowerCamelCase : List[str] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
__lowerCamelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
__lowerCamelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__lowerCamelCase : Union[str, Any] = model.roberta.encoder.layer[i]
__lowerCamelCase : List[Any] = xmod_sent_encoder.layers[i]
# self attention
__lowerCamelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('Dimensions of self-attention weights do not match.' )
__lowerCamelCase : int = xmod_layer.self_attn.q_proj.weight
__lowerCamelCase : Any = xmod_layer.self_attn.q_proj.bias
__lowerCamelCase : Tuple = xmod_layer.self_attn.k_proj.weight
__lowerCamelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
__lowerCamelCase : List[Any] = xmod_layer.self_attn.v_proj.weight
__lowerCamelCase : Dict = xmod_layer.self_attn.v_proj.bias
# self-attention output
__lowerCamelCase : List[str] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('Dimensions of self-attention output weights do not match.' )
__lowerCamelCase : Any = xmod_layer.self_attn.out_proj.weight
__lowerCamelCase : int = xmod_layer.self_attn.out_proj.bias
__lowerCamelCase : Optional[Any] = xmod_layer.self_attn_layer_norm.weight
__lowerCamelCase : Optional[int] = xmod_layer.self_attn_layer_norm.bias
# intermediate
__lowerCamelCase : List[Any] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of intermediate weights do not match.' )
__lowerCamelCase : Union[str, Any] = xmod_layer.fca.weight
__lowerCamelCase : Optional[Any] = xmod_layer.fca.bias
# output
__lowerCamelCase : Dict = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of feed-forward weights do not match.' )
__lowerCamelCase : Dict = xmod_layer.fca.weight
__lowerCamelCase : Tuple = xmod_layer.fca.bias
__lowerCamelCase : Union[str, Any] = xmod_layer.final_layer_norm.weight
__lowerCamelCase : str = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
__lowerCamelCase : Optional[Any] = xmod_layer.adapter_layer_norm.weight
__lowerCamelCase : Optional[int] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('Lists of language adapters do not match.' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
__lowerCamelCase : Tuple = bert_output.adapter_modules[lang_code]
__lowerCamelCase : Optional[int] = xmod_layer.adapter_modules[lang_code]
__lowerCamelCase : List[Any] = from_adapter.fca.weight
__lowerCamelCase : int = from_adapter.fca.bias
__lowerCamelCase : Tuple = from_adapter.fca.weight
__lowerCamelCase : int = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
__lowerCamelCase : Optional[int] = xmod_sent_encoder.layer_norm.weight
__lowerCamelCase : Optional[int] = xmod_sent_encoder.layer_norm.bias
if classification_head:
__lowerCamelCase : Any = xmod.model.classification_heads['mnli'].dense.weight
__lowerCamelCase : Dict = xmod.model.classification_heads['mnli'].dense.bias
__lowerCamelCase : Tuple = xmod.model.classification_heads['mnli'].out_proj.weight
__lowerCamelCase : Any = xmod.model.classification_heads['mnli'].out_proj.bias
else:
# LM Head
__lowerCamelCase : Any = xmod.model.encoder.lm_head.dense.weight
__lowerCamelCase : Optional[Any] = xmod.model.encoder.lm_head.dense.bias
__lowerCamelCase : Union[str, Any] = xmod.model.encoder.lm_head.layer_norm.weight
__lowerCamelCase : Tuple = xmod.model.encoder.lm_head.layer_norm.bias
__lowerCamelCase : List[Any] = xmod.model.encoder.lm_head.weight
__lowerCamelCase : Union[str, Any] = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
__lowerCamelCase : int = xmod.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )[0]
if classification_head:
__lowerCamelCase : Tuple = xmod.model.classification_heads['mnli'](xmod.extract_features(SCREAMING_SNAKE_CASE__ ) )
else:
__lowerCamelCase : Any = xmod.model(SCREAMING_SNAKE_CASE__ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
__lowerCamelCase : str = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
__lowerCamelCase : Optional[int] = torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-3 )
print('Do both models output the same tensors?' , '🔥' if success else '💩' )
if not success:
raise Exception('Something went wRoNg' )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
lowercase_ = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 194 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ = 5_0_0_0_0
lowercase_ = 5_0_0_0
lowercase_ ,lowercase_ = os.path.split(__file__)
lowercase_ = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for i in range(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Tuple = dataset[i]
@get_duration
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for i in range(0 , len(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Optional[Any] = dataset[i : i + batch_size]
@get_duration
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__ ):
for i in range(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : str = dataset[i]
@get_duration
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__ ):
for i in range(0 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : int = dataset[i : i + batch_size]
def UpperCamelCase__ ( ):
__lowerCamelCase : Union[str, Any] = {'num examples': SPEED_TEST_N_EXAMPLES}
__lowerCamelCase : Optional[Any] = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
(read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
(read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
__lowerCamelCase : Any = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
__lowerCamelCase : Optional[int] = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
__lowerCamelCase : str = generate_example_dataset(
os.path.join(SCREAMING_SNAKE_CASE__ , 'dataset.arrow' ) , SCREAMING_SNAKE_CASE__ , num_examples=SCREAMING_SNAKE_CASE__ , seq_shapes={'list': (100,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(SCREAMING_SNAKE_CASE__ ) )
__lowerCamelCase : Optional[int] = func(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
print('shuffling dataset' )
__lowerCamelCase : str = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(SCREAMING_SNAKE_CASE__ ) )
__lowerCamelCase : int = func(
SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE__ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 194 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __lowerCamelCase ( UpperCAmelCase_ : Dict ):
"""simple docstring"""
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def __lowerCamelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
a :List[Any] = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
a :List[str] = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
a :Any = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
a :Optional[Any] = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
a :int = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
a :Optional[int] = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
a :List[str] = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
a :Any = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
a :Dict = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
a :Union[str, Any] = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
a :Tuple = key.replace('''image_encoder.module''' , '''flava.image_model''' )
a :int = key.replace('''text_encoder.module''' , '''flava.text_model''' )
a :Tuple = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
a :Any = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
a :Union[str, Any] = key.replace('''text_projection''' , '''flava.text_projection''' )
a :Tuple = key.replace('''image_projection''' , '''flava.image_projection''' )
a :List[str] = value.float()
for key, value in codebook_state_dict.items():
a :List[Any] = value
return upgrade
@torch.no_grad()
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Dict=None ):
"""simple docstring"""
if config_path is not None:
a :List[str] = FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
else:
a :Tuple = FlavaConfig()
a :List[str] = FlavaForPreTraining(SCREAMING_SNAKE_CASE__ ).eval()
a :List[Any] = convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , save_checkpoint=SCREAMING_SNAKE_CASE__ )
if os.path.exists(SCREAMING_SNAKE_CASE__ ):
a :Tuple = torch.load(SCREAMING_SNAKE_CASE__ , map_location='''cpu''' )
else:
a :Any = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ , map_location='''cpu''' )
a :str = upgrade_state_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
hf_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
a :Optional[Any] = hf_model.state_dict()
a :List[Any] = count_parameters(SCREAMING_SNAKE_CASE__ )
a :Tuple = count_parameters(SCREAMING_SNAKE_CASE__ ) + count_parameters(SCREAMING_SNAKE_CASE__ )
assert torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1E-3 )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
snake_case : int = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
snake_case : Dict = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 94 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
snake_case_ : List[str] = logging.getLogger(__name__)
@dataclass
class __a :
__a : Optional[str] = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__a : Optional[str] = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__a : int = field(
default=1_024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__a : bool = field(
default=lowerCamelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__a : bool = field(
default=lowerCamelCase , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__a : Optional[int] = field(
default=lowerCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__a : Optional[int] = field(
default=lowerCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__a : Optional[int] = field(
default=lowerCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__a : Optional[str] = field(
default=lowerCamelCase , metadata={"help": "A csv or a json file containing the training data."} )
__a : Optional[str] = field(
default=lowerCamelCase , metadata={"help": "A csv or a json file containing the validation data."} )
__a : Optional[str] = field(default=lowerCamelCase , metadata={"help": "A csv or a json file containing the test data."} )
def UpperCAmelCase__ ( self : Dict ) -> Any:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.''' )
else:
UpperCAmelCase_ : Dict = self.train_file.split('''.''' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
UpperCAmelCase_ : Union[str, Any] = self.validation_file.split('''.''' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __a :
__a : str = field(
default=lowerCamelCase , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__a : Optional[str] = field(
default=lowerCamelCase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__a : Optional[str] = field(
default=lowerCamelCase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__a : Optional[str] = field(
default=lowerCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__a : bool = field(
default=lowerCamelCase , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__a : str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__a : bool = field(
default=lowerCamelCase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCamelCase_ ( ) -> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase_ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : int = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', handlers=[logging.StreamHandler(sys.stdout )], )
UpperCAmelCase_ : List[str] = training_args.get_process_log_level()
logger.setLevel(SCREAMING_SNAKE_CASE__ )
datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__ )
transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
UpperCAmelCase_ : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase_ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCAmelCase_ : List[Any] = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
UpperCAmelCase_ : Dict = {'''train''': data_args.train_file, '''validation''': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
UpperCAmelCase_ : Dict = data_args.train_file.split('''.''' )[-1]
UpperCAmelCase_ : Union[str, Any] = data_args.test_file.split('''.''' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
UpperCAmelCase_ : int = data_args.test_file
else:
raise ValueError('''Need either a GLUE task or a test file for `do_predict`.''' )
for key in data_files.keys():
logger.info(F"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('''.csv''' ):
# Loading a dataset from local csv files
UpperCAmelCase_ : List[Any] = load_dataset('''csv''', data_files=SCREAMING_SNAKE_CASE__, cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
UpperCAmelCase_ : int = load_dataset('''json''', data_files=SCREAMING_SNAKE_CASE__, cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
UpperCAmelCase_ : Optional[Any] = raw_datasets['''train'''].features['''label'''].names
UpperCAmelCase_ : List[str] = len(SCREAMING_SNAKE_CASE__ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase_ : Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=SCREAMING_SNAKE_CASE__, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# load tapex tokenizer
UpperCAmelCase_ : str = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, add_prefix_space=SCREAMING_SNAKE_CASE__, )
UpperCAmelCase_ : Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path ), config=SCREAMING_SNAKE_CASE__, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# Padding strategy
if data_args.pad_to_max_length:
UpperCAmelCase_ : Optional[int] = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
UpperCAmelCase_ : Dict = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
UpperCAmelCase_ : Tuple = {'''Refused''': 0, '''Entailed''': 1}
UpperCAmelCase_ : Tuple = {0: '''Refused''', 1: '''Entailed'''}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
UpperCAmelCase_ : int = min(data_args.max_seq_length, tokenizer.model_max_length )
def preprocess_tabfact_function(SCREAMING_SNAKE_CASE__ : Optional[int] ):
# Tokenize the texts
def _convert_table_text_to_pandas(SCREAMING_SNAKE_CASE__ : Tuple ):
UpperCAmelCase_ : List[str] = [_table_row.split('''#''' ) for _table_row in _table_text.strip('''\n''' ).split('''\n''' )]
UpperCAmelCase_ : Any = pd.DataFrame.from_records(_table_content[1:], columns=_table_content[0] )
return _table_pd
UpperCAmelCase_ : Optional[Any] = examples['''statement''']
UpperCAmelCase_ : Union[str, Any] = list(map(_convert_table_text_to_pandas, examples['''table_text'''] ) )
UpperCAmelCase_ : Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, padding=SCREAMING_SNAKE_CASE__, max_length=SCREAMING_SNAKE_CASE__, truncation=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : List[Any] = examples['''label''']
return result
with training_args.main_process_first(desc='''dataset map pre-processing''' ):
UpperCAmelCase_ : List[str] = raw_datasets.map(
SCREAMING_SNAKE_CASE__, batched=SCREAMING_SNAKE_CASE__, load_from_cache_file=not data_args.overwrite_cache, desc='''Running tokenizer on dataset''', )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
UpperCAmelCase_ : Any = raw_datasets['''train''']
if data_args.max_train_samples is not None:
UpperCAmelCase_ : Dict = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
UpperCAmelCase_ : str = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
UpperCAmelCase_ : Any = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('''--do_predict requires a test dataset''' )
UpperCAmelCase_ : Dict = raw_datasets['''test''']
if data_args.max_predict_samples is not None:
UpperCAmelCase_ : List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(SCREAMING_SNAKE_CASE__ ) ), 3 ):
logger.info(F"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(SCREAMING_SNAKE_CASE__ : EvalPrediction ):
UpperCAmelCase_ : Any = p.predictions[0] if isinstance(p.predictions, SCREAMING_SNAKE_CASE__ ) else p.predictions
UpperCAmelCase_ : Optional[int] = np.argmax(SCREAMING_SNAKE_CASE__, axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
UpperCAmelCase_ : Optional[Any] = default_data_collator
elif training_args.fpaa:
UpperCAmelCase_ : str = DataCollatorWithPadding(SCREAMING_SNAKE_CASE__, pad_to_multiple_of=8 )
else:
UpperCAmelCase_ : List[Any] = None
# Initialize our Trainer
UpperCAmelCase_ : int = Trainer(
model=SCREAMING_SNAKE_CASE__, args=SCREAMING_SNAKE_CASE__, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, compute_metrics=SCREAMING_SNAKE_CASE__, tokenizer=SCREAMING_SNAKE_CASE__, data_collator=SCREAMING_SNAKE_CASE__, )
# Training
if training_args.do_train:
UpperCAmelCase_ : Dict = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase_ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase_ : Optional[int] = last_checkpoint
UpperCAmelCase_ : Dict = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Any = train_result.metrics
UpperCAmelCase_ : Tuple = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE__ )
)
UpperCAmelCase_ : List[Any] = min(SCREAMING_SNAKE_CASE__, len(SCREAMING_SNAKE_CASE__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''', SCREAMING_SNAKE_CASE__ )
trainer.save_metrics('''train''', SCREAMING_SNAKE_CASE__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase_ : Union[str, Any] = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Tuple = min(SCREAMING_SNAKE_CASE__, len(SCREAMING_SNAKE_CASE__ ) )
trainer.log_metrics('''eval''', SCREAMING_SNAKE_CASE__ )
trainer.save_metrics('''eval''', SCREAMING_SNAKE_CASE__ )
if training_args.do_predict:
logger.info('''*** Predict ***''' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
UpperCAmelCase_ : Optional[int] = predict_dataset.remove_columns('''label''' )
UpperCAmelCase_ : Union[str, Any] = trainer.predict(SCREAMING_SNAKE_CASE__, metric_key_prefix='''predict''' ).predictions
UpperCAmelCase_ : Any = np.argmax(SCREAMING_SNAKE_CASE__, axis=1 )
UpperCAmelCase_ : int = os.path.join(training_args.output_dir, '''predict_results_tabfact.txt''' )
if trainer.is_world_process_zero():
with open(SCREAMING_SNAKE_CASE__, '''w''' ) as writer:
logger.info('''***** Predict Results *****''' )
writer.write('''index\tprediction\n''' )
for index, item in enumerate(SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase_ : Dict = label_list[item]
writer.write(F"""{index}\t{item}\n""" )
UpperCAmelCase_ : Optional[int] = {'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}
if training_args.push_to_hub:
trainer.push_to_hub(**SCREAMING_SNAKE_CASE__ )
else:
trainer.create_model_card(**SCREAMING_SNAKE_CASE__ )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[int]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 125 | 0 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case_ : str = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
snake_case_ : str = max(len(lowercase__ ) , len(lowercase__ ) )
return "0b" + "".join(
str(int(char_a == '1' and char_b == '1' ) )
for char_a, char_b in zip(a_binary.zfill(lowercase__ ) , b_binary.zfill(lowercase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356 |
def SCREAMING_SNAKE_CASE__ ( __a , __a = False ):
if not isinstance(__a , __a ):
snake_case_ : str = f"""Expected string as input, found {type(__a )}"""
raise ValueError(__a )
if not isinstance(__a , __a ):
snake_case_ : int = f"""Expected boolean as use_pascal parameter, found {type(__a )}"""
raise ValueError(__a )
snake_case_ : Union[str, Any] = input_str.split('_' )
snake_case_ : int = 0 if use_pascal else 1
snake_case_ : List[Any] = words[start_index:]
snake_case_ : str = [word[0].upper() + word[1:] for word in words_to_capitalize]
snake_case_ : Optional[Any] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 88 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCamelCase ( lowerCamelCase__ ):
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
lowerCamelCase_ ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(lowerCAmelCase )
BertModel.from_pretrained(lowerCAmelCase )
BertTokenizer.from_pretrained(lowerCAmelCase )
pipeline(task='''fill-mask''', model=lowerCAmelCase )
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
lowerCamelCase_ =self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
lowerCamelCase_ ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(lowerCAmelCase )
BertModel.from_pretrained(lowerCAmelCase )
BertTokenizer.from_pretrained(lowerCAmelCase )
pipeline(task='''fill-mask''', model=lowerCAmelCase )
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
# next emulate no network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
lowerCamelCase_ =self.get_env()
lowerCamelCase_ ='''1'''
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 1, result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''', result.stderr.decode().replace('''\n''', '''''' ), )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import AutoModel
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
| 75 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A :
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=30 , lowerCamelCase__=2 , lowerCamelCase__=3 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=10 , lowerCamelCase__=0.02 , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=2 , ) -> Optional[int]:
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = scope
lowercase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowercase__ = (image_size // patch_size) ** 2
lowercase__ = num_patches + 2
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = self.get_config()
return config, pixel_values, labels
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
lowercase__ = DeiTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
lowercase__ = DeiTForMaskedImageModeling(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = DeiTForMaskedImageModeling(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
lowercase__ = self.type_sequence_label_size
lowercase__ = DeiTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = DeiTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : int = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowerCamelCase : Tuple = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase : Any = False
lowerCamelCase : str = False
lowerCamelCase : str = False
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = DeiTModelTester(self )
lowercase__ = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def A__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(lowerCamelCase__ )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__ )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Dict:
'''simple docstring'''
lowercase__ = super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A__ ( self ) -> Any:
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase__ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
lowercase__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowercase__ = model(**lowerCamelCase__ ).loss
loss.backward()
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase__ = False
lowercase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
lowercase__ = model_class(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowercase__ = model(**lowerCamelCase__ ).loss
loss.backward()
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = [
{"""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(lowerCamelCase__ ),
*get_values(lowerCamelCase__ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
lowercase__ = problem_type["""title"""]
lowercase__ = problem_type["""num_labels"""]
lowercase__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if problem_type["num_labels"] > 1:
lowercase__ = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
lowercase__ = 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=lowerCamelCase__ ) as warning_list:
lowercase__ = model(**lowerCamelCase__ ).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 A__ ( self ) -> Any:
'''simple docstring'''
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = DeiTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def _A ( ):
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def A__ ( self ) -> int:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase__ = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
lowerCamelCase__ )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
lowercase__ = model(**lowerCamelCase__ )
# verify the logits
lowercase__ = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
lowercase__ = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__ = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" )
lowercase__ = inputs.pixel_values.to(lowerCamelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
lowercase__ = model(lowerCamelCase__ )
| 164 | 0 |
"""simple docstring"""
from ....utils import logging
A_ : List[str] = logging.get_logger(__name__)
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=2_0_4_8 ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = config.__dict__
lowerCamelCase__ : str = modal_hidden_size
if num_labels:
lowerCamelCase__ : List[str] = num_labels
| 316 |
"""simple docstring"""
import re
def lowerCamelCase_ ( _lowerCamelCase ):
if len(re.findall('[ATCG]' , _lowerCamelCase ) ) != len(_lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( snake_case__ , unittest.TestCase ):
__a : Union[str, Any] = KandinskyVaaControlnetImgaImgPipeline
__a : List[Any] = ["image_embeds", "negative_image_embeds", "image", "hint"]
__a : List[str] = ["image_embeds", "negative_image_embeds", "image", "hint"]
__a : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
__a : str = False
@property
def A ( self : Tuple ):
'''simple docstring'''
return 32
@property
def A ( self : List[Any] ):
'''simple docstring'''
return 32
@property
def A ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def A ( self : int ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return 100
@property
def A ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = {
"in_channels": 8,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image_hint",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
UpperCAmelCase = UNetaDConditionModel(**snake_case__ )
return model
@property
def A ( self : Any ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.dummy_unet
UpperCAmelCase = self.dummy_movq
UpperCAmelCase = {
"num_train_timesteps": 1_000,
"beta_schedule": "linear",
"beta_start": 0.0_0085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
UpperCAmelCase = DDIMScheduler(**snake_case__ )
UpperCAmelCase = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def A ( self : Union[str, Any] , lowercase : str , lowercase : str=0 ):
'''simple docstring'''
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case__ )
# create init_image
UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase = Image.fromarray(np.uinta(snake_case__ ) ).convert('''RGB''' ).resize((256, 256) )
# create hint
UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
if str(snake_case__ ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(snake_case__ )
else:
UpperCAmelCase = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"hint": hint,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = "cpu"
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**snake_case__ )
UpperCAmelCase = pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase = pipe(**self.get_dummy_inputs(snake_case__ ) )
UpperCAmelCase = output.images
UpperCAmelCase = pipe(
**self.get_dummy_inputs(snake_case__ ) , return_dict=snake_case__ , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array(
[0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def A ( self : str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
UpperCAmelCase = init_image.resize((512, 512) )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
UpperCAmelCase = torch.from_numpy(np.array(snake_case__ ) ).float() / 255.0
UpperCAmelCase = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
UpperCAmelCase = "A robot, 4k photo"
UpperCAmelCase = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case__ )
UpperCAmelCase = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
UpperCAmelCase = pipeline.to(snake_case__ )
pipeline.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase = pipe_prior(
snake_case__ , image=snake_case__ , strength=0.85 , generator=snake_case__ , negative_prompt='''''' , ).to_tuple()
UpperCAmelCase = pipeline(
image=snake_case__ , image_embeds=snake_case__ , negative_image_embeds=snake_case__ , hint=snake_case__ , generator=snake_case__ , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(snake_case__ , snake_case__ )
| 34 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowercase__ ( unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = JukeboxTokenizer
_UpperCAmelCase :List[Any] = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": "I met a traveller from an antique land,\n Who said \"Two vast and trunkless legs of stone\n Stand in the desert. . . . Near them, on the sand,\n Half sunk a shattered visage lies, whose frown,\n And wrinkled lip, and sneer of cold command,\n Tell that its sculptor well those passions read\n Which yet survive, stamped on these lifeless things,\n The hand that mocked them, and the heart that fed;\n And on the pedestal, these words appear:\n My name is Ozymandias, King of Kings;\n Look on my Works, ye Mighty, and despair!\n Nothing beside remains. Round the decay\n Of that colossal Wreck, boundless and bare\n The lone and level sands stretch far away\n ",
}
@require_torch
def UpperCAmelCase__ ( self : Dict ):
import torch
lowerCamelCase_ : Any =JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics" )
lowerCamelCase_ : List[Any] =tokenizer(**self.metas )["input_ids"]
# fmt: off
lowerCamelCase_ : Union[str, Any] =[
torch.tensor([[
0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def UpperCAmelCase__ ( self : str ):
import torch
lowerCamelCase_ : Any =JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics" )
lowerCamelCase_ : Optional[int] =tokenizer(**self.metas )["input_ids"]
# fmt: off
lowerCamelCase_ : Union[str, Any] =[
torch.tensor([[
0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 144 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : Union[str, Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : Any ) -> Dict:
SCREAMING_SNAKE_CASE_ = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __A ( cls : Optional[int] ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id="test-model-flax" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id="valid_org/test-model-flax-org" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE_ = False
return models_are_equal
@require_flax
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size="10KB" )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 305 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int]=7 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : Any=3_0 , UpperCAmelCase__ : str=4_0_0 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[int]=1 / 2_5_5 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : List[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Any=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Optional[int]=True , ) -> Any:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowerCAmelCase = size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = num_channels
lowerCAmelCase = min_resolution
lowerCAmelCase = max_resolution
lowerCAmelCase = do_resize
lowerCAmelCase = size
lowerCAmelCase = do_rescale
lowerCAmelCase = rescale_factor
lowerCAmelCase = do_normalize
lowerCAmelCase = image_mean
lowerCAmelCase = image_std
lowerCAmelCase = do_pad
def __UpperCAmelCase ( self : int ) -> Dict:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict=False ) -> str:
if not batched:
lowerCAmelCase = image_inputs[0]
if isinstance(UpperCAmelCase__ , Image.Image ):
lowerCAmelCase , lowerCAmelCase = image.size
else:
lowerCAmelCase , lowerCAmelCase = image.shape[1], image.shape[2]
if w < h:
lowerCAmelCase = int(self.size['shortest_edge'] * h / w )
lowerCAmelCase = self.size['shortest_edge']
elif w > h:
lowerCAmelCase = self.size['shortest_edge']
lowerCAmelCase = int(self.size['shortest_edge'] * w / h )
else:
lowerCAmelCase = self.size['shortest_edge']
lowerCAmelCase = self.size['shortest_edge']
else:
lowerCAmelCase = []
for image in image_inputs:
lowerCAmelCase , lowerCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCAmelCase = max(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : item[0] )[0]
lowerCAmelCase = max(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
lowerCamelCase : List[str] = DetrImageProcessor if is_vision_available() else None
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
lowerCAmelCase = DetrImageProcessingTester(self )
@property
def __UpperCAmelCase ( self : Optional[int] ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self : str ) -> str:
lowerCAmelCase = 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_rescale' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'rescale_factor' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'size' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'do_pad' ) )
def __UpperCAmelCase ( self : Optional[int] ) -> List[str]:
lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
lowerCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=UpperCAmelCase__ )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2, 'longest_edge': 8_4} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Any:
pass
def __UpperCAmelCase ( self : Dict ) -> str:
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , Image.Image )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __UpperCAmelCase ( self : int ) -> Tuple:
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , np.ndarray )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __UpperCAmelCase ( self : List[Any] ) -> Any:
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , torch.Tensor )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __UpperCAmelCase ( self : Optional[Any] ) -> Any:
# prepare image and target
lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
lowerCAmelCase = json.loads(f.read() )
lowerCAmelCase = {'image_id': 3_9_7_6_9, 'annotations': target}
# encode them
lowerCAmelCase = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
lowerCAmelCase = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , return_tensors='pt' )
# verify pixel values
lowerCAmelCase = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['pixel_values'].shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
# verify area
lowerCAmelCase = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , UpperCAmelCase__ ) )
# verify boxes
lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , UpperCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowerCAmelCase = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , UpperCAmelCase__ ) )
# verify is_crowd
lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , UpperCAmelCase__ ) )
# verify class_labels
lowerCAmelCase = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , UpperCAmelCase__ ) )
# verify orig_size
lowerCAmelCase = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , UpperCAmelCase__ ) )
# verify size
lowerCAmelCase = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , UpperCAmelCase__ ) )
@slow
def __UpperCAmelCase ( self : str ) -> str:
# prepare image, target and masks_path
lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
lowerCAmelCase = json.loads(f.read() )
lowerCAmelCase = {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}
lowerCAmelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
lowerCAmelCase = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
lowerCAmelCase = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , masks_path=UpperCAmelCase__ , return_tensors='pt' )
# verify pixel values
lowerCAmelCase = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['pixel_values'].shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
# verify area
lowerCAmelCase = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , UpperCAmelCase__ ) )
# verify boxes
lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , UpperCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowerCAmelCase = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , UpperCAmelCase__ ) )
# verify is_crowd
lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , UpperCAmelCase__ ) )
# verify class_labels
lowerCAmelCase = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , UpperCAmelCase__ ) )
# verify masks
lowerCAmelCase = 8_2_2_8_7_3
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , UpperCAmelCase__ )
# verify orig_size
lowerCAmelCase = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , UpperCAmelCase__ ) )
# verify size
lowerCAmelCase = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , UpperCAmelCase__ ) )
| 4 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case ="""\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
__snake_case ="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
__snake_case ="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def a_ ( lowerCamelCase : str , lowerCamelCase : Union[str, Any] ):
return float((preds == labels).mean() )
def a_ ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict , lowerCamelCase : str="binary" ):
lowerCAmelCase = simple_accuracy(lowerCamelCase , lowerCamelCase )
lowerCAmelCase = float(fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average=lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def a_ ( lowerCamelCase : List[Any] , lowerCamelCase : List[Any] ):
lowerCAmelCase = {}
for id_pred, label in zip(lowerCamelCase , lowerCamelCase ):
lowerCAmelCase = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
lowerCAmelCase = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
lowerCAmelCase = [(pred, label)]
lowerCAmelCase , lowerCAmelCase = [], []
for question, preds_labels in question_map.items():
lowerCAmelCase , lowerCAmelCase = zip(*lowerCamelCase )
lowerCAmelCase = fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average='macro' )
fas.append(lowerCamelCase )
lowerCAmelCase = int(sum(pred == label for pred, label in preds_labels ) == len(lowerCamelCase ) )
ems.append(lowerCamelCase )
lowerCAmelCase = float(sum(lowerCamelCase ) / len(lowerCamelCase ) )
lowerCAmelCase = sum(lowerCamelCase ) / len(lowerCamelCase )
lowerCAmelCase = float(fa_score(y_true=lowerCamelCase , y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
def __UpperCAmelCase ( self : List[str] ) -> List[Any]:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None , )
def __UpperCAmelCase ( self : Union[str, Any] ) -> str:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[Any] ) -> Any:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase__ , UpperCAmelCase__ )}
elif self.config_name == "cb":
return acc_and_fa(UpperCAmelCase__ , UpperCAmelCase__ , fa_avg='macro' )
elif self.config_name == "record":
lowerCAmelCase = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
lowerCAmelCase = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(UpperCAmelCase__ , UpperCAmelCase__ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
| 4 | 1 |
'''simple docstring'''
import itertools
import math
def a__ ( _SCREAMING_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(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = 2
while True:
if is_prime(_SCREAMING_SNAKE_CASE ):
yield num
num += 1
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00_01 ) -> int:
"""simple docstring"""
return next(itertools.islice(prime_generator() , nth - 1 , _SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 67 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def a__ ( _SCREAMING_SNAKE_CASE : str = "" ) -> bool:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) == 0:
return True
UpperCAmelCase_ : List[str] = input_str.replace(" " , "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
UpperCAmelCase_ : dict[str, int] = {}
for character in lower_case_input_str:
UpperCAmelCase_ : Any = character_freq_dict.get(_SCREAMING_SNAKE_CASE , 0 ) + 1
UpperCAmelCase_ : Union[str, Any] = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def a__ ( _SCREAMING_SNAKE_CASE : str = "" ) -> None:
"""simple docstring"""
print("\nFor string = " , _SCREAMING_SNAKE_CASE , ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()" , "\tans =" , can_string_be_rearranged_as_palindrome_counter(_SCREAMING_SNAKE_CASE ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
print(
"> can_string_be_rearranged_as_palindrome()" , "\tans =" , can_string_be_rearranged_as_palindrome(_SCREAMING_SNAKE_CASE ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
if __name__ == "__main__":
_lowerCamelCase = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_lowerCamelCase = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")
| 67 | 1 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = current_set.copy()
for row_index, row in enumerate(lowerCAmelCase ):
_lowerCAmelCase = row[0]
for column_index, column in enumerate(lowerCAmelCase ):
if magnitude == 0:
_lowerCAmelCase = column
continue
_lowerCAmelCase = column / magnitude
# Subtract to cancel term
_lowerCAmelCase = current_set[0]
_lowerCAmelCase = [first_row]
_lowerCAmelCase = current_set[1::]
for row in current_set:
_lowerCAmelCase = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(lowerCAmelCase )
continue
for column_index in range(len(lowerCAmelCase ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(lowerCAmelCase )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
_lowerCAmelCase = final_set[0]
_lowerCAmelCase = []
_lowerCAmelCase = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
_lowerCAmelCase = simplify(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , lowerCAmelCase )
_lowerCAmelCase = resultant
return final_set
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if len(lowerCAmelCase ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
_lowerCAmelCase = len(lowerCAmelCase ) + 1
if any(len(lowerCAmelCase ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(lowerCAmelCase , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(lowerCAmelCase ) == 1:
return [equations[0][-1] / equations[0][0]]
_lowerCAmelCase = equations.copy()
if any(0 in row for row in data_set ):
_lowerCAmelCase = data_set.copy()
_lowerCAmelCase = []
for row_index, row in enumerate(lowerCAmelCase ):
if 0 not in row:
_lowerCAmelCase = data_set.pop(lowerCAmelCase )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , lowerCAmelCase )
_lowerCAmelCase = data_set.copy()
_lowerCAmelCase = simplify(lowerCAmelCase )
_lowerCAmelCase = simplified[::-1]
_lowerCAmelCase = []
for row in simplified:
_lowerCAmelCase = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
_lowerCAmelCase = row.copy()[: len(lowerCAmelCase ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(lowerCAmelCase ) == 0:
solutions.append(0 )
continue
_lowerCAmelCase = temp_row[1::]
_lowerCAmelCase = temp_row[::-1]
for column_index, column in enumerate(lowerCAmelCase ):
current_solution -= column * solutions[column_index]
solutions.append(lowerCAmelCase )
_lowerCAmelCase = []
for item in solutions:
final.append(float(round(lowerCAmelCase , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ : Optional[Any] =[
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 70 |
'''simple docstring'''
from torch import nn
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"Unsupported activation function: {act_fn}" )
| 70 | 1 |
from functools import lru_cache
def __lowerCamelCase ( __a :int ) -> set:
"""simple docstring"""
A__ = 2
A__ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lowerCAmelCase )
if n > 1:
factors.add(__lowerCAmelCase )
return factors
@lru_cache
def __lowerCamelCase ( __a :Optional[int] ) -> int:
"""simple docstring"""
return len(unique_prime_factors(__lowerCAmelCase ) )
def __lowerCamelCase ( __a :Optional[Any] ) -> bool:
"""simple docstring"""
return len(set(__lowerCAmelCase ) ) in (0, 1)
def __lowerCamelCase ( __a :Optional[int] ) -> list:
"""simple docstring"""
A__ = 2
while True:
# Increment each value of a generated range
A__ = [base + i for i in range(__lowerCAmelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
A__ = [upf_len(__lowerCAmelCase ) for x in group]
checker.append(__lowerCAmelCase )
# If all numbers in the list are equal, return the group variable.
if equality(__lowerCAmelCase ):
return group
# Increment our base variable by 1
base += 1
def __lowerCamelCase ( __a :List[str] = 4 ) -> int:
"""simple docstring"""
A__ = run(__lowerCAmelCase )
return results[0] if len(__lowerCAmelCase ) else None
if __name__ == "__main__":
print(solution())
| 350 |
import math
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
A__ = input("""Enter message: """ )
A__ = int(input(F'Enter key [2-{len(__a ) - 1}]: ' ) )
A__ = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
A__ = encrypt_message(__a , __a )
elif mode.lower().startswith("""d""" ):
A__ = decrypt_message(__a , __a )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(F'Output:\n{text + "|"}' )
def __lowerCamelCase ( __a :int , __a :str ) -> str:
"""simple docstring"""
A__ = [""""""] * key
for col in range(__a ):
A__ = col
while pointer < len(__a ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(__a )
def __lowerCamelCase ( __a :int , __a :str ) -> str:
"""simple docstring"""
A__ = math.ceil(len(__a ) / key )
A__ = key
A__ = (num_cols * num_rows) - len(__a )
A__ = [""""""] * num_cols
A__ = 0
A__ = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
A__ = 0
row += 1
return "".join(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 276 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class lowercase__ :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=6 , __UpperCAmelCase=17 , __UpperCAmelCase=23 , __UpperCAmelCase=11 , __UpperCAmelCase=True , )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = seq_length
lowerCAmelCase__ = act_dim
lowerCAmelCase__ = state_dim
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = max_length
lowerCAmelCase__ = is_training
def UpperCAmelCase ( self )-> List[str]:
'''simple docstring'''
lowerCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowerCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowerCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCAmelCase__ = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1000 )
lowerCAmelCase__ = random_attention_mask((self.batch_size, self.seq_length) )
lowerCAmelCase__ = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = DecisionTransformerModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowerCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
(
lowerCAmelCase__
) = config_and_inputs
lowerCAmelCase__ = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class lowercase__ ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, unittest.TestCase ):
a_ =(DecisionTransformerModel,) if is_torch_available() else ()
a_ =()
a_ ={"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
a_ =False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
a_ =False
a_ =False
a_ =False
a_ =False
a_ =False
a_ =False
a_ =False
a_ =False
a_ =False
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = DecisionTransformerModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
@slow
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = DecisionTransformerModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(_UpperCAmelCase )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(_UpperCAmelCase )] , _UpperCAmelCase )
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = 2 # number of steps of autoregressive prediction we will perform
lowerCAmelCase__ = 10 # defined by the RL environment, may be normalized
lowerCAmelCase__ = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
lowerCAmelCase__ = model.to(_UpperCAmelCase )
lowerCAmelCase__ = model.config
torch.manual_seed(0 )
lowerCAmelCase__ = torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ) # env.reset()
lowerCAmelCase__ = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] , device=_UpperCAmelCase )
lowerCAmelCase__ = torch.tensor(_UpperCAmelCase , device=_UpperCAmelCase , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowerCAmelCase__ = state
lowerCAmelCase__ = torch.zeros(1 , 0 , config.act_dim , device=_UpperCAmelCase , dtype=torch.floataa )
lowerCAmelCase__ = torch.zeros(1 , 0 , device=_UpperCAmelCase , dtype=torch.floataa )
lowerCAmelCase__ = torch.tensor(0 , device=_UpperCAmelCase , dtype=torch.long ).reshape(1 , 1 )
for step in range(_UpperCAmelCase ):
lowerCAmelCase__ = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=_UpperCAmelCase )] , dim=1 )
lowerCAmelCase__ = torch.cat([rewards, torch.zeros(1 , 1 , device=_UpperCAmelCase )] , dim=1 )
lowerCAmelCase__ = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowerCAmelCase__ = model(
states=_UpperCAmelCase , actions=_UpperCAmelCase , rewards=_UpperCAmelCase , returns_to_go=_UpperCAmelCase , timesteps=_UpperCAmelCase , attention_mask=_UpperCAmelCase , return_dict=_UpperCAmelCase , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowerCAmelCase__ = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ),
1.0,
False,
{},
)
lowerCAmelCase__ = action_pred[0, -1]
lowerCAmelCase__ = torch.cat([states, state] , dim=1 )
lowerCAmelCase__ = returns_to_go[0, -1] - reward
lowerCAmelCase__ = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowerCAmelCase__ = torch.cat(
[timesteps, torch.ones((1, 1) , device=_UpperCAmelCase , dtype=torch.long ) * (step + 1)] , dim=1 )
| 340 |
"""simple docstring"""
def __A ( a_ :int = 60_08_51_47_51_43) -> int:
try:
__a : List[Any] = int(a_)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''')
__a : int = 1
__a : List[Any] = 2
while i * i <= n:
while n % i == 0:
__a : List[str] = i
n //= i
i += 1
if n > 1:
__a : Optional[int] = n
return int(a_)
if __name__ == "__main__":
print(F'{solution() = }') | 160 | 0 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> float:
__a = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def __lowerCAmelCase ( ) -> Union[str, Any]:
print(sum_of_series(1 , 1 , 10 ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 370 |
from __future__ import annotations
def __lowerCAmelCase ( a__ , a__ = None ) -> list[list[str]]:
__a = word_bank or []
# create a table
__a = len(a__ ) + 1
__a = []
for _ in range(a__ ):
table.append([] )
# seed value
__a = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(a__ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(a__ )] == word:
__a = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(a__ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(a__ )]:
combination.reverse()
return table[len(a__ )]
if __name__ == "__main__":
print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
print(
all_construct(
'hexagonosaurus',
['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
)
) | 33 | 0 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
a_ : Union[str, Any] = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
a_ : Tuple = logging.get_logger(__name__)
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = "maskformer"
_lowerCamelCase = {"hidden_size": "mask_feature_size"}
_lowerCamelCase = ["resnet", "swin"]
_lowerCamelCase = ["detr"]
def __init__( self , UpperCamelCase = 256 , UpperCamelCase = 256 , UpperCamelCase = 0.1 , UpperCamelCase = False , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = 0.02 , UpperCamelCase = 1.0 , UpperCamelCase = 1.0 , UpperCamelCase = 1.0 , UpperCamelCase = 20.0 , UpperCamelCase = None , **UpperCamelCase , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowerCamelCase_ = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = backbone_config.pop("model_type" )
lowerCamelCase_ = CONFIG_MAPPING[backbone_model_type]
lowerCamelCase_ = config_class.from_dict(UpperCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {",".join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowerCamelCase_ = DetrConfig()
else:
# verify that the decoder is supported
lowerCamelCase_ = (
decoder_config.pop("model_type" ) if isinstance(UpperCamelCase , UpperCamelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {",".join(self.decoders_supported )}''' )
if isinstance(UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = CONFIG_MAPPING[decoder_type]
lowerCamelCase_ = config_class.from_dict(UpperCamelCase )
lowerCamelCase_ = backbone_config
lowerCamelCase_ = decoder_config
# main feature dimension for the model
lowerCamelCase_ = fpn_feature_size
lowerCamelCase_ = mask_feature_size
# initializer
lowerCamelCase_ = init_std
lowerCamelCase_ = init_xavier_std
# Hungarian matcher && loss
lowerCamelCase_ = cross_entropy_weight
lowerCamelCase_ = dice_weight
lowerCamelCase_ = mask_weight
lowerCamelCase_ = use_auxiliary_loss
lowerCamelCase_ = no_object_weight
lowerCamelCase_ = output_auxiliary_logits
lowerCamelCase_ = self.decoder_config.encoder_attention_heads
lowerCamelCase_ = self.decoder_config.num_hidden_layers
super().__init__(**UpperCamelCase )
@classmethod
def snake_case ( cls , UpperCamelCase , UpperCamelCase , **UpperCamelCase ):
"""simple docstring"""
return cls(
backbone_config=UpperCamelCase , decoder_config=UpperCamelCase , **UpperCamelCase , )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = copy.deepcopy(self.__dict__ )
lowerCamelCase_ = self.backbone_config.to_dict()
lowerCamelCase_ = self.decoder_config.to_dict()
lowerCamelCase_ = self.__class__.model_type
return output
| 55 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class snake_case :
"""simple docstring"""
def __init__( self , UpperCamelCase , ):
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = 13
lowerCamelCase_ = 7
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = 99
lowerCamelCase_ = 32
lowerCamelCase_ = 2
lowerCamelCase_ = 4
lowerCamelCase_ = 37
lowerCamelCase_ = "gelu"
lowerCamelCase_ = 0.1
lowerCamelCase_ = 0.1
lowerCamelCase_ = 512
lowerCamelCase_ = 16
lowerCamelCase_ = 2
lowerCamelCase_ = 0.02
lowerCamelCase_ = 3
lowerCamelCase_ = 4
lowerCamelCase_ = None
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self ):
"""simple docstring"""
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = self.prepare_config_and_inputs()
lowerCamelCase_ = True
lowerCamelCase_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFEsmModel(config=UpperCamelCase )
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCamelCase_ = model(UpperCamelCase )
lowerCamelCase_ = [input_ids, input_mask]
lowerCamelCase_ = model(UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ):
"""simple docstring"""
lowerCamelCase_ = True
lowerCamelCase_ = TFEsmModel(config=UpperCamelCase )
lowerCamelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
lowerCamelCase_ = model(UpperCamelCase )
lowerCamelCase_ = [input_ids, input_mask]
lowerCamelCase_ = model(UpperCamelCase , encoder_hidden_states=UpperCamelCase )
# Also check the case where encoder outputs are not passed
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFEsmForMaskedLM(config=UpperCamelCase )
lowerCamelCase_ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFEsmForTokenClassification(config=UpperCamelCase )
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCamelCase_ = model(UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class snake_case ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
_lowerCamelCase = (
{
"feature-extraction": TFEsmModel,
"fill-mask": TFEsmForMaskedLM,
"text-classification": TFEsmForSequenceClassification,
"token-classification": TFEsmForTokenClassification,
"zero-shot": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFEsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFEsmModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
@unittest.skip("Protein models do not support embedding resizing." )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip("Protein models do not support embedding resizing." )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(UpperCamelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowerCamelCase_ = model.get_bias()
assert isinstance(UpperCamelCase , UpperCamelCase )
for k, v in name.items():
assert isinstance(UpperCamelCase , tf.Variable )
else:
lowerCamelCase_ = model.get_output_embeddings()
assert x is None
lowerCamelCase_ = model.get_bias()
assert name is None
@require_tf
class snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowerCamelCase_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(UpperCamelCase )[0]
lowerCamelCase_ = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , UpperCamelCase )
# compare the actual values for a slice.
lowerCamelCase_ = tf.constant(
[
[
[8.921_518, -10.589_814, -6.4_671_307],
[-6.3_967_156, -13.911_377, -1.1_211_915],
[-7.781_247, -13.951_557, -3.740_592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowerCamelCase_ = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(UpperCamelCase )[0]
# compare the actual values for a slice.
lowerCamelCase_ = tf.constant(
[
[
[0.14_443_092, 0.54_125_327, 0.3_247_739],
[0.30_340_484, 0.00_526_676, 0.31_077_722],
[0.32_278_043, -0.24_987_096, 0.3_414_628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 55 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase_ ( a_):
def __init__( self : Optional[int] ) -> Tuple:
# test for the above condition
self.test()
def _UpperCamelCase ( self : Dict ) -> Optional[int]:
_UpperCamelCase = 0
_UpperCamelCase = False
while not completed:
if counter == 1:
self.reset()
_UpperCamelCase = self.advance()
if not self.does_advance(lowercase_ ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
_UpperCamelCase = self.update(lowercase_ )
counter += 1
if counter > 1_0000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def _UpperCamelCase ( self : int ) -> List[str]:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def _UpperCamelCase ( self : int , __UpperCamelCase : int ) -> int:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : int ) -> List[Any]:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def _UpperCamelCase ( self : int ) -> Any:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def _UpperCamelCase ( self : List[str] ) -> Optional[Any]:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def _UpperCamelCase ( self : Dict , __UpperCamelCase : Any=False ) -> List[str]:
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class UpperCAmelCase_ ( a_):
def __init__( self : Optional[int] , __UpperCamelCase : List[int] ) -> Tuple:
super(lowercase_ , self ).__init__()
if not isinstance(lowercase_ , lowercase_ ) or len(lowercase_ ) == 0:
raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(lowercase_ , lowercase_ ) 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}.''' )
_UpperCamelCase = token_ids
_UpperCamelCase = len(self.token_ids )
_UpperCamelCase = -1 # the index of the currently fulfilled step
_UpperCamelCase = False
def _UpperCamelCase ( self : Union[str, Any] ) -> int:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def _UpperCamelCase ( self : Dict , __UpperCamelCase : int ) -> Dict:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(lowercase_ )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(lowercase_ )}''' )
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
if self.does_advance(lowercase_ ):
self.fulfilled_idx += 1
_UpperCamelCase = True
if self.fulfilled_idx == (self.seqlen - 1):
_UpperCamelCase = True
_UpperCamelCase = completed
else:
# failed to make progress.
_UpperCamelCase = True
self.reset()
return stepped, completed, reset
def _UpperCamelCase ( self : Optional[int] ) -> Tuple:
_UpperCamelCase = False
_UpperCamelCase = 0
def _UpperCamelCase ( self : Optional[int] ) -> Dict:
return self.seqlen - (self.fulfilled_idx + 1)
def _UpperCamelCase ( self : Optional[int] , __UpperCamelCase : Optional[Any]=False ) -> Optional[int]:
_UpperCamelCase = PhrasalConstraint(self.token_ids )
if stateful:
_UpperCamelCase = self.seqlen
_UpperCamelCase = self.fulfilled_idx
_UpperCamelCase = self.completed
return new_constraint
class UpperCAmelCase_ :
def __init__( self : str , __UpperCamelCase : List[List[int]] , __UpperCamelCase : int=True ) -> Dict:
_UpperCamelCase = max([len(lowercase_ ) for one in nested_token_ids] )
_UpperCamelCase = {}
for token_ids in nested_token_ids:
_UpperCamelCase = root
for tidx, token_id in enumerate(lowercase_ ):
if token_id not in level:
_UpperCamelCase = {}
_UpperCamelCase = level[token_id]
if no_subsets and self.has_subsets(lowercase_ , lowercase_ ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
F''' {nested_token_ids}.''' )
_UpperCamelCase = root
def _UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : List[str] ) -> Dict:
_UpperCamelCase = self.trie
for current_token in current_seq:
_UpperCamelCase = start[current_token]
_UpperCamelCase = list(start.keys() )
return next_tokens
def _UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : List[str] ) -> Any:
_UpperCamelCase = self.next_tokens(lowercase_ )
return len(lowercase_ ) == 0
def _UpperCamelCase ( self : Dict , __UpperCamelCase : List[Any] ) -> List[Any]:
_UpperCamelCase = list(root.values() )
if len(lowercase_ ) == 0:
return 1
else:
return sum([self.count_leaves(lowercase_ ) for nn in next_nodes] )
def _UpperCamelCase ( self : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ) -> Any:
_UpperCamelCase = self.count_leaves(lowercase_ )
return len(lowercase_ ) != leaf_count
class UpperCAmelCase_ ( a_):
def __init__( self : Dict , __UpperCamelCase : List[List[int]] ) -> int:
super(lowercase_ , self ).__init__()
if not isinstance(lowercase_ , lowercase_ ) or len(lowercase_ ) == 0:
raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(lowercase_ , lowercase_ ) 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(lowercase_ , lowercase_ ) 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}.''' )
_UpperCamelCase = DisjunctiveTrie(lowercase_ )
_UpperCamelCase = nested_token_ids
_UpperCamelCase = self.trie.max_height
_UpperCamelCase = []
_UpperCamelCase = False
def _UpperCamelCase ( self : Dict ) -> Dict:
_UpperCamelCase = self.trie.next_tokens(self.current_seq )
if len(lowercase_ ) == 0:
return None
else:
return token_list
def _UpperCamelCase ( self : str , __UpperCamelCase : int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase_ )}''' )
_UpperCamelCase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def _UpperCamelCase ( self : Any , __UpperCamelCase : int ) -> str:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase_ )}''' )
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
if self.does_advance(lowercase_ ):
self.current_seq.append(lowercase_ )
_UpperCamelCase = True
else:
_UpperCamelCase = True
self.reset()
_UpperCamelCase = self.trie.reached_leaf(self.current_seq )
_UpperCamelCase = completed
return stepped, completed, reset
def _UpperCamelCase ( self : Dict ) -> List[Any]:
_UpperCamelCase = False
_UpperCamelCase = []
def _UpperCamelCase ( self : Any ) -> 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 _UpperCamelCase ( self : Any , __UpperCamelCase : Union[str, Any]=False ) -> Optional[Any]:
_UpperCamelCase = DisjunctiveConstraint(self.token_ids )
if stateful:
_UpperCamelCase = self.seqlen
_UpperCamelCase = self.current_seq
_UpperCamelCase = self.completed
return new_constraint
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , __UpperCamelCase : List[Constraint] ) -> int:
_UpperCamelCase = constraints
# max # of steps required to fulfill a given constraint
_UpperCamelCase = max([c.seqlen for c in constraints] )
_UpperCamelCase = len(lowercase_ )
_UpperCamelCase = False
self.init_state()
def _UpperCamelCase ( self : str ) -> Optional[Any]:
_UpperCamelCase = []
_UpperCamelCase = None
_UpperCamelCase = [constraint.copy(stateful=lowercase_ ) for constraint in self.constraints]
def _UpperCamelCase ( self : Any ) -> Optional[int]:
_UpperCamelCase = 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 _UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
_UpperCamelCase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_UpperCamelCase = constraint.advance()
if isinstance(lowercase_ , lowercase_ ):
token_list.append(lowercase_ )
elif isinstance(lowercase_ , lowercase_ ):
token_list.extend(lowercase_ )
else:
_UpperCamelCase = self.inprogress_constraint.advance()
if isinstance(lowercase_ , lowercase_ ):
token_list.append(lowercase_ )
elif isinstance(lowercase_ , lowercase_ ):
token_list.extend(lowercase_ )
if len(lowercase_ ) == 0:
return None
else:
return token_list
def _UpperCamelCase ( self : Optional[int] , __UpperCamelCase : Optional[List[int]] ) -> Dict:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_UpperCamelCase = self.add(lowercase_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def _UpperCamelCase ( self : Tuple , __UpperCamelCase : int ) -> Dict:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )
_UpperCamelCase = False, False
if self.completed:
_UpperCamelCase = True
_UpperCamelCase = 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
_UpperCamelCase = self.inprogress_constraint.update(lowercase_ )
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=lowercase_ ) )
_UpperCamelCase = 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 )
_UpperCamelCase = None
if len(self.pending_constraints ) == 0:
# we're done!
_UpperCamelCase = 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(lowercase_ ):
_UpperCamelCase = pending_constraint.update(lowercase_ )
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(lowercase_ )
_UpperCamelCase = None
if not complete and stepped:
_UpperCamelCase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
_UpperCamelCase = (
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.
_UpperCamelCase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : Optional[Any]=True ) -> Union[str, Any]:
_UpperCamelCase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_UpperCamelCase = [
constraint.copy(stateful=lowercase_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_UpperCamelCase = self.inprogress_constraint.copy(stateful=lowercase_ )
_UpperCamelCase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 357 | """simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase_ ( _lowercase , _lowercase):
@register_to_config
def __init__( self : Tuple , __UpperCamelCase : bool , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Optional[int] = None ) -> int:
super().__init__()
_UpperCamelCase = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
_UpperCamelCase = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
_UpperCamelCase = None
_UpperCamelCase = torch.nn.Parameter(__UpperCamelCase )
class UpperCAmelCase_ ( _lowercase):
snake_case__ = 42
snake_case__ = 42
snake_case__ = 42
snake_case__ = 42
snake_case__ = 42
snake_case__ = 42
def __init__( self : List[str] , __UpperCamelCase : VQModel , __UpperCamelCase : CLIPTextModel , __UpperCamelCase : CLIPTokenizer , __UpperCamelCase : TransformeraDModel , __UpperCamelCase : VQDiffusionScheduler , __UpperCamelCase : LearnedClassifierFreeSamplingEmbeddings , ) -> Optional[int]:
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : List[str] ) -> str:
_UpperCamelCase = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
_UpperCamelCase = self.tokenizer(
__UpperCamelCase , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
_UpperCamelCase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_UpperCamelCase = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
_UpperCamelCase = text_input_ids[:, : self.tokenizer.model_max_length]
_UpperCamelCase = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
_UpperCamelCase = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
_UpperCamelCase = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
_UpperCamelCase = self.learned_classifier_free_sampling_embeddings.embeddings
_UpperCamelCase = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
_UpperCamelCase = [''''''] * batch_size
_UpperCamelCase = text_input_ids.shape[-1]
_UpperCamelCase = self.tokenizer(
__UpperCamelCase , padding='''max_length''' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='''pt''' , )
_UpperCamelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
_UpperCamelCase = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_UpperCamelCase = negative_prompt_embeds.shape[1]
_UpperCamelCase = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
_UpperCamelCase = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_UpperCamelCase = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self : List[str] , __UpperCamelCase : Union[str, List[str]] , __UpperCamelCase : int = 100 , __UpperCamelCase : float = 5.0 , __UpperCamelCase : float = 1.0 , __UpperCamelCase : int = 1 , __UpperCamelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCamelCase : Optional[torch.FloatTensor] = None , __UpperCamelCase : Optional[str] = "pil" , __UpperCamelCase : bool = True , __UpperCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __UpperCamelCase : int = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCamelCase = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCamelCase = len(__UpperCamelCase )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}''' )
_UpperCamelCase = batch_size * num_images_per_prompt
_UpperCamelCase = guidance_scale > 1.0
_UpperCamelCase = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__UpperCamelCase )}.''' )
# get the initial completely masked latents unless the user supplied it
_UpperCamelCase = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
_UpperCamelCase = self.transformer.num_vector_embeds - 1
_UpperCamelCase = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
F''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
_UpperCamelCase = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
_UpperCamelCase = self.scheduler.timesteps.to(self.device )
_UpperCamelCase = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
_UpperCamelCase = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
_UpperCamelCase = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
_UpperCamelCase , _UpperCamelCase = model_output.chunk(2 )
_UpperCamelCase = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
_UpperCamelCase = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
_UpperCamelCase = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
_UpperCamelCase = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
_UpperCamelCase = self.vqvae.config.vq_embed_dim
_UpperCamelCase = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
_UpperCamelCase = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
_UpperCamelCase = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
_UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCamelCase = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : float ) -> torch.FloatTensor:
_UpperCamelCase , _UpperCamelCase = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
_UpperCamelCase = torch.exp(__UpperCamelCase )
_UpperCamelCase = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
_UpperCamelCase = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
_UpperCamelCase = torch.cat((all_true, keep_mask) , dim=1 )
_UpperCamelCase = keep_mask[:, :-1, :]
_UpperCamelCase = keep_mask.gather(1 , indices.argsort(1 ) )
_UpperCamelCase = log_p_x_0.clone()
_UpperCamelCase = -torch.inf # -inf = log(0)
return rv
| 54 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
_a = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 194 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> str:
"""simple docstring"""
with open(__snake_case ) as metadata_file:
_UpperCamelCase = json.load(__snake_case )
_UpperCamelCase = LukeConfig(use_entity_aware_attention=__snake_case, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_UpperCamelCase = torch.load(__snake_case, map_location='''cpu''' )
# Load the entity vocab file
_UpperCamelCase = load_entity_vocab(__snake_case )
_UpperCamelCase = RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_UpperCamelCase = AddedToken('''<ent>''', lstrip=__snake_case, rstrip=__snake_case )
_UpperCamelCase = 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, LukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(__snake_case, __snake_case )
_UpperCamelCase = LukeTokenizer.from_pretrained(__snake_case )
# Initialize the embeddings of the special tokens
_UpperCamelCase = state_dict['''embeddings.word_embeddings.weight''']
_UpperCamelCase = word_emb[tokenizer.convert_tokens_to_ids(['''@'''] )[0]].unsqueeze(0 )
_UpperCamelCase = word_emb[tokenizer.convert_tokens_to_ids(['''#'''] )[0]].unsqueeze(0 )
_UpperCamelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# 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"]:
_UpperCamelCase = F'''encoder.layer.{layer_index}.attention.self.'''
_UpperCamelCase = state_dict[prefix + matrix_name]
_UpperCamelCase = state_dict[prefix + matrix_name]
_UpperCamelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_UpperCamelCase = state_dict['''entity_embeddings.entity_embeddings.weight''']
_UpperCamelCase = entity_emb[entity_vocab['''[MASK]''']]
_UpperCamelCase = LukeModel(config=__snake_case ).eval()
_UpperCamelCase , _UpperCamelCase = model.load_state_dict(__snake_case, strict=__snake_case )
if not (len(__snake_case ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(F'''Missing keys {", ".join(__snake_case )}. Expected only missing embeddings.position_ids''' )
if not (all(key.startswith('''entity_predictions''' ) or key.startswith('''lm_head''' ) for key in unexpected_keys )):
raise ValueError(
'''Unexpected keys'''
F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}''' )
# Check outputs
_UpperCamelCase = LukeTokenizer.from_pretrained(__snake_case, task='''entity_classification''' )
_UpperCamelCase = (
'''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
)
_UpperCamelCase = (39, 42)
_UpperCamelCase = tokenizer(__snake_case, entity_spans=[span], add_prefix_space=__snake_case, return_tensors='''pt''' )
_UpperCamelCase = model(**__snake_case )
# Verify word hidden states
if model_size == "large":
_UpperCamelCase = torch.Size((1, 42, 10_24) )
_UpperCamelCase = torch.tensor(
[[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
else: # base
_UpperCamelCase = torch.Size((1, 42, 7_68) )
_UpperCamelCase = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )
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":
_UpperCamelCase = torch.Size((1, 1, 10_24) )
_UpperCamelCase = torch.tensor([[0.0466, -0.0106, -0.0179]] )
else: # base
_UpperCamelCase = torch.Size((1, 1, 7_68) )
_UpperCamelCase = torch.tensor([[0.1457, 0.1044, 0.0174]] )
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
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__snake_case ) )
model.save_pretrained(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = {}
with open(__snake_case, '''r''', encoding='''utf-8''' ) as f:
for index, line in enumerate(__snake_case ):
_UpperCamelCase , _UpperCamelCase = line.rstrip().split('''\t''' )
_UpperCamelCase = index
return entity_vocab
if __name__ == "__main__":
_a = 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."""
)
_a = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 194 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class UpperCamelCase_ ( UpperCamelCase , unittest.TestCase):
"""simple docstring"""
snake_case__ : Optional[Any] = MobileBertTokenizer
snake_case__ : List[str] = MobileBertTokenizerFast
snake_case__ : Tuple = True
snake_case__ : Optional[int] = True
snake_case__ : Union[str, Any] = filter_non_english
snake_case__ : Optional[int] = "google/mobilebert-uncased"
def UpperCAmelCase_ ( self : Dict ) -> int:
super().setUp()
__SCREAMING_SNAKE_CASE = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCAmelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] ) -> str:
__SCREAMING_SNAKE_CASE = "UNwant\u00E9d,running"
__SCREAMING_SNAKE_CASE = "unwanted, running"
return input_text, output_text
def UpperCAmelCase_ ( self : List[str] ) -> List[Any]:
__SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(UpperCAmelCase__ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [9, 6, 7, 1_2, 1_0, 1_1] )
def UpperCAmelCase_ ( self : str ) -> List[Any]:
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = "UNwant\u00E9d,running"
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# With lower casing
__SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = "UNwant\u00E9d,running"
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self : str ) -> Tuple:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self : Tuple ) -> int:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
__SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=UpperCAmelCase__ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
__SCREAMING_SNAKE_CASE = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
__SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = i
__SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=UpperCAmelCase__ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def UpperCAmelCase_ ( self : int ) -> List[Any]:
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def UpperCAmelCase_ ( self : Dict ) -> int:
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(UpperCAmelCase__ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(UpperCAmelCase__ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
__SCREAMING_SNAKE_CASE = tokenizer.encode("sequence builders" , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer.encode("multi-sequence build" , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ , UpperCAmelCase__ )
assert encoded_sentence == [1_0_1] + text + [1_0_2]
assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
__SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , return_token_type_ids=UpperCAmelCase__ , return_offsets_mapping=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(UpperCAmelCase__ , "do_lower_case" ) else False
__SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "Allen"),
((2_1, 2_3), "##NL"),
((2_3, 2_4), "##P"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "allen"),
((2_1, 2_3), "##nl"),
((2_3, 2_4), "##p"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = ["的", "人", "有"]
__SCREAMING_SNAKE_CASE = "".join(UpperCAmelCase__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_p.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_p.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase__ )
# it is expected that only the first Chinese character is not preceded by "##".
__SCREAMING_SNAKE_CASE = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(UpperCAmelCase__ )
]
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 195 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Optional[int] = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
a__ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 195 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowercase ( UpperCamelCase__ ):
@slow
@require_torch
def a__ ( self ) -> Dict:
_A : int = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" , """prajjwal1/bert-tiny""" )
_A : Tuple = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_A : Dict = bertabert.config.encoder.vocab_size
_A : List[str] = tokenizer.sep_token_id
_A : Dict = tokenizer.cls_token_id
_A : List[Any] = 128
_A : Tuple = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""train[:1%]""" )
_A : List[str] = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""validation[:1%]""" )
_A : Union[str, Any] = train_dataset.select(range(32 ) )
_A : str = val_dataset.select(range(16 ) )
_A : Dict = 4
def _map_to_encoder_decoder_inputs(_a ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_A : List[Any] = tokenizer(batch["""article"""] , padding="""max_length""" , truncation=_a , max_length=512 )
_A : int = tokenizer(batch["""highlights"""] , padding="""max_length""" , truncation=_a , max_length=128 )
_A : Union[str, Any] = inputs.input_ids
_A : List[Any] = inputs.attention_mask
_A : Dict = outputs.input_ids
_A : str = outputs.input_ids.copy()
_A : Union[str, Any] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
_A : Dict = outputs.attention_mask
assert all(len(_a ) == 512 for x in inputs.input_ids )
assert all(len(_a ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_a ):
_A : int = pred.label_ids
_A : str = pred.predictions
# all unnecessary tokens are removed
_A : Dict = tokenizer.batch_decode(_a , skip_special_tokens=_a )
_A : Optional[Any] = tokenizer.batch_decode(_a , skip_special_tokens=_a )
_A : Tuple = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_a ) )] ) / len(_a )
return {"accuracy": accuracy}
# map train dataset
_A : Dict = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_a , batch_size=_a , remove_columns=["""article""", """highlights"""] , )
train_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
# same for validation dataset
_A : Optional[int] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_a , batch_size=_a , remove_columns=["""article""", """highlights"""] , )
val_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
_A : Optional[int] = self.get_auto_remove_tmp_dir()
_A : Any = SeqaSeqTrainingArguments(
output_dir=_a , per_device_train_batch_size=_a , per_device_eval_batch_size=_a , predict_with_generate=_a , evaluation_strategy="""steps""" , do_train=_a , do_eval=_a , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
_A : Optional[Any] = SeqaSeqTrainer(
model=_a , args=_a , compute_metrics=_compute_metrics , train_dataset=_a , eval_dataset=_a , tokenizer=_a , )
# start training
trainer.train()
| 26 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = 42
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : str=3 , UpperCamelCase__ : List[Any]=("DownEncoderBlock2D",) , UpperCamelCase__ : Optional[Any]=(64,) , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : Union[str, Any]=32 , UpperCamelCase__ : Optional[Any]="silu" , UpperCamelCase__ : List[str]=True , ) -> str:
"""simple docstring"""
super().__init__()
__magic_name__ = layers_per_block
__magic_name__ = torch.nn.Convad(
UpperCamelCase__ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
__magic_name__ = None
__magic_name__ = nn.ModuleList([] )
# down
__magic_name__ = block_out_channels[0]
for i, down_block_type in enumerate(UpperCamelCase__ ):
__magic_name__ = output_channel
__magic_name__ = block_out_channels[i]
__magic_name__ = i == len(UpperCamelCase__ ) - 1
__magic_name__ = get_down_block(
UpperCamelCase__ , num_layers=self.layers_per_block , in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=UpperCamelCase__ , resnet_groups=UpperCamelCase__ , attention_head_dim=UpperCamelCase__ , temb_channels=UpperCamelCase__ , )
self.down_blocks.append(UpperCamelCase__ )
# mid
__magic_name__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase__ , output_scale_factor=1 , resnet_time_scale_shift="""default""" , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCamelCase__ , temb_channels=UpperCamelCase__ , )
# out
__magic_name__ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=UpperCamelCase__ , eps=1E-6 )
__magic_name__ = nn.SiLU()
__magic_name__ = 2 * out_channels if double_z else out_channels
__magic_name__ = nn.Convad(block_out_channels[-1] , UpperCamelCase__ , 3 , padding=1 )
__magic_name__ = False
def _lowercase ( self : List[str] , UpperCamelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
__magic_name__ = x
__magic_name__ = self.conv_in(UpperCamelCase__ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCamelCase__ : int ):
def custom_forward(*UpperCamelCase__ : str ):
return module(*UpperCamelCase__ )
return custom_forward
# down
if is_torch_version(""">=""" , """1.11.0""" ):
for down_block in self.down_blocks:
__magic_name__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCamelCase__ ) , UpperCamelCase__ , use_reentrant=UpperCamelCase__ )
# middle
__magic_name__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase__ , use_reentrant=UpperCamelCase__ )
else:
for down_block in self.down_blocks:
__magic_name__ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__ ) , UpperCamelCase__ )
# middle
__magic_name__ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , UpperCamelCase__ )
else:
# down
for down_block in self.down_blocks:
__magic_name__ = down_block(UpperCamelCase__ )
# middle
__magic_name__ = self.mid_block(UpperCamelCase__ )
# post-process
__magic_name__ = self.conv_norm_out(UpperCamelCase__ )
__magic_name__ = self.conv_act(UpperCamelCase__ )
__magic_name__ = self.conv_out(UpperCamelCase__ )
return sample
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : int=3 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : List[Any]=("UpDecoderBlock2D",) , UpperCamelCase__ : List[Any]=(64,) , UpperCamelCase__ : Dict=2 , UpperCamelCase__ : int=32 , UpperCamelCase__ : Optional[int]="silu" , UpperCamelCase__ : Tuple="group" , ) -> Dict:
"""simple docstring"""
super().__init__()
__magic_name__ = layers_per_block
__magic_name__ = nn.Convad(
UpperCamelCase__ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
__magic_name__ = None
__magic_name__ = nn.ModuleList([] )
__magic_name__ = in_channels if norm_type == """spatial""" else None
# mid
__magic_name__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase__ , output_scale_factor=1 , resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCamelCase__ , temb_channels=UpperCamelCase__ , )
# up
__magic_name__ = list(reversed(UpperCamelCase__ ) )
__magic_name__ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(UpperCamelCase__ ):
__magic_name__ = output_channel
__magic_name__ = reversed_block_out_channels[i]
__magic_name__ = i == len(UpperCamelCase__ ) - 1
__magic_name__ = get_up_block(
UpperCamelCase__ , num_layers=self.layers_per_block + 1 , in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , prev_output_channel=UpperCamelCase__ , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase__ , resnet_groups=UpperCamelCase__ , attention_head_dim=UpperCamelCase__ , temb_channels=UpperCamelCase__ , resnet_time_scale_shift=UpperCamelCase__ , )
self.up_blocks.append(UpperCamelCase__ )
__magic_name__ = output_channel
# out
if norm_type == "spatial":
__magic_name__ = SpatialNorm(block_out_channels[0] , UpperCamelCase__ )
else:
__magic_name__ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=UpperCamelCase__ , eps=1E-6 )
__magic_name__ = nn.SiLU()
__magic_name__ = nn.Convad(block_out_channels[0] , UpperCamelCase__ , 3 , padding=1 )
__magic_name__ = False
def _lowercase ( self : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple=None ) -> Tuple:
"""simple docstring"""
__magic_name__ = z
__magic_name__ = self.conv_in(UpperCamelCase__ )
__magic_name__ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCamelCase__ : Optional[int] ):
def custom_forward(*UpperCamelCase__ : int ):
return module(*UpperCamelCase__ )
return custom_forward
if is_torch_version(""">=""" , """1.11.0""" ):
# middle
__magic_name__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase__ , UpperCamelCase__ , use_reentrant=UpperCamelCase__ )
__magic_name__ = sample.to(UpperCamelCase__ )
# up
for up_block in self.up_blocks:
__magic_name__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ , use_reentrant=UpperCamelCase__ )
else:
# middle
__magic_name__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = sample.to(UpperCamelCase__ )
# up
for up_block in self.up_blocks:
__magic_name__ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
else:
# middle
__magic_name__ = self.mid_block(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = sample.to(UpperCamelCase__ )
# up
for up_block in self.up_blocks:
__magic_name__ = up_block(UpperCamelCase__ , UpperCamelCase__ )
# post-process
if latent_embeds is None:
__magic_name__ = self.conv_norm_out(UpperCamelCase__ )
else:
__magic_name__ = self.conv_norm_out(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = self.conv_act(UpperCamelCase__ )
__magic_name__ = self.conv_out(UpperCamelCase__ )
return sample
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Dict="random" , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : Dict=True ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
__magic_name__ = n_e
__magic_name__ = vq_embed_dim
__magic_name__ = beta
__magic_name__ = legacy
__magic_name__ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
__magic_name__ = remap
if self.remap is not None:
self.register_buffer("""used""" , torch.tensor(np.load(self.remap ) ) )
__magic_name__ = self.used.shape[0]
__magic_name__ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__magic_name__ = self.re_embed
__magic_name__ = self.re_embed + 1
print(
F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
F'''Using {self.unknown_index} for unknown indices.''' )
else:
__magic_name__ = n_e
__magic_name__ = sane_index_shape
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = inds.shape
assert len(UpperCamelCase__ ) > 1
__magic_name__ = inds.reshape(ishape[0] , -1 )
__magic_name__ = self.used.to(UpperCamelCase__ )
__magic_name__ = (inds[:, :, None] == used[None, None, ...]).long()
__magic_name__ = match.argmax(-1 )
__magic_name__ = match.sum(2 ) < 1
if self.unknown_index == "random":
__magic_name__ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
__magic_name__ = self.unknown_index
return new.reshape(UpperCamelCase__ )
def _lowercase ( self : Optional[Any] , UpperCamelCase__ : str ) -> Tuple:
"""simple docstring"""
__magic_name__ = inds.shape
assert len(UpperCamelCase__ ) > 1
__magic_name__ = inds.reshape(ishape[0] , -1 )
__magic_name__ = self.used.to(UpperCamelCase__ )
if self.re_embed > self.used.shape[0]: # extra token
__magic_name__ = 0 # simply set to zero
__magic_name__ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , UpperCamelCase__ )
return back.reshape(UpperCamelCase__ )
def _lowercase ( self : List[str] , UpperCamelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
__magic_name__ = z.permute(0 , 2 , 3 , 1 ).contiguous()
__magic_name__ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__magic_name__ = torch.argmin(torch.cdist(UpperCamelCase__ , self.embedding.weight ) , dim=1 )
__magic_name__ = self.embedding(UpperCamelCase__ ).view(z.shape )
__magic_name__ = None
__magic_name__ = None
# compute loss for embedding
if not self.legacy:
__magic_name__ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__magic_name__ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__magic_name__ = z + (z_q - z).detach()
# reshape back to match original input shape
__magic_name__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
__magic_name__ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
__magic_name__ = self.remap_to_used(UpperCamelCase__ )
__magic_name__ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
__magic_name__ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
if self.remap is not None:
__magic_name__ = indices.reshape(shape[0] , -1 ) # add batch axis
__magic_name__ = self.unmap_to_all(UpperCamelCase__ )
__magic_name__ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__magic_name__ = self.embedding(UpperCamelCase__ )
if shape is not None:
__magic_name__ = z_q.view(UpperCamelCase__ )
# reshape back to match original input shape
__magic_name__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple=False ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = parameters
__magic_name__ , __magic_name__ = torch.chunk(UpperCamelCase__ , 2 , dim=1 )
__magic_name__ = torch.clamp(self.logvar , -30.0 , 20.0 )
__magic_name__ = deterministic
__magic_name__ = torch.exp(0.5 * self.logvar )
__magic_name__ = torch.exp(self.logvar )
if self.deterministic:
__magic_name__ = __magic_name__ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def _lowercase ( self : Tuple , UpperCamelCase__ : Optional[torch.Generator] = None ) -> torch.FloatTensor:
"""simple docstring"""
__magic_name__ = randn_tensor(
self.mean.shape , generator=UpperCamelCase__ , device=self.parameters.device , dtype=self.parameters.dtype )
__magic_name__ = self.mean + self.std * sample
return x
def _lowercase ( self : Dict , UpperCamelCase__ : Optional[int]=None ) -> Any:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict=[1, 2, 3] ) -> Optional[int]:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
__magic_name__ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=UpperCamelCase__ )
def _lowercase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.mean
| 88 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : List[Any] = LEDConfig
_lowercase : List[Any] = {}
_lowercase : int = '''gelu'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=False , _lowercase=99 , _lowercase=32 , _lowercase=2 , _lowercase=4 , _lowercase=37 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=20 , _lowercase=2 , _lowercase=1 , _lowercase=0 , _lowercase=4 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = pad_token_id
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
_lowerCAmelCase = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
_lowerCAmelCase = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_lowerCAmelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_lowerCAmelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = 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 , attention_window=self.attention_window , **self.config_updates , )
_lowerCAmelCase = prepare_led_inputs_dict(_lowercase , _lowercase , _lowercase )
_lowerCAmelCase = tf.concat(
[tf.zeros_like(_lowercase )[:, :-1], tf.ones_like(_lowercase )[:, -1:]] , axis=-1 , )
_lowerCAmelCase = global_attention_mask
return config, inputs_dict
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFLEDModel(config=_lowercase ).get_decoder()
_lowerCAmelCase = inputs_dict["""input_ids"""]
_lowerCAmelCase = input_ids[:1, :]
_lowerCAmelCase = inputs_dict["""attention_mask"""][:1, :]
_lowerCAmelCase = 1
# first forward pass
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , use_cache=_lowercase )
_lowerCAmelCase , _lowerCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCAmelCase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_lowerCAmelCase = tf.concat([input_ids, next_tokens] , axis=-1 )
_lowerCAmelCase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase )[0]
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , past_key_values=_lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_lowerCAmelCase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx]
_lowerCAmelCase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowercase , _lowercase , rtol=1e-3 )
def A (__lowerCamelCase :Dict , __lowerCamelCase :Optional[int] , __lowerCamelCase :Tuple , __lowerCamelCase :Tuple=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :List[str]=None , ):
if attention_mask is None:
_lowerCAmelCase = tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_lowerCAmelCase = 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:
_lowerCAmelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowerCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : Optional[Any] = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_lowercase : Union[str, Any] = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_lowercase : List[str] = (
{
'''conversational''': TFLEDForConditionalGeneration,
'''feature-extraction''': TFLEDModel,
'''summarization''': TFLEDForConditionalGeneration,
'''text2text-generation''': TFLEDForConditionalGeneration,
'''translation''': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowercase : List[str] = True
_lowercase : Tuple = False
_lowercase : Any = False
_lowercase : Optional[int] = False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFLEDModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = tf.zeros_like(inputs_dict["""attention_mask"""] )
_lowerCAmelCase = 2
_lowerCAmelCase = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["""global_attention_mask"""] , )
_lowerCAmelCase = True
_lowerCAmelCase = self.model_tester.seq_length
_lowerCAmelCase = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_lowercase ):
_lowerCAmelCase = outputs.decoder_attentions
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_lowercase ):
_lowerCAmelCase = [t.numpy() for t in outputs.encoder_attentions]
_lowerCAmelCase = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
_lowerCAmelCase = len(_lowercase )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
if self.is_encoder_decoder:
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_decoder_attentions_output(_lowercase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
# Check attention is always last and order is fine
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowercase ) )
self.assertEqual(model.config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self ):
"""simple docstring"""
pass
def A (__lowerCamelCase :List[Any] ):
return tf.constant(__lowerCamelCase , dtype=tf.intaa )
_lowercase = 1e-4
@slow
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ).led
# change to intended input here
_lowerCAmelCase = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
_lowerCAmelCase = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
_lowerCAmelCase = prepare_led_inputs_dict(model.config , _lowercase , _lowercase )
_lowerCAmelCase = model(**_lowercase )[0]
_lowerCAmelCase = (1, 1_024, 768)
self.assertEqual(output.shape , _lowercase )
# change to expected output here
_lowerCAmelCase = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowercase , atol=1e-3 )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" )
# change to intended input here
_lowerCAmelCase = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
_lowerCAmelCase = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
_lowerCAmelCase = prepare_led_inputs_dict(model.config , _lowercase , _lowercase )
_lowerCAmelCase = model(**_lowercase )[0]
_lowerCAmelCase = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _lowercase )
# change to expected output here
_lowerCAmelCase = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowercase , atol=1e-3 , rtol=1e-3 )
| 229 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
_lowerCAmelCase = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_lowercase , scheduler=_lowercase )
@torch.no_grad()
def __call__( self , _lowercase = 1 , _lowercase = None , _lowercase = 0.0 , _lowercase = 50 , _lowercase = None , _lowercase = "pil" , _lowercase = True , ):
"""simple docstring"""
if isinstance(self.unet.config.sample_size , _lowercase ):
_lowerCAmelCase = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
_lowerCAmelCase = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(_lowercase , _lowercase ) and len(_lowercase ) != batch_size:
raise ValueError(
F'You have passed a list of generators of length {len(_lowercase )}, but requested an effective batch'
F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
_lowerCAmelCase = randn_tensor(_lowercase , generator=_lowercase , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_lowerCAmelCase = self.unet(_lowercase , _lowercase ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_lowerCAmelCase = self.scheduler.step(
_lowercase , _lowercase , _lowercase , eta=_lowercase , use_clipped_model_output=_lowercase , generator=_lowercase ).prev_sample
_lowerCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_lowerCAmelCase = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 229 | 1 |
from __future__ import annotations
lowerCamelCase__ = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , __lowercase : dict[str, list[str]] , __lowercase : str ):
'''simple docstring'''
__a = graph
# mapping node to its parent in resulting breadth first tree
__a = {}
__a = source_vertex
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
__a = {self.source_vertex}
__a = None
__a = [self.source_vertex] # first in first out queue
while queue:
__a = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(__lowercase )
__a = vertex
queue.append(__lowercase )
def UpperCamelCase_ ( self : Optional[int] , __lowercase : str ):
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
__a = self.parent.get(__lowercase )
if target_vertex_parent is None:
__a = (
F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
)
raise ValueError(__lowercase )
return self.shortest_path(__lowercase ) + F"->{target_vertex}"
if __name__ == "__main__":
lowerCamelCase__ = Graph(graph, """G""")
g.breath_first_search()
print(g.shortest_path("""D"""))
print(g.shortest_path("""G"""))
print(g.shortest_path("""Foo"""))
| 302 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple =KandinskyVaaPriorPipeline
__lowerCamelCase : Union[str, Any] =['prompt']
__lowerCamelCase : Any =['prompt', 'negative_prompt']
__lowerCamelCase : List[str] =[
'num_images_per_prompt',
'generator',
'num_inference_steps',
'latents',
'negative_prompt',
'guidance_scale',
'output_type',
'return_dict',
]
__lowerCamelCase : List[Any] =False
@property
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return 32
@property
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
return 32
@property
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
return self.time_input_dim
@property
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return 100
@property
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
__a = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
__a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__lowercase )
@property
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
__a = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 12,
"""embedding_dim""": self.text_embedder_hidden_size,
"""num_layers""": 1,
}
__a = PriorTransformer(**__lowercase )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__a = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__a = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__a = CLIPVisionModelWithProjection(__lowercase )
return model
@property
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
__a = CLIPImageProcessor(
crop_size=224 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=224 , )
return image_processor
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
__a = self.dummy_prior
__a = self.dummy_image_encoder
__a = self.dummy_text_encoder
__a = self.dummy_tokenizer
__a = self.dummy_image_processor
__a = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1000 , clip_sample=__lowercase , clip_sample_range=10.0 , )
__a = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""scheduler""": scheduler,
"""image_processor""": image_processor,
}
return components
def UpperCamelCase_ ( self : Optional[int] , __lowercase : List[str] , __lowercase : Any=0 ):
'''simple docstring'''
if str(__lowercase ).startswith("""mps""" ):
__a = torch.manual_seed(__lowercase )
else:
__a = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__a = {
"""prompt""": """horse""",
"""generator""": generator,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
__a = """cpu"""
__a = self.get_dummy_components()
__a = self.pipeline_class(**__lowercase )
__a = pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__a = pipe(**self.get_dummy_inputs(__lowercase ) )
__a = output.image_embeds
__a = pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
__a = image[0, -10:]
__a = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__a = np.array(
[-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
__a = torch_device == """cpu"""
__a = True
__a = False
self._test_inference_batch_single_identical(
test_max_difference=__lowercase , relax_max_difference=__lowercase , test_mean_pixel_difference=__lowercase , )
@skip_mps
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
__a = torch_device == """cpu"""
__a = False
self._test_attention_slicing_forward_pass(
test_max_difference=__lowercase , test_mean_pixel_difference=__lowercase , )
| 302 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Dict = 10
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[int] = [1, 2, 3, 4]
__snake_case: str = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(A , self.block_size , 0 ) , A )
def UpperCAmelCase__ ( self : int ):
__snake_case: Any = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
__snake_case: Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(A , self.block_size , 0 ) , A )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: Optional[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
__snake_case: Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(A , self.block_size , 0 ) , A )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[Any] = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
__snake_case , __snake_case: int = process_story(A )
self.assertEqual(A , [] )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: List[str] = """"""
__snake_case , __snake_case: Optional[Any] = process_story(A )
self.assertEqual(A , [] )
self.assertEqual(A , [] )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: int = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
__snake_case , __snake_case: int = process_story(A )
__snake_case: str = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(A , A )
__snake_case: List[Any] = ["""It was the best of times."""]
self.assertEqual(A , A )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: List[str] = torch.tensor([1, 2, 3, 4] )
__snake_case: Tuple = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(A , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : str ):
__snake_case: Optional[Any] = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
__snake_case: List[str] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(A , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : str ):
__snake_case: Dict = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
__snake_case: Dict = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(A , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : str ):
__snake_case: Any = 101
__snake_case: Tuple = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
__snake_case: List[Any] = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
__snake_case: Tuple = compute_token_type_ids(A , A )
np.testing.assert_array_equal(A , A )
| 293 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self : List[Any] , A : AutoencoderKL , A : CLIPTextModel , A : CLIPTokenizer , A : UNetaDConditionModel , A : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , A : StableDiffusionSafetyChecker , A : CLIPImageProcessor , ):
super().__init__()
self.register_modules(
vae=A , text_encoder=A , tokenizer=A , unet=A , scheduler=A , safety_checker=A , feature_extractor=A , )
def UpperCAmelCase__ ( self : Optional[Any] , A : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__snake_case: Tuple = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def UpperCAmelCase__ ( self : str ):
self.enable_attention_slicing(A )
@torch.no_grad()
def __call__( self : List[str] , A : Union[str, List[str]] , A : int = 512 , A : int = 512 , A : int = 50 , A : float = 7.5 , A : Optional[Union[str, List[str]]] = None , A : Optional[int] = 1 , A : float = 0.0 , A : Optional[torch.Generator] = None , A : Optional[torch.FloatTensor] = None , A : Optional[str] = "pil" , A : bool = True , A : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A : int = 1 , A : Optional[torch.FloatTensor] = None , **A : Optional[Any] , ):
if isinstance(A , A ):
__snake_case: int = 1
elif isinstance(A , A ):
__snake_case: Optional[Any] = len(A )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(A , A ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(A )}.''' )
# get prompt text embeddings
__snake_case: Tuple = self.tokenizer(
A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
__snake_case: Any = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__snake_case: List[str] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
__snake_case: Dict = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
__snake_case: Union[str, Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__snake_case , __snake_case , __snake_case: List[Any] = text_embeddings.shape
__snake_case: Tuple = text_embeddings.repeat(1 , A , 1 )
__snake_case: Dict = text_embeddings.view(bs_embed * num_images_per_prompt , A , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__snake_case: List[str] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__snake_case: List[str]
if negative_prompt is None:
__snake_case: Any = [""""""]
elif type(A ) is not type(A ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(A )} !='''
f''' {type(A )}.''' )
elif isinstance(A , A ):
__snake_case: List[str] = [negative_prompt]
elif batch_size != len(A ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(A )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
""" the batch size of `prompt`.""" )
else:
__snake_case: str = negative_prompt
__snake_case: Any = text_input_ids.shape[-1]
__snake_case: Dict = self.tokenizer(
A , padding="""max_length""" , max_length=A , truncation=A , return_tensors="""pt""" , )
__snake_case: Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__snake_case: Optional[Any] = uncond_embeddings.shape[1]
__snake_case: str = uncond_embeddings.repeat(A , A , 1 )
__snake_case: List[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , A , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__snake_case: Any = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__snake_case: Tuple = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__snake_case: List[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
__snake_case: Optional[Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__snake_case: Any = torch.randn(
A , generator=A , device="""cpu""" , dtype=A ).to(self.device )
__snake_case: Tuple = torch.randn(A , generator=A , device="""cpu""" , dtype=A ).to(
self.device )
else:
__snake_case: Dict = torch.randn(
A , generator=A , device=self.device , dtype=A )
__snake_case: Optional[int] = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
__snake_case: Optional[int] = latents_reference.to(self.device )
__snake_case: List[str] = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
__snake_case: int = (latents_shape[3] - latents_shape_reference[3]) // 2
__snake_case: Optional[int] = (latents_shape[2] - latents_shape_reference[2]) // 2
__snake_case: int = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
__snake_case: Dict = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
__snake_case: List[Any] = 0 if dx < 0 else dx
__snake_case: Dict = 0 if dy < 0 else dy
__snake_case: List[str] = max(-dx , 0 )
__snake_case: int = max(-dy , 0 )
# import pdb
# pdb.set_trace()
__snake_case: List[Any] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__snake_case: str = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__snake_case: Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__snake_case: Optional[int] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case: int = {}
if accepts_eta:
__snake_case: Optional[Any] = eta
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
__snake_case: str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__snake_case: Dict = self.scheduler.scale_model_input(A , A )
# predict the noise residual
__snake_case: List[Any] = self.unet(A , A , encoder_hidden_states=A ).sample
# perform guidance
if do_classifier_free_guidance:
__snake_case , __snake_case: Any = noise_pred.chunk(2 )
__snake_case: Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__snake_case: str = self.scheduler.step(A , A , A , **A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A , A , A )
__snake_case: Optional[int] = 1 / 0.1_8215 * latents
__snake_case: List[Any] = self.vae.decode(A ).sample
__snake_case: str = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__snake_case: Any = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
__snake_case: List[Any] = self.feature_extractor(self.numpy_to_pil(A ) , return_tensors="""pt""" ).to(
self.device )
__snake_case , __snake_case: List[str] = self.safety_checker(
images=A , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
__snake_case: Optional[int] = None
if output_type == "pil":
__snake_case: Tuple = self.numpy_to_pil(A )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 293 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : Optional[Any] = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''owlvit_text_model'''
def __init__(self : int , _UpperCAmelCase : Dict=4_9408 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : int=2048 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : int=8 , _UpperCAmelCase : List[str]=16 , _UpperCAmelCase : int="quick_gelu" , _UpperCAmelCase : int=1E-5 , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : Tuple=1.0 , _UpperCAmelCase : Tuple=0 , _UpperCAmelCase : Tuple=4_9406 , _UpperCAmelCase : Optional[Any]=4_9407 , **_UpperCAmelCase : int , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = max_position_embeddings
lowercase__ = hidden_act
lowercase__ = layer_norm_eps
lowercase__ = attention_dropout
lowercase__ = initializer_range
lowercase__ = initializer_factor
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_UpperCAmelCase )
lowercase__ , lowercase__ = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
lowercase__ = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''owlvit_vision_model'''
def __init__(self : List[Any] , _UpperCAmelCase : int=768 , _UpperCAmelCase : Optional[int]=3072 , _UpperCAmelCase : Tuple=12 , _UpperCAmelCase : Tuple=12 , _UpperCAmelCase : Tuple=3 , _UpperCAmelCase : Tuple=768 , _UpperCAmelCase : Tuple=32 , _UpperCAmelCase : Dict="quick_gelu" , _UpperCAmelCase : List[str]=1E-5 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1.0 , **_UpperCAmelCase : List[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = hidden_act
lowercase__ = layer_norm_eps
lowercase__ = attention_dropout
lowercase__ = initializer_range
lowercase__ = initializer_factor
@classmethod
def lowerCamelCase__ (cls : Dict , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : Union[str, Any] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_UpperCAmelCase )
lowercase__ , lowercase__ = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
lowercase__ = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''owlvit'''
A__ = True
def __init__(self : Optional[Any] , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Tuple=512 , _UpperCAmelCase : Optional[int]=2.6_592 , _UpperCAmelCase : Optional[Any]=True , **_UpperCAmelCase : Optional[Any] , ) -> List[Any]:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
if text_config is None:
lowercase__ = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
lowercase__ = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
lowercase__ = OwlViTTextConfig(**_UpperCAmelCase )
lowercase__ = OwlViTVisionConfig(**_UpperCAmelCase )
lowercase__ = projection_dim
lowercase__ = logit_scale_init_value
lowercase__ = return_dict
lowercase__ = 1.0
@classmethod
def lowerCamelCase__ (cls : Tuple , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : Tuple ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_UpperCAmelCase )
lowercase__ , lowercase__ = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , **_UpperCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
lowercase__ = {}
lowercase__ = text_config
lowercase__ = vision_config
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.text_config.to_dict()
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
class A ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def lowerCamelCase__ (self : Dict ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def lowerCamelCase__ (self : Optional[int] ) -> float:
"""simple docstring"""
return 1E-4
def lowerCamelCase__ (self : Any , _UpperCAmelCase : "ProcessorMixin" , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : Optional["TensorType"] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = super().generate_dummy_inputs(
processor.tokenizer , batch_size=_UpperCAmelCase , seq_length=_UpperCAmelCase , framework=_UpperCAmelCase )
lowercase__ = super().generate_dummy_inputs(
processor.image_processor , batch_size=_UpperCAmelCase , framework=_UpperCAmelCase )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
return 14
| 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = tmp_path / """file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : str ) -> Tuple:
"""simple docstring"""
lowercase__ = tmp_path / """malformed_file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : List[str] ) -> str:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_image.csv"""
lowercase__ = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_label.csv"""
lowercase__ = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_int_list.csv"""
lowercase__ = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = Csv()
lowercase__ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__magic_name__ , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__magic_name__ ) in record.message
for record in caplog.records )
@require_pil
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
lowercase__ = csv._generate_tables([[csv_file_with_image]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
lowercase__ = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1:]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
lowercase__ = csv._generate_tables([[csv_file_with_label]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
lowercase__ = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__magic_name__ ) for label in labels]
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __magic_name__ : [int(__magic_name__ ) for i in x.split()]} )
lowercase__ = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
lowercase__ = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 305 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase = 'scheduler_config.json'
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = 1
_UpperCamelCase : List[Any] = 2
_UpperCamelCase : Any = 3
_UpperCamelCase : str = 4
_UpperCamelCase : Dict = 5
@dataclass
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : jnp.ndarray
class _UpperCamelCase :
_UpperCamelCase : str = SCHEDULER_CONFIG_NAME
_UpperCamelCase : Dict = ['''dtype''']
_UpperCamelCase : int = []
_UpperCamelCase : Optional[int] = True
@classmethod
def lowercase ( cls: Optional[Any] , _SCREAMING_SNAKE_CASE: Dict[str, Any] = None , _SCREAMING_SNAKE_CASE: Optional[str] = None , _SCREAMING_SNAKE_CASE: List[str]=False , **_SCREAMING_SNAKE_CASE: Any , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = cls.load_config(
pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE , return_unused_kwargs=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ , UpperCamelCase_ = cls.from_config(_SCREAMING_SNAKE_CASE , return_unused_kwargs=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , "create_state" ) and getattr(_SCREAMING_SNAKE_CASE , "has_state" , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Union[str, os.PathLike] , _SCREAMING_SNAKE_CASE: bool = False , **_SCREAMING_SNAKE_CASE: str ) -> List[Any]:
"""simple docstring"""
self.save_config(save_directory=_SCREAMING_SNAKE_CASE , push_to_hub=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def lowercase ( self: Dict ) -> List[str]:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def lowercase ( cls: Optional[Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ = list(set([cls.__name__] + cls._compatibles ) )
UpperCamelCase_ = importlib.import_module(__name__.split("." )[0] )
UpperCamelCase_ = [
getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
return compatible_classes
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> jnp.ndarray:
assert len(UpperCamelCase_ ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase_ ) - x.ndim) ) , UpperCamelCase_ )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_=0.9_99 , UpperCamelCase_=jnp.floataa ) -> jnp.ndarray:
def alpha_bar(UpperCamelCase_ ):
return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
UpperCamelCase_ = []
for i in range(UpperCamelCase_ ):
UpperCamelCase_ = i / num_diffusion_timesteps
UpperCamelCase_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(UpperCamelCase_ ) / alpha_bar(UpperCamelCase_ ) , UpperCamelCase_ ) )
return jnp.array(UpperCamelCase_ , dtype=UpperCamelCase_ )
@flax.struct.dataclass
class _UpperCamelCase :
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
@classmethod
def lowercase ( cls: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = scheduler.config
if config.trained_betas is not None:
UpperCamelCase_ = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
UpperCamelCase_ = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCamelCase_ = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCamelCase_ = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
UpperCamelCase_ = 1.0 - betas
UpperCamelCase_ = jnp.cumprod(_SCREAMING_SNAKE_CASE , axis=0 )
return cls(
alphas=_SCREAMING_SNAKE_CASE , betas=_SCREAMING_SNAKE_CASE , alphas_cumprod=_SCREAMING_SNAKE_CASE , )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
UpperCamelCase_ = state.alphas_cumprod
UpperCamelCase_ = alphas_cumprod[timesteps] ** 0.5
UpperCamelCase_ = sqrt_alpha_prod.flatten()
UpperCamelCase_ = broadcast_to_shape_from_left(UpperCamelCase_ , original_samples.shape )
UpperCamelCase_ = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCamelCase_ = sqrt_one_minus_alpha_prod.flatten()
UpperCamelCase_ = broadcast_to_shape_from_left(UpperCamelCase_ , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ , UpperCamelCase_ = get_sqrt_alpha_prod(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ , UpperCamelCase_ = get_sqrt_alpha_prod(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 328 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = IFImgaImgSuperResolutionPipeline
_UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
_UpperCamelCase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
_UpperCamelCase : List[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[int]=0 ) -> List[Any]:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 16, 16) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Any ) -> Union[str, Any]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowercase ( self: int ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowercase ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowercase ( self: Dict ) -> Any:
"""simple docstring"""
self._test_save_load_local()
def lowercase ( self: Any ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 328 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
if digit_amount > 0:
return round(number - int(UpperCamelCase__ ) , UpperCamelCase__ )
return number - int(UpperCamelCase__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 67 | '''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
__UpperCAmelCase =logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class a__ ( UpperCAmelCase__ ):
def __init__( self : List[str] , *a : Union[str, Any] , **a : Optional[Any] ):
"""simple docstring"""
super().__init__(*a , **a )
requires_backends(self , '''vision''' )
self.check_model_type(a )
def __call__( self : Any , a : Union[str, List[str], "Image.Image", List["Image.Image"]] , **a : Optional[int] ):
"""simple docstring"""
return super().__call__(a , **a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **a : Any ):
"""simple docstring"""
return {}, {}, {}
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : List[str] ):
"""simple docstring"""
__lowerCamelCase = load_image(a )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=a , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE__ ( self : int , a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = self.model(**a )
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : Any ):
"""simple docstring"""
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=a )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 2_55 / np.max(a )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(a )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 67 | 1 |
"""simple docstring"""
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 UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowercase (self : Optional[int]) -> Optional[int]:
__snake_case : Optional[Any] = inspect.getfile(accelerate.test_utils)
__snake_case : Dict = 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 : Tuple = test_metrics
@require_cpu
def _lowercase (self : Tuple) -> List[str]:
debug_launcher(self.test_metrics.main , num_processes=1)
@require_cpu
def _lowercase (self : List[Any]) -> Dict:
debug_launcher(self.test_metrics.main)
@require_single_gpu
def _lowercase (self : Union[str, Any]) -> Union[str, Any]:
self.test_metrics.main()
@require_multi_gpu
def _lowercase (self : Tuple) -> List[str]:
print(f"Found {torch.cuda.device_count()} devices.")
__snake_case : Any = ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1):
execute_subprocess_async(_A , env=os.environ.copy())
| 365 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : Union[str, Any]) -> Optional[int]:
__snake_case : Optional[Any] = 0
def _lowercase (self : Tuple) -> int:
__snake_case : Optional[Any] = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
self.assertIsInstance(_A , _A)
def _lowercase (self : str) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : List[str] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Any) -> Optional[int]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Any = Path(_A) / 'preprocessor_config.json'
__snake_case : List[Any] = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : List[Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__snake_case : List[Any] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A).to_dict()
config_dict.pop('image_processor_type')
__snake_case : Optional[int] = CLIPImageProcessor(**_A)
# save in new folder
model_config.save_pretrained(_A)
config.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
# make sure private variable is not incorrectly saved
__snake_case : int = json.loads(config.to_json_string())
self.assertTrue('_processor_class' not in dict_as_saved)
self.assertIsInstance(_A , _A)
def _lowercase (self : Union[str, Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : int = Path(_A) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Optional[int]) -> Dict:
with self.assertRaisesRegex(
_A , 'clip-base is not a local folder and is not a valid model identifier'):
__snake_case : Tuple = AutoImageProcessor.from_pretrained('clip-base')
def _lowercase (self : str) -> int:
with self.assertRaisesRegex(
_A , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__snake_case : str = AutoImageProcessor.from_pretrained(_A , revision='aaaaaa')
def _lowercase (self : List[Any]) -> str:
with self.assertRaisesRegex(
_A , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')
def _lowercase (self : Optional[int]) -> List[str]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_A):
__snake_case : Any = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A):
__snake_case : Tuple = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
__snake_case : Union[str, Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A , trust_remote_code=_A)
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor')
def _lowercase (self : int) -> Optional[int]:
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A):
AutoImageProcessor.register(_A , _A)
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Tuple = Path(_A) / 'preprocessor_config.json'
__snake_case : Dict = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = CustomImageProcessor.from_pretrained(_A)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase (self : List[Any]) -> Tuple:
class UpperCamelCase ( lowercase ):
UpperCAmelCase : str = True
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# If remote code is not set, the default is to use local
__snake_case : Tuple = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__snake_case : List[Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(not hasattr(_A , 'is_local'))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 95 | 0 |
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperCAmelCase__ = credit_card_number
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(__A ) - 2
for i in range(__A, -1, -2 ):
# double the value of every second digit
UpperCAmelCase__ = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
UpperCAmelCase__ = cc_number[:i] + str(__A ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A ) - 1, -1, -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperCAmelCase__ = f"""{credit_card_number} is an invalid credit card number because"""
if not credit_card_number.isdigit():
print(f"""{error_message} it has nonnumerical characters.""" )
return False
if not 13 <= len(__A ) <= 16:
print(f"""{error_message} of its length.""" )
return False
if not validate_initial_digits(__A ):
print(f"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(__A ):
print(f"""{error_message} it fails the Luhn check.""" )
return False
print(f"""{credit_card_number} is a valid credit card number.""" )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('4111111111111111')
validate_credit_card_number('32323')
| 65 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Optional[Any] = ["""flax""", """transformers"""]
def __init__( self : Union[str, Any] , *_A : Dict , **_A : Any ) -> int:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , *_A : List[Any] , **_A : Any ) -> List[str]:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : List[str] , *_A : Tuple , **_A : Optional[int] ) -> int:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Union[str, Any] = ["""flax""", """transformers"""]
def __init__( self : Union[str, Any] , *_A : Any , **_A : int ) -> List[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Union[str, Any] , *_A : Optional[int] , **_A : Dict ) -> Optional[Any]:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Tuple , *_A : Any , **_A : Union[str, Any] ) -> Dict:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Dict = ["""flax""", """transformers"""]
def __init__( self : int , *_A : Optional[int] , **_A : Any ) -> List[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Any , *_A : int , **_A : str ) -> Any:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , *_A : Union[str, Any] , **_A : List[str] ) -> Optional[int]:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Optional[int] = ["""flax""", """transformers"""]
def __init__( self : Tuple , *_A : Dict , **_A : str ) -> Optional[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : str , *_A : Dict , **_A : Optional[Any] ) -> Dict:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Any , *_A : List[str] , **_A : str ) -> Optional[int]:
requires_backends(cls , ['flax', 'transformers'] ) | 331 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : Union[str, Any] = {
"""configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
"""tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : Tuple = ["""BertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : List[str] = [
"""BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BertForMaskedLM""",
"""BertForMultipleChoice""",
"""BertForNextSentencePrediction""",
"""BertForPreTraining""",
"""BertForQuestionAnswering""",
"""BertForSequenceClassification""",
"""BertForTokenClassification""",
"""BertLayer""",
"""BertLMHeadModel""",
"""BertModel""",
"""BertPreTrainedModel""",
"""load_tf_weights_in_bert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : Tuple = [
"""TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBertEmbeddings""",
"""TFBertForMaskedLM""",
"""TFBertForMultipleChoice""",
"""TFBertForNextSentencePrediction""",
"""TFBertForPreTraining""",
"""TFBertForQuestionAnswering""",
"""TFBertForSequenceClassification""",
"""TFBertForTokenClassification""",
"""TFBertLMHeadModel""",
"""TFBertMainLayer""",
"""TFBertModel""",
"""TFBertPreTrainedModel""",
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : Optional[int] = ["""TFBertTokenizer"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : Any = [
"""FlaxBertForCausalLM""",
"""FlaxBertForMaskedLM""",
"""FlaxBertForMultipleChoice""",
"""FlaxBertForNextSentencePrediction""",
"""FlaxBertForPreTraining""",
"""FlaxBertForQuestionAnswering""",
"""FlaxBertForSequenceClassification""",
"""FlaxBertForTokenClassification""",
"""FlaxBertModel""",
"""FlaxBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 353 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
UpperCAmelCase__ : str = open # noqa: we just need to have a builtin inside this module to test it properly
| 301 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[Any]:
return x if y == 0 else greatest_common_divisor(__snake_case, x % y )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Dict:
return (x * y) // greatest_common_divisor(__snake_case, __snake_case )
def UpperCAmelCase__ ( UpperCAmelCase__ = 20 ) -> List[Any]:
A_ = 1
for i in range(1, n + 1 ):
A_ = lcm(__snake_case, __snake_case )
return g
if __name__ == "__main__":
print(f"""{solution() = }""")
| 162 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : 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'''),
}
__A : Dict = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__A : Optional[int] = '''README.md'''
def lowercase ( __snake_case : int , __snake_case : Any , __snake_case : int ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : int = f.read()
lowercase_ , lowercase_ : List[str] = REPLACE_PATTERNS[pattern]
lowercase_ : Union[str, Any] = replace.replace('''VERSION''' , __snake_case )
lowercase_ : Optional[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 : int ):
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[Any] , __snake_case : Optional[Any]=False ):
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 ( ):
lowercase_ : Union[str, Any] = '''🤗 Transformers currently provides the following architectures'''
lowercase_ : Union[str, Any] = '''1. Want to contribute a new model?'''
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : List[str] = f.readlines()
# Find the start of the list.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase_ : str = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase_ : str = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowercase ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase_ : List[Any] = f.read()
lowercase_ : List[str] = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase ( __snake_case : Optional[Any]=False ):
lowercase_ : str = 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:
lowercase_ : Optional[Any] = default_version.base_version
elif patch:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowercase_ : int = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : Dict = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case , patch=__snake_case )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowercase ( ):
lowercase_ : List[Any] = get_version()
lowercase_ : List[str] = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowercase_ : Any = current_version.base_version
# Check with the user we got that right.
lowercase_ : Tuple = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : str = 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__":
__A : int = 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.''')
__A : 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()
| 33 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
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 .scheduling_lms_discrete 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 .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 350 | """simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = '''facebook/bart-large-mnli'''
lowercase__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
lowercase__ = '''text_classifier'''
lowercase__ = AutoTokenizer
lowercase__ = AutoModelForSequenceClassification
lowercase__ = ['''text''', ['''text''']]
lowercase__ = ['''text''']
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
super().setup()
__UpperCamelCase =self.model.config
__UpperCamelCase =-1
for idx, label in config.idalabel.items():
if label.lower().startswith('''entail''' ):
__UpperCamelCase =int(UpperCamelCase__ )
if self.entailment_id == -1:
raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )
def UpperCAmelCase_ ( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any ) -> Any:
'''simple docstring'''
__UpperCamelCase =labels
return self.pre_processor(
[text] * len(UpperCamelCase__ ) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )
def UpperCAmelCase_ ( self : Optional[Any] , UpperCamelCase__ : Any ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase =outputs.logits
__UpperCamelCase =torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 85 | 0 |
"""simple docstring"""
import math
from collections.abc import Callable
def snake_case_ ( A_ : Callable[[float], float], A_ : float, A_ : float ):
'''simple docstring'''
_lowerCamelCase : float = xa
_lowerCamelCase : float = xa
while True:
if x_n == x_na or function(A_ ) == function(A_ ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
_lowerCamelCase : float = x_na - (
function(A_ ) / ((function(A_ ) - function(A_ )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
_lowerCamelCase : int = x_na
_lowerCamelCase : List[Any] = x_na
def snake_case_ ( A_ : float ):
'''simple docstring'''
return math.pow(A_, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 72 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a__ : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float , **UpperCAmelCase__ : List[str] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = feature_size
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = padding_value
__SCREAMING_SNAKE_CASE = kwargs.pop("padding_side" , "right" )
__SCREAMING_SNAKE_CASE = kwargs.pop("return_attention_mask" , UpperCAmelCase__ )
super().__init__(**UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] , UpperCAmelCase__ : Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , UpperCAmelCase__ : Union[bool, str, PaddingStrategy] = True , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(UpperCAmelCase__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__SCREAMING_SNAKE_CASE = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
__SCREAMING_SNAKE_CASE = processed_features[self.model_input_names[0]]
__SCREAMING_SNAKE_CASE = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(UpperCAmelCase__ ) == 0:
if return_attention_mask:
__SCREAMING_SNAKE_CASE = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__SCREAMING_SNAKE_CASE = required_input[0]
if isinstance(UpperCAmelCase__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__SCREAMING_SNAKE_CASE = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = "tf"
elif is_torch_tensor(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = "pt"
elif isinstance(UpperCAmelCase__ , (int, float, list, tuple, np.ndarray) ):
__SCREAMING_SNAKE_CASE = "np"
else:
raise ValueError(
F"""type of {first_element} unknown: {type(UpperCAmelCase__ )}. """
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__SCREAMING_SNAKE_CASE = to_numpy(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = [to_numpy(UpperCAmelCase__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
__SCREAMING_SNAKE_CASE = self._get_padding_strategies(padding=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = processed_features[self.model_input_names[0]]
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
if not all(len(UpperCAmelCase__ ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
__SCREAMING_SNAKE_CASE = []
for i in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = {k: v[i] for k, v in processed_features.items()}
# truncation
__SCREAMING_SNAKE_CASE = self._truncate(
UpperCAmelCase__ , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , truncation=UpperCAmelCase__ , )
truncated_inputs.append(UpperCAmelCase__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__SCREAMING_SNAKE_CASE = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__SCREAMING_SNAKE_CASE = PaddingStrategy.MAX_LENGTH
__SCREAMING_SNAKE_CASE = {}
for i in range(UpperCAmelCase__ ):
# padding
__SCREAMING_SNAKE_CASE = self._pad(
truncated_inputs[i] , max_length=UpperCAmelCase__ , padding_strategy=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , )
for key, value in outputs.items():
if key not in batch_outputs:
__SCREAMING_SNAKE_CASE = []
if value.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = value.astype(np.floataa )
batch_outputs[key].append(UpperCAmelCase__ )
return BatchFeature(UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] , UpperCAmelCase__ : Union[Dict[str, np.ndarray], BatchFeature] , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[bool] = None , ) -> dict:
__SCREAMING_SNAKE_CASE = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__SCREAMING_SNAKE_CASE = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__SCREAMING_SNAKE_CASE = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__SCREAMING_SNAKE_CASE = np.ones(len(UpperCAmelCase__ ) , dtype=np.intaa )
if needs_to_be_padded:
__SCREAMING_SNAKE_CASE = max_length - len(UpperCAmelCase__ )
if self.padding_side == "right":
if return_attention_mask:
__SCREAMING_SNAKE_CASE = np.pad(
processed_features["attention_mask"] , (0, difference) )
__SCREAMING_SNAKE_CASE = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__SCREAMING_SNAKE_CASE = np.pad(
UpperCAmelCase__ , UpperCAmelCase__ , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__SCREAMING_SNAKE_CASE = np.pad(
processed_features["attention_mask"] , (difference, 0) )
__SCREAMING_SNAKE_CASE = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__SCREAMING_SNAKE_CASE = np.pad(
UpperCAmelCase__ , UpperCAmelCase__ , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCAmelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : Union[Dict[str, np.ndarray], BatchFeature] , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[bool] = None , ) -> str:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
__SCREAMING_SNAKE_CASE = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__SCREAMING_SNAKE_CASE = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ ) > max_length
if needs_to_be_truncated:
__SCREAMING_SNAKE_CASE = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__SCREAMING_SNAKE_CASE = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCAmelCase_ ( self : Optional[int] , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : Optional[int]=None ) -> str:
# Get padding strategy
if padding is not False:
if padding is True:
__SCREAMING_SNAKE_CASE = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = PaddingStrategy(UpperCAmelCase__ )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = padding
else:
__SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 54 | 0 |
# Function to print upper half of diamond (pyramid)
def A ( a_ ) -> str:
for i in range(0 ,a_ ):
for _ in range(0 ,n - i - 1 ): # printing spaces
print(' ' ,end='' )
for _ in range(0 ,i + 1 ): # printing stars
print('* ' ,end='' )
print()
def A ( a_ ) -> Any:
for i in range(a_ ,0 ,-1 ):
for _ in range(a_ ,0 ,-1 ): # printing stars
print('* ' ,end='' )
print()
for _ in range(n - i + 1 ,0 ,-1 ): # printing spaces
print(' ' ,end='' )
def A ( a_ ) -> Union[str, Any]:
if n <= 0:
print(' ... .... nothing printing :(' )
return
floyd(a_ ) # upper half
reverse_floyd(a_ ) # lower half
if __name__ == "__main__":
print(R'''| /\ | |- | |- |--| |\ /| |-''')
print(R'''|/ \| |- |_ |_ |__| | \/ | |_''')
A_ :Optional[Any] = 1
while K:
A_ :List[Any] = int(input('''enter the number and , and see the magic : '''))
print()
pretty_print(user_number)
A_ :Optional[int] = int(input('''press 0 to exit... and 1 to continue...'''))
print('''Good Bye...''')
| 245 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def A ( a_ ,a_ ,a_ ,a_ ,a_ ) -> Optional[int]:
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCamelCase : Optional[int] =TapasConfig.from_json_file(a_ )
# set absolute/relative position embeddings parameter
__UpperCamelCase : str =reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCamelCase : Optional[Any] =TapasForQuestionAnswering(config=a_ )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCamelCase : Optional[int] =4
__UpperCamelCase : Optional[Any] =True
# hparam_utils.py hparams
__UpperCamelCase : int =0.664_694
__UpperCamelCase : Any =0.207_951
__UpperCamelCase : Tuple =0.121_194
__UpperCamelCase : List[str] =True
__UpperCamelCase : Dict =True
__UpperCamelCase : Optional[Any] =False
__UpperCamelCase : Optional[int] =0.0_352_513
__UpperCamelCase : Optional[Any] =TapasForQuestionAnswering(config=a_ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCamelCase : List[Any] =4
__UpperCamelCase : List[str] =False
# hparam_utils.py hparams
__UpperCamelCase : List[str] =36.4_519
__UpperCamelCase : Dict =0.903_421
__UpperCamelCase : List[Any] =222.088
__UpperCamelCase : Optional[Any] =True
__UpperCamelCase : Optional[int] =True
__UpperCamelCase : Dict =True
__UpperCamelCase : Dict =0.763_141
__UpperCamelCase : Union[str, Any] =TapasForQuestionAnswering(config=a_ )
elif task == "TABFACT":
__UpperCamelCase : List[Any] =TapasForSequenceClassification(config=a_ )
elif task == "MLM":
__UpperCamelCase : Optional[Any] =TapasForMaskedLM(config=a_ )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCamelCase : Optional[Any] =TapasModel(config=a_ )
else:
raise ValueError(F'Task {task} not supported.' )
print(F'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(a_ ,a_ ,a_ )
# Save pytorch-model (weights and configuration)
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(a_ )
# Save tokenizer files
print(F'Save tokenizer files to {pytorch_dump_path}' )
__UpperCamelCase : Optional[Any] =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' ,model_max_length=512 )
tokenizer.save_pretrained(a_ )
print('Used relative position embeddings:' ,model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A_ :Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS 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.'''
)
A_ :Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 245 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class A_ ( unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = StableDiffusionLDMaDPipeline
_UpperCamelCase : List[Any] = TEXT_TO_IMAGE_PARAMS
_UpperCamelCase : str = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCamelCase : int = TEXT_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE__ ( self ):
torch.manual_seed(0 )
lowercase = 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 , )
lowercase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
lowercase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowercase = CLIPTextModel(snake_case )
lowercase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowercase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=0 ):
if str(snake_case ).startswith('mps' ):
lowercase = torch.manual_seed(snake_case )
else:
lowercase = torch.Generator(device=snake_case ).manual_seed(snake_case )
lowercase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionLDMaDPipeline(**snake_case )
lowercase = ldmad_pipe.to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_dummy_inputs(snake_case )
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = rgb[0, -3:, -3:, -1]
lowercase = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowercase = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowercase = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_dummy_components()
lowercase = StableDiffusionLDMaDPipeline(**snake_case )
lowercase = ldmad_pipe.to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_dummy_inputs(snake_case )
lowercase = 3 * [inputs['prompt']]
# forward
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = rgb_slice_a[0, -3:, -3:, -1]
lowercase = depth_slice_a[0, -3:, -1]
lowercase = self.get_dummy_inputs(snake_case )
lowercase = 3 * [inputs.pop('prompt' )]
lowercase = ldmad_pipe.tokenizer(
snake_case , padding='max_length' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=snake_case , return_tensors='pt' , )
lowercase = text_inputs['input_ids'].to(snake_case )
lowercase = ldmad_pipe.text_encoder(snake_case )[0]
lowercase = prompt_embeds
# forward
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = rgb_slice_a[0, -3:, -3:, -1]
lowercase = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(skip_prk_steps=snake_case )
lowercase = StableDiffusionLDMaDPipeline(**snake_case )
lowercase = ldmad_pipe.to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_dummy_inputs(snake_case )
lowercase = 'french fries'
lowercase = ldmad_pipe(**snake_case , negative_prompt=snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = rgb[0, -3:, -3:, -1]
lowercase = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowercase = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowercase = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case="cpu" , snake_case=torch.floataa , snake_case=0 ):
lowercase = torch.Generator(device=snake_case ).manual_seed(snake_case )
lowercase = np.random.RandomState(snake_case ).standard_normal((1, 4, 64, 64) )
lowercase = torch.from_numpy(snake_case ).to(device=snake_case , dtype=snake_case )
lowercase = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
lowercase = ldmad_pipe.to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_inputs(snake_case )
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = rgb[0, -3:, -3:, -1].flatten()
lowercase = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
lowercase = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowercase = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3
@nightly
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case="cpu" , snake_case=torch.floataa , snake_case=0 ):
lowercase = torch.Generator(device=snake_case ).manual_seed(snake_case )
lowercase = np.random.RandomState(snake_case ).standard_normal((1, 4, 64, 64) )
lowercase = torch.from_numpy(snake_case ).to(device=snake_case , dtype=snake_case )
lowercase = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_inputs(snake_case )
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = 0.495_586
lowercase = 0.33_795_515
lowercase = 112.48_518
lowercase = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(snake_case )
ldmad_pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_inputs(snake_case )
lowercase = ldmad_pipe(**snake_case )
lowercase , lowercase = output.rgb, output.depth
lowercase = 0.4_194_127
lowercase = 0.35_375_586
lowercase = 0.5_638_502
lowercase = 0.34_686_103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
| 195 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
UpperCAmelCase = '''Create a default config file for Accelerate with only a few flags set.'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE="no" , __SCREAMING_SNAKE_CASE = default_json_config_file , __SCREAMING_SNAKE_CASE = False ):
lowercase = Path(__SCREAMING_SNAKE_CASE )
path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
lowercase = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
lowercase = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
lowercase = torch.cuda.device_count()
lowercase = num_gpus
lowercase = False
if num_gpus > 1:
lowercase = 'MULTI_GPU'
else:
lowercase = 'NO'
elif is_xpu_available() and use_xpu:
lowercase = torch.xpu.device_count()
lowercase = num_xpus
lowercase = False
if num_xpus > 1:
lowercase = 'MULTI_XPU'
else:
lowercase = 'NO'
elif is_npu_available():
lowercase = torch.npu.device_count()
lowercase = num_npus
lowercase = False
if num_npus > 1:
lowercase = 'MULTI_NPU'
else:
lowercase = 'NO'
else:
lowercase = 0
lowercase = True
lowercase = 1
lowercase = 'NO'
lowercase = ClusterConfig(**__SCREAMING_SNAKE_CASE )
config.to_json_file(__SCREAMING_SNAKE_CASE )
return path
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = parser.add_parser('default' , parents=__SCREAMING_SNAKE_CASE , help=__SCREAMING_SNAKE_CASE , formatter_class=__SCREAMING_SNAKE_CASE )
parser.add_argument(
'--config_file' , default=__SCREAMING_SNAKE_CASE , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=__SCREAMING_SNAKE_CASE , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=__SCREAMING_SNAKE_CASE )
return parser
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 195 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __UpperCamelCase ( lowercase__ ):
lowercase : Union[str, Any] = (DPMSolverSDEScheduler,)
lowercase : Dict = 1_0
def a__ ( self :Union[str, Any] ,**_UpperCamelCase :Optional[Any] ):
snake_case_ : Union[str, Any] = {
"""num_train_timesteps""": 1_1_0_0,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**_UpperCamelCase )
return config
def a__ ( self :int ):
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=_UpperCamelCase )
def a__ ( self :Tuple ):
for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,[0.00_02, 0.0_02, 0.02] ):
self.check_over_configs(beta_start=_UpperCamelCase ,beta_end=_UpperCamelCase )
def a__ ( self :Optional[int] ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_UpperCamelCase )
def a__ ( self :Optional[int] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_UpperCamelCase )
def a__ ( self :Optional[Any] ):
snake_case_ : Any = self.scheduler_classes[0]
snake_case_ : Dict = self.get_scheduler_config()
snake_case_ : str = scheduler_class(**_UpperCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
snake_case_ : Dict = self.dummy_model()
snake_case_ : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
snake_case_ : Any = sample.to(_UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
snake_case_ : List[Any] = scheduler.scale_model_input(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : Tuple = model(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : List[Any] = scheduler.step(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
snake_case_ : Any = output.prev_sample
snake_case_ : Tuple = torch.sum(torch.abs(_UpperCamelCase ) )
snake_case_ : List[str] = torch.mean(torch.abs(_UpperCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5 ) < 1E-2
assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6 ) < 1E-2
assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52 ) < 1E-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1E-2
assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26 ) < 1E-3
def a__ ( self :str ):
snake_case_ : str = self.scheduler_classes[0]
snake_case_ : Any = self.get_scheduler_config(prediction_type="""v_prediction""" )
snake_case_ : Tuple = scheduler_class(**_UpperCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
snake_case_ : Optional[int] = self.dummy_model()
snake_case_ : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
snake_case_ : int = sample.to(_UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
snake_case_ : List[str] = scheduler.scale_model_input(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : int = model(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : Tuple = scheduler.step(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
snake_case_ : str = output.prev_sample
snake_case_ : Dict = torch.sum(torch.abs(_UpperCamelCase ) )
snake_case_ : Optional[Any] = torch.mean(torch.abs(_UpperCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3 ) < 1E-2
assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3 ) < 1E-2
assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97 ) < 1E-3
else:
assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5 ) < 1E-2
assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21 ) < 1E-3
def a__ ( self :Any ):
snake_case_ : Tuple = self.scheduler_classes[0]
snake_case_ : List[str] = self.get_scheduler_config()
snake_case_ : Optional[Any] = scheduler_class(**_UpperCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=_UpperCamelCase )
snake_case_ : Optional[Any] = self.dummy_model()
snake_case_ : Optional[int] = self.dummy_sample_deter.to(_UpperCamelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
snake_case_ : Dict = scheduler.scale_model_input(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : str = model(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : List[str] = scheduler.step(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
snake_case_ : List[str] = output.prev_sample
snake_case_ : Dict = torch.sum(torch.abs(_UpperCamelCase ) )
snake_case_ : str = torch.mean(torch.abs(_UpperCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8 ) < 1E-2
assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2 ) < 1E-2
assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71 ) < 1E-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1E-2
assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26 ) < 1E-3
def a__ ( self :Optional[Any] ):
snake_case_ : Dict = self.scheduler_classes[0]
snake_case_ : Any = self.get_scheduler_config()
snake_case_ : List[str] = scheduler_class(**_UpperCamelCase ,use_karras_sigmas=_UpperCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=_UpperCamelCase )
snake_case_ : Dict = self.dummy_model()
snake_case_ : Tuple = self.dummy_sample_deter.to(_UpperCamelCase ) * scheduler.init_noise_sigma
snake_case_ : List[Any] = sample.to(_UpperCamelCase )
for t in scheduler.timesteps:
snake_case_ : Any = scheduler.scale_model_input(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : int = model(_UpperCamelCase ,_UpperCamelCase )
snake_case_ : List[Any] = scheduler.step(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
snake_case_ : Any = output.prev_sample
snake_case_ : Optional[Any] = torch.sum(torch.abs(_UpperCamelCase ) )
snake_case_ : Dict = torch.mean(torch.abs(_UpperCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2
else:
assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2 | 361 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : str = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
__A : Optional[Any] = {
'facebook/blenderbot_small-90M': 512,
}
class __UpperCamelCase ( lowercase__ ):
lowercase : str = VOCAB_FILES_NAMES
lowercase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict = BlenderbotSmallTokenizer
def __init__( self :str ,_UpperCamelCase :Optional[int]=None ,_UpperCamelCase :Union[str, Any]=None ,_UpperCamelCase :Tuple="<|endoftext|>" ,_UpperCamelCase :int="<|endoftext|>" ,_UpperCamelCase :Dict="<|endoftext|>" ,_UpperCamelCase :Optional[Any]=False ,_UpperCamelCase :List[Any]=True ,**_UpperCamelCase :Any ,):
super().__init__(
ByteLevelBPETokenizer(
vocab=_UpperCamelCase ,merges=_UpperCamelCase ,add_prefix_space=_UpperCamelCase ,trim_offsets=_UpperCamelCase ,) ,bos_token=_UpperCamelCase ,eos_token=_UpperCamelCase ,unk_token=_UpperCamelCase ,**_UpperCamelCase ,)
snake_case_ : Any = add_prefix_space
def a__ ( self :Optional[Any] ,_UpperCamelCase :int ,_UpperCamelCase :Optional[Any]=None ):
snake_case_ : List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def a__ ( self :int ,_UpperCamelCase :List[int] ,_UpperCamelCase :Optional[List[int]] = None ):
snake_case_ : int = [self.sep_token_id]
snake_case_ : Tuple = [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] | 8 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class _lowercase :
'''simple docstring'''
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str]=13 , SCREAMING_SNAKE_CASE__ : Any=7 , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : str=True , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : Tuple=99 , SCREAMING_SNAKE_CASE__ : List[str]=64 , SCREAMING_SNAKE_CASE__ : Tuple=5 , SCREAMING_SNAKE_CASE__ : List[str]=4 , SCREAMING_SNAKE_CASE__ : int=37 , SCREAMING_SNAKE_CASE__ : Any="gelu" , SCREAMING_SNAKE_CASE__ : int=0.1 , SCREAMING_SNAKE_CASE__ : List[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=5_12 , SCREAMING_SNAKE_CASE__ : str=16 , SCREAMING_SNAKE_CASE__ : List[Any]=2 , SCREAMING_SNAKE_CASE__ : List[str]=0.0_2 , SCREAMING_SNAKE_CASE__ : Optional[int]=3 , SCREAMING_SNAKE_CASE__ : Any=4 , SCREAMING_SNAKE_CASE__ : List[Any]=None , ) -> int:
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_input_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_labels
__lowerCAmelCase = num_choices
__lowerCAmelCase = scope
__lowerCAmelCase = vocab_size - 1
def a ( self : str ) -> Any:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_input_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def a ( self : List[Any] ) -> Tuple:
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def a ( self : Tuple ) -> Any:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase = True
return config, input_ids, input_mask, token_labels
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
__lowerCAmelCase = GPTNeoXModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict ) -> Any:
__lowerCAmelCase = True
__lowerCAmelCase = GPTNeoXModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[str]:
__lowerCAmelCase = GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str ) -> List[Any]:
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a ( self : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Optional[int]:
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple ) -> Union[str, Any]:
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a ( self : int , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Tuple:
__lowerCAmelCase = True
__lowerCAmelCase = GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
# first forward pass
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , use_cache=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = output_from_no_past["""hidden_states"""][0]
__lowerCAmelCase = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , past_key_values=SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , )["""hidden_states"""][0]
# select random slice
__lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-3 ) )
def a ( self : List[Any] ) -> int:
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = (GPTNeoXForCausalLM,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE : int = (
{
"""feature-extraction""": GPTNeoXModel,
"""question-answering""": GPTNeoXForQuestionAnswering,
"""text-classification""": GPTNeoXForSequenceClassification,
"""text-generation""": GPTNeoXForCausalLM,
"""token-classification""": GPTNeoXForTokenClassification,
"""zero-shot""": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : Dict = False
_SCREAMING_SNAKE_CASE : Tuple = False
_SCREAMING_SNAKE_CASE : List[Any] = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
def a ( self : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase = GPTNeoXModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=64 , num_attention_heads=8 )
def a ( self : List[str] ) -> Optional[int]:
self.config_tester.run_common_tests()
def a ( self : int ) -> Optional[Any]:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def a ( self : int ) -> Optional[int]:
# This regression test was failing with PyTorch < 1.3
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
__lowerCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def a ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[int] ) -> Tuple:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__ )
def a ( self : Union[str, Any] ) -> int:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def a ( self : List[str] ) -> Dict:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def a ( self : str ) -> Optional[int]:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@unittest.skip(reason="""Feed forward chunking is not implemented""" )
def a ( self : str ) -> List[str]:
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def a ( self : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
__lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__lowerCAmelCase = GPTNeoXModel(SCREAMING_SNAKE_CASE__ )
original_model.to(SCREAMING_SNAKE_CASE__ )
original_model.eval()
__lowerCAmelCase = original_model(SCREAMING_SNAKE_CASE__ ).last_hidden_state
__lowerCAmelCase = original_model(SCREAMING_SNAKE_CASE__ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__lowerCAmelCase = {"""type""": scaling_type, """factor""": 1_0.0}
__lowerCAmelCase = GPTNeoXModel(SCREAMING_SNAKE_CASE__ )
scaled_model.to(SCREAMING_SNAKE_CASE__ )
scaled_model.eval()
__lowerCAmelCase = scaled_model(SCREAMING_SNAKE_CASE__ ).last_hidden_state
__lowerCAmelCase = scaled_model(SCREAMING_SNAKE_CASE__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-5 ) )
@require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def a ( self : Dict ) -> List[Any]:
__lowerCAmelCase = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
for checkpointing in [True, False]:
__lowerCAmelCase = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE__ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
__lowerCAmelCase = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
__lowerCAmelCase = model.generate(**SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ , max_new_tokens=20 )
__lowerCAmelCase = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )[0]
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 229 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
def UpperCamelCase_ ( snake_case_ : float ) -> float:
'''simple docstring'''
if velocity > c:
raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!""" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("""Speed must be greater than or equal to 1!""" )
return velocity / c
def UpperCamelCase_ ( snake_case_ : float ) -> float:
'''simple docstring'''
return 1 / sqrt(1 - beta(snake_case_ ) ** 2 )
def UpperCamelCase_ ( snake_case_ : float ) -> np.ndarray:
'''simple docstring'''
return np.array(
[
[gamma(snake_case_ ), -gamma(snake_case_ ) * beta(snake_case_ ), 0, 0],
[-gamma(snake_case_ ) * beta(snake_case_ ), gamma(snake_case_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def UpperCamelCase_ ( snake_case_ : float , snake_case_ : np.ndarray | None = None ) -> np.ndarray:
'''simple docstring'''
if event is None:
__lowerCAmelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(snake_case_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
_A : str = transform(29979245)
print('''Example of four vector: ''')
print(f'ct\' = {four_vector[0]}')
print(f'x\' = {four_vector[1]}')
print(f'y\' = {four_vector[2]}')
print(f'z\' = {four_vector[3]}')
# Substitute symbols with numerical values
_A : int = {ct: c, x: 1, y: 1, z: 1}
_A : Any = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'\n{numerical_vector}')
| 229 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class snake_case :
def __init__( self , __UpperCAmelCase , ) ->Union[str, Any]:
a_ = parent
a_ = 13
a_ = 7
a_ = True
a_ = True
a_ = True
a_ = 99
a_ = 32
a_ = 2
a_ = 4
a_ = 37
a_ = "gelu"
a_ = 0.1
a_ = 0.1
a_ = 5_12
a_ = 16
a_ = 2
a_ = 0.02
a_ = 3
a_ = 4
a_ = None
def UpperCAmelCase__ ( self) ->List[str]:
a_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a_ = None
if self.use_input_mask:
a_ = random_attention_mask([self.batch_size, self.seq_length])
a_ = None
a_ = None
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a_ = ids_tensor([self.batch_size] , self.num_choices)
a_ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self) ->str:
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) = self.prepare_config_and_inputs()
a_ = True
a_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
a_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->List[Any]:
a_ = TFEsmModel(config=__UpperCAmelCase)
a_ = {"input_ids": input_ids, "attention_mask": input_mask}
a_ = model(__UpperCAmelCase)
a_ = [input_ids, input_mask]
a_ = model(__UpperCAmelCase)
a_ = model(__UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) ->Tuple:
a_ = True
a_ = TFEsmModel(config=__UpperCAmelCase)
a_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
a_ = model(__UpperCAmelCase)
a_ = [input_ids, input_mask]
a_ = model(__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase)
# Also check the case where encoder outputs are not passed
a_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->str:
a_ = TFEsmForMaskedLM(config=__UpperCAmelCase)
a_ = model([input_ids, input_mask])
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Union[str, Any]:
a_ = self.num_labels
a_ = TFEsmForTokenClassification(config=__UpperCAmelCase)
a_ = {"input_ids": input_ids, "attention_mask": input_mask}
a_ = model(__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCAmelCase__ ( self) ->List[Any]:
a_ = self.prepare_config_and_inputs()
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) = config_and_inputs
a_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class snake_case ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
a_ : int = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
a_ : Union[str, Any] = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
a_ : Dict = False
a_ : Union[str, Any] = False
def UpperCAmelCase__ ( self) ->Any:
a_ = TFEsmModelTester(self)
a_ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37)
def UpperCAmelCase__ ( self) ->str:
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self) ->Dict:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Tuple:
a_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->str:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->str:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase)
@slow
def UpperCAmelCase__ ( self) ->Union[str, Any]:
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = TFEsmModel.from_pretrained(__UpperCAmelCase)
self.assertIsNotNone(__UpperCAmelCase)
@unittest.skip("Protein models do not support embedding resizing.")
def UpperCAmelCase__ ( self) ->Any:
pass
@unittest.skip("Protein models do not support embedding resizing.")
def UpperCAmelCase__ ( self) ->Union[str, Any]:
pass
def UpperCAmelCase__ ( self) ->Any:
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(__UpperCAmelCase)
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer)
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
a_ = model.get_bias()
assert isinstance(__UpperCAmelCase , __UpperCAmelCase)
for k, v in name.items():
assert isinstance(__UpperCAmelCase , tf.Variable)
else:
a_ = model.get_output_embeddings()
assert x is None
a_ = model.get_bias()
assert name is None
@require_tf
class snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self) ->str:
a_ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
a_ = tf.constant([[0, 1, 2, 3, 4, 5]])
a_ = model(__UpperCAmelCase)[0]
a_ = [1, 6, 33]
self.assertEqual(list(output.numpy().shape) , __UpperCAmelCase)
# compare the actual values for a slice.
a_ = tf.constant(
[
[
[8.921_518, -10.589_814, -6.4_671_307],
[-6.3_967_156, -13.911_377, -1.1_211_915],
[-7.781_247, -13.951_557, -3.740_592],
]
])
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2))
@slow
def UpperCAmelCase__ ( self) ->List[str]:
a_ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
a_ = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
a_ = model(__UpperCAmelCase)[0]
# compare the actual values for a slice.
a_ = tf.constant(
[
[
[0.14_443_092, 0.54_125_327, 0.3_247_739],
[0.30_340_484, 0.00_526_676, 0.31_077_722],
[0.32_278_043, -0.24_987_096, 0.3_414_628],
]
])
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4)) | 303 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_ = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_ = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class snake_case :
def __init__( self) ->Optional[int]:
a_ = WATERMARK_BITS
a_ = WatermarkEncoder()
self.encoder.set_watermark("bits" , self.watermark)
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->Optional[int]:
# can't encode images that are smaller than 256
if images.shape[-1] < 2_56:
return images
a_ = (2_55 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1).float().numpy()
a_ = [self.encoder.encode(__UpperCAmelCase , "dwtDct") for image in images]
a_ = torch.from_numpy(np.array(__UpperCAmelCase)).permute(0 , 3 , 1 , 2)
a_ = torch.clamp(2 * (images / 2_55 - 0.5) , min=-1.0 , max=1.0)
return images | 303 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Union[List[PIL.Image.Image], np.ndarray]
__magic_name__ :Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(""">=""", """0.0.12""")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :np.ndarray
__magic_name__ :List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 293 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowerCAmelCase ( yaml.SafeLoader ):
"""simple docstring"""
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = [self.constructed_objects[key_node] for key_node, _ in node.value]
lowerCAmelCase__ :str = [tuple(__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else key for key in keys]
lowerCAmelCase__ :Optional[int] = Counter(__UpperCAmelCase )
lowerCAmelCase__ :int = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}" )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = super().construct_mapping(__UpperCAmelCase , deep=__UpperCAmelCase )
self._check_no_duplicates_on_constructed_node(__UpperCAmelCase )
return mapping
def __A (_SCREAMING_SNAKE_CASE ) ->Tuple[Optional[str], str]:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
lowerCAmelCase__ :Optional[int] = full_content[1:].index('---' ) + 1
lowerCAmelCase__ :Union[str, Any] = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_SCREAMING_SNAKE_CASE )
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :List[str] = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ , lowerCAmelCase__ :Union[str, Any] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__UpperCAmelCase )
else:
return cls()
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
if path.exists():
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ :Optional[Any] = readme_file.read()
else:
lowerCAmelCase__ :Union[str, Any] = None
lowerCAmelCase__ :Union[str, Any] = self._to_readme(__UpperCAmelCase )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase = None ):
'''simple docstring'''
if readme_content is not None:
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = _split_yaml_from_readme(__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = '---\n' + self.to_yaml_string() + '---\n' + content
else:
lowerCAmelCase__ :str = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = yaml.load(__UpperCAmelCase , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
lowerCAmelCase__ :int = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__UpperCAmelCase , allow_unicode=__UpperCAmelCase , encoding='utf-8' , ).decode('utf-8' )
__A = {
"""image-classification""": [],
"""translation""": [],
"""image-segmentation""": [],
"""fill-mask""": [],
"""automatic-speech-recognition""": [],
"""token-classification""": [],
"""sentence-similarity""": [],
"""audio-classification""": [],
"""question-answering""": [],
"""summarization""": [],
"""zero-shot-classification""": [],
"""table-to-text""": [],
"""feature-extraction""": [],
"""other""": [],
"""multiple-choice""": [],
"""text-classification""": [],
"""text-to-image""": [],
"""text2text-generation""": [],
"""zero-shot-image-classification""": [],
"""tabular-classification""": [],
"""tabular-regression""": [],
"""image-to-image""": [],
"""tabular-to-text""": [],
"""unconditional-image-generation""": [],
"""text-retrieval""": [],
"""text-to-speech""": [],
"""object-detection""": [],
"""audio-to-audio""": [],
"""text-generation""": [],
"""conversational""": [],
"""table-question-answering""": [],
"""visual-question-answering""": [],
"""image-to-text""": [],
"""reinforcement-learning""": [],
"""voice-activity-detection""": [],
"""time-series-forecasting""": [],
"""document-question-answering""": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__A = ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
ap.add_argument("""readme_filepath""")
__A = ap.parse_args()
__A = Path(args.readme_filepath)
__A = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 293 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class A_ ( unittest.TestCase ):
def _lowerCAmelCase (self :Tuple )-> int:
__A = inspect.getfile(accelerate.test_utils )
__A = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
__A = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def _lowerCAmelCase (self :int )-> str:
__A = f"""
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
""".split()
__A = [sys.executable] + distributed_args
execute_subprocess_async(_UpperCamelCase , env=os.environ.copy() )
| 250 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Union[str, Any] = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotSmallConfig',
'BlenderbotSmallOnnxConfig',
],
'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = ['BlenderbotSmallTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : int = [
'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotSmallForCausalLM',
'BlenderbotSmallForConditionalGeneration',
'BlenderbotSmallModel',
'BlenderbotSmallPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = [
'TFBlenderbotSmallForConditionalGeneration',
'TFBlenderbotSmallModel',
'TFBlenderbotSmallPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : str = [
'FlaxBlenderbotSmallForConditionalGeneration',
'FlaxBlenderbotSmallModel',
'FlaxBlenderbotSmallPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
snake_case__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 250 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowercase__ : str = "scheduler_config.json"
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
_snake_case = 1
_snake_case = 2
_snake_case = 3
_snake_case = 4
_snake_case = 5
@dataclass
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
_snake_case = 42
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
_snake_case = SCHEDULER_CONFIG_NAME
_snake_case = ['dtype']
_snake_case = []
_snake_case = True
@classmethod
def A__ ( cls , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , )-> str:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = cls.load_config(
pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_ , subfolder=SCREAMING_SNAKE_CASE_ , return_unused_kwargs=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
__UpperCamelCase , __UpperCamelCase = cls.from_config(SCREAMING_SNAKE_CASE_ , return_unused_kwargs=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if hasattr(SCREAMING_SNAKE_CASE_ , '''create_state''' ) and getattr(SCREAMING_SNAKE_CASE_ , '''has_state''' , SCREAMING_SNAKE_CASE_ ):
__UpperCamelCase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , **SCREAMING_SNAKE_CASE_ )-> Tuple:
'''simple docstring'''
self.save_config(save_directory=SCREAMING_SNAKE_CASE_ , push_to_hub=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@property
def A__ ( self )-> Dict:
'''simple docstring'''
return self._get_compatibles()
@classmethod
def A__ ( cls )-> str:
'''simple docstring'''
__UpperCamelCase = list(set([cls.__name__] + cls._compatibles ) )
__UpperCamelCase = importlib.import_module(__name__.split('''.''' )[0] )
__UpperCamelCase = [
getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
return compatible_classes
def A_ ( snake_case : jnp.ndarray , snake_case : Tuple[int] ) -> jnp.ndarray:
'''simple docstring'''
assert len(snake_case ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case ) - x.ndim) ) , snake_case )
def A_ ( snake_case : int , snake_case : Optional[Any]=0.999 , snake_case : Dict=jnp.floataa ) -> jnp.ndarray:
'''simple docstring'''
def alpha_bar(snake_case : Optional[Any] ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
__UpperCamelCase = []
for i in range(snake_case ):
__UpperCamelCase = i / num_diffusion_timesteps
__UpperCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(snake_case ) / alpha_bar(snake_case ) , snake_case ) )
return jnp.array(snake_case , dtype=snake_case )
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
_snake_case = 42
_snake_case = 42
_snake_case = 42
@classmethod
def A__ ( cls , SCREAMING_SNAKE_CASE_ )-> Optional[int]:
'''simple docstring'''
__UpperCamelCase = scheduler.config
if config.trained_betas is not None:
__UpperCamelCase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__UpperCamelCase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__UpperCamelCase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__UpperCamelCase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}" )
__UpperCamelCase = 1.0 - betas
__UpperCamelCase = jnp.cumprod(SCREAMING_SNAKE_CASE_ , axis=0 )
return cls(
alphas=SCREAMING_SNAKE_CASE_ , betas=SCREAMING_SNAKE_CASE_ , alphas_cumprod=SCREAMING_SNAKE_CASE_ , )
def A_ ( snake_case : CommonSchedulerState , snake_case : jnp.ndarray , snake_case : jnp.ndarray , snake_case : jnp.ndarray ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase = state.alphas_cumprod
__UpperCamelCase = alphas_cumprod[timesteps] ** 0.5
__UpperCamelCase = sqrt_alpha_prod.flatten()
__UpperCamelCase = broadcast_to_shape_from_left(snake_case , original_samples.shape )
__UpperCamelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
__UpperCamelCase = sqrt_one_minus_alpha_prod.flatten()
__UpperCamelCase = broadcast_to_shape_from_left(snake_case , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def A_ ( snake_case : CommonSchedulerState , snake_case : jnp.ndarray , snake_case : jnp.ndarray , snake_case : jnp.ndarray ) -> str:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = get_sqrt_alpha_prod(snake_case , snake_case , snake_case , snake_case )
__UpperCamelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def A_ ( snake_case : CommonSchedulerState , snake_case : jnp.ndarray , snake_case : jnp.ndarray , snake_case : jnp.ndarray ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = get_sqrt_alpha_prod(snake_case , snake_case , snake_case , snake_case )
__UpperCamelCase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 328 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase__ : List[str] = 1_6
lowercase__ : str = 3_2
def A_ ( snake_case : Accelerator , snake_case : int = 16 ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__UpperCamelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(snake_case : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
__UpperCamelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case , max_length=snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__UpperCamelCase = datasets.map(
snake_case , batched=snake_case , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__UpperCamelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(snake_case : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__UpperCamelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__UpperCamelCase = 16
elif accelerator.mixed_precision != "no":
__UpperCamelCase = 8
else:
__UpperCamelCase = None
return tokenizer.pad(
snake_case , padding='''longest''' , max_length=snake_case , pad_to_multiple_of=snake_case , return_tensors='''pt''' , )
# Instantiate dataloaders.
__UpperCamelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
__UpperCamelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowercase__ : Union[str, Any] = mocked_dataloaders # noqa: F811
def A_ ( snake_case : List[str] , snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , snake_case ) == "1":
__UpperCamelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
__UpperCamelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
__UpperCamelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__UpperCamelCase = config['''lr''']
__UpperCamelCase = int(config['''num_epochs'''] )
__UpperCamelCase = int(config['''seed'''] )
__UpperCamelCase = int(config['''batch_size'''] )
set_seed(snake_case )
__UpperCamelCase , __UpperCamelCase = get_dataloaders(snake_case , snake_case )
__UpperCamelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
__UpperCamelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__UpperCamelCase = batch_size // MAX_GPU_BATCH_SIZE
__UpperCamelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__UpperCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=snake_case )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__UpperCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__UpperCamelCase = AdamW(params=model.parameters() , lr=snake_case )
# Instantiate scheduler
__UpperCamelCase = get_linear_schedule_with_warmup(
optimizer=snake_case , num_warmup_steps=100 , num_training_steps=(len(snake_case ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = accelerator.prepare(
snake_case , snake_case , snake_case , snake_case , snake_case )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
__UpperCamelCase = os.path.split(snake_case )[-1].split('''.''' )[0]
accelerator.init_trackers(snake_case , snake_case )
# Now we train the model
for epoch in range(snake_case ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
__UpperCamelCase = 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 )
__UpperCamelCase = model(**snake_case )
__UpperCamelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
__UpperCamelCase = loss / gradient_accumulation_steps
accelerator.backward(snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
__UpperCamelCase = model(**snake_case )
__UpperCamelCase = outputs.logits.argmax(dim=-1 )
__UpperCamelCase , __UpperCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=snake_case , references=snake_case , )
__UpperCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , snake_case )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(snake_case ),
'''epoch''': epoch,
} , step=snake_case , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def A_ ( ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=snake_case , default=snake_case , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=snake_case , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
__UpperCamelCase = parser.parse_args()
__UpperCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(snake_case , snake_case )
if __name__ == "__main__":
main()
| 328 | 1 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowercase_ = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
"""name""",
"""cache_dir""",
"""data_dir""",
"""all_configs""",
"""save_infos""",
"""ignore_verifications""",
"""force_redownload""",
"""clear_cache""",
],
defaults=[None, None, None, False, False, False, False, False],
)
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
return (abs(source - target ) / target) < 0.0_1
@pytest.mark.integration
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ = _TestCommandArgs(dataset=_SCREAMING_SNAKE_CASE , all_configs=_SCREAMING_SNAKE_CASE , save_infos=_SCREAMING_SNAKE_CASE )
lowercase__ = TestCommand(*_SCREAMING_SNAKE_CASE )
test_command.run()
lowercase__ = os.path.join(_SCREAMING_SNAKE_CASE , 'README.md' )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
lowercase__ = DatasetInfosDict.from_directory(_SCREAMING_SNAKE_CASE )
lowercase__ = DatasetInfosDict(
{
'default': DatasetInfo(
features=Features(
{
'tokens': Sequence(Value('string' ) ),
'ner_tags': Sequence(
ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] ) ),
'langs': Sequence(Value('string' ) ),
'spans': Sequence(Value('string' ) ),
} ) , splits=[
{
'name': 'train',
'num_bytes': 2351563,
'num_examples': 10000,
},
{
'name': 'validation',
'num_bytes': 238418,
'num_examples': 1000,
},
] , download_size=3940680 , dataset_size=2589981 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
lowercase__ , lowercase__ = getattr(dataset_infos['default'] , _SCREAMING_SNAKE_CASE ), getattr(expected_dataset_infos['default'] , _SCREAMING_SNAKE_CASE )
if key == "num_bytes":
assert is_apercent_close(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif key == "splits":
assert list(_SCREAMING_SNAKE_CASE ) == list(_SCREAMING_SNAKE_CASE )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 269 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowercase_ = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_UpperCamelCase : Optional[str] = field(
default='NER' , metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_UpperCamelCase : bool = field(default=UpperCAmelCase , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : str = field(
metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} , )
_UpperCamelCase : int = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_UpperCamelCase : bool = field(
default=UpperCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def __UpperCamelCase () -> str:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
' --overwrite_output_dir to overcome.' )
lowercase__ = import_module('tasks' )
try:
lowercase__ = getattr(_SCREAMING_SNAKE_CASE , model_args.task_type )
lowercase__ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , _SCREAMING_SNAKE_CASE )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ = token_classification_task.get_labels(data_args.labels )
lowercase__ = dict(enumerate(_SCREAMING_SNAKE_CASE ) )
lowercase__ = len(_SCREAMING_SNAKE_CASE )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE )} , cache_dir=model_args.cache_dir , )
lowercase__ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ = (
TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ = (
TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[List[int], List[int]]:
lowercase__ = np.argmax(_SCREAMING_SNAKE_CASE , axis=2 )
lowercase__ , lowercase__ = preds.shape
lowercase__ = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
lowercase__ = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(_SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ , lowercase__ = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"precision": precision_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"recall": recall_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"f1": fa_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
}
# Data collator
lowercase__ = DataCollatorWithPadding(_SCREAMING_SNAKE_CASE , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=_SCREAMING_SNAKE_CASE , eval_dataset=_SCREAMING_SNAKE_CASE , compute_metrics=_SCREAMING_SNAKE_CASE , data_collator=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowercase__ = trainer.evaluate()
lowercase__ = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
writer.write('%s = %s\n' % (key, value) )
results.update(_SCREAMING_SNAKE_CASE )
# Predict
if training_args.do_predict:
lowercase__ = TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ = trainer.predict(_SCREAMING_SNAKE_CASE )
lowercase__ , lowercase__ = align_predictions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowercase__ = os.path.join(training_args.output_dir , 'test_results.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
for key, value in metrics.items():
logger.info(' %s = %s' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
writer.write('%s = %s\n' % (key, value) )
# Save predictions
lowercase__ = os.path.join(training_args.output_dir , 'test_predictions.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
with open(os.path.join(data_args.data_dir , 'test.txt' ) , 'r' ) as f:
token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return results
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Any:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
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