code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
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from __future__ import annotations
def a__ ( _UpperCamelCase : list[int] ,_UpperCamelCase : list[int] ,_UpperCamelCase : list[int] ,_UpperCamelCase : list[list[str]] ,_UpperCamelCase : int ,):
__lowerCamelCase = len(_UpperCamelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(_UpperCamelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] ,[*diagonal_right_collisions, row - col] ,[*diagonal_left_collisions, row + col] ,_UpperCamelCase ,_UpperCamelCase ,)
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = []
depth_first_search([] ,[] ,[] ,_UpperCamelCase ,_UpperCamelCase )
# Print all the boards
for board in boards:
for column in board:
print(_UpperCamelCase )
print('''''' )
print(len(_UpperCamelCase ) ,'''solutions were found.''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """visual_bert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=512 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = visual_embedding_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = bypass_transformer
__lowerCamelCase = special_visual_initialize
| 622 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
__lowerCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
__lowerCamelCase = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
__lowerCamelCase = torch.tensor(
[[-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]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCAmelCase , atol=1E-3 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
__lowerCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
__lowerCamelCase = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
__lowerCamelCase = torch.tensor(
[[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCAmelCase , atol=1E-3 ) )
| 622 |
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 __lowerCAmelCase ( lowerCAmelCase__ ):
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 , ):
'''simple docstring'''
# 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.
__lowerCamelCase = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
__lowerCamelCase = {} 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 , )
__lowerCamelCase = do_lower_case
__lowerCamelCase = remove_space
__lowerCamelCase = keep_accents
__lowerCamelCase = vocab_file
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {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 ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.remove_space:
__lowerCamelCase = ''' '''.join(inputs.strip().split() )
else:
__lowerCamelCase = inputs
__lowerCamelCase = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__lowerCamelCase = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
__lowerCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
__lowerCamelCase = outputs.lower()
return outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.preprocess_text(__UpperCAmelCase )
__lowerCamelCase = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
__lowerCamelCase = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__lowerCamelCase = 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:
__lowerCamelCase = cur_pieces[1:]
else:
__lowerCamelCase = 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 ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = ''''''
__lowerCamelCase = 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
__lowerCamelCase = True
__lowerCamelCase = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__lowerCamelCase = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = 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:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 622 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for a, b in zip(__UpperCAmelCase , __UpperCAmelCase ):
self.assertAlmostEqual(__UpperCAmelCase , __UpperCAmelCase , delta=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__UpperCAmelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = None
ops.enable_eager_execution_internal()
__lowerCamelCase = tf.config.list_physical_devices('''CPU''' )
if len(__UpperCAmelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__lowerCamelCase = tf.config.list_logical_devices(device_type='''CPU''' )
__lowerCamelCase = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__lowerCamelCase = GradientAccumulator()
__lowerCamelCase = tf.Variable([4.0, 3.0] )
__lowerCamelCase ,__lowerCamelCase = create_optimizer(5E-5 , 10 , 5 )
__lowerCamelCase = tf.Variable([0.0, 0.0] , trainable=__UpperCAmelCase )
def accumulate_on_replica(__UpperCAmelCase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__UpperCAmelCase , __UpperCAmelCase ):
with strategy.scope():
__lowerCamelCase = strategy.experimental_local_results(__UpperCAmelCase )
local_variables[0].assign(__UpperCAmelCase )
local_variables[1].assign(__UpperCAmelCase )
strategy.run(__UpperCAmelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__UpperCAmelCase )
def _check_local_values(__UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __UpperCAmelCase , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , __UpperCAmelCase , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 1 |
from __future__ import annotations
a_ = [True] * 1_000_001
a_ = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a_ = False
i += 1
def a__ ( _UpperCamelCase : int ):
return seive[n]
def a__ ( _UpperCamelCase : int ):
return any(digit in '''02468''' for digit in str(_UpperCamelCase ) )
def a__ ( _UpperCamelCase : int = 1_00_00_00 ):
__lowerCamelCase = [2] # result already includes the number 2.
for num in range(3 ,limit + 1 ,2 ):
if is_prime(_UpperCamelCase ) and not contains_an_even_digit(_UpperCamelCase ):
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(_UpperCamelCase ) )]
if all(is_prime(_UpperCamelCase ) for i in list_nums ):
result.append(_UpperCamelCase )
return result
def a__ ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''ylacombe/bark-small'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = '''en_speaker_1'''
__lowerCamelCase = '''This is a test string'''
__lowerCamelCase = '''speaker_embeddings_path.json'''
__lowerCamelCase = '''speaker_embeddings'''
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = BarkProcessor(tokenizer=__UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
__lowerCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__lowerCamelCase = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
__lowerCamelCase = 35
__lowerCamelCase = 2
__lowerCamelCase = 8
__lowerCamelCase = {
'''semantic_prompt''': np.ones(__UpperCAmelCase ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
__lowerCamelCase = processor(text=self.input_string , voice_preset=__UpperCAmelCase )
__lowerCamelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
__lowerCamelCase = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = processor(text=self.input_string , voice_preset=__UpperCAmelCase )
__lowerCamelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
__lowerCamelCase = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = BarkProcessor(tokenizer=__UpperCAmelCase )
__lowerCamelCase = processor(text=self.input_string )
__lowerCamelCase = tokenizer(
self.input_string , padding='''max_length''' , max_length=256 , add_special_tokens=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 622 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """lxmert"""
lowerCAmelCase__ = {}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=9500 , __UpperCAmelCase=1600 , __UpperCAmelCase=400 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=9 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=2048 , __UpperCAmelCase=4 , __UpperCAmelCase=6.67 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = num_qa_labels
__lowerCamelCase = num_object_labels
__lowerCamelCase = num_attr_labels
__lowerCamelCase = l_layers
__lowerCamelCase = x_layers
__lowerCamelCase = r_layers
__lowerCamelCase = visual_feat_dim
__lowerCamelCase = visual_pos_dim
__lowerCamelCase = visual_loss_normalizer
__lowerCamelCase = task_matched
__lowerCamelCase = task_mask_lm
__lowerCamelCase = task_obj_predict
__lowerCamelCase = task_qa
__lowerCamelCase = visual_obj_loss
__lowerCamelCase = visual_attr_loss
__lowerCamelCase = visual_feat_loss
__lowerCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__UpperCAmelCase )
| 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = XLMProphetNetTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''[PAD]'''
__lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__UpperCAmelCase ) , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''Hello World!'''
__lowerCamelCase = [35389, 6672, 49, 2]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 622 | 1 |
def a__ ( _UpperCamelCase : int ):
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
__lowerCamelCase = 4
__lowerCamelCase = (1 << p) - 1
for _ in range(p - 2 ):
__lowerCamelCase = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
a_ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = None
# source code of `config_class`
__lowerCamelCase = inspect.getsource(_UpperCamelCase )
__lowerCamelCase = _re_checkpoint.findall(_UpperCamelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('''/''' ):
__lowerCamelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
__lowerCamelCase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__lowerCamelCase = ckpt_name
break
return checkpoint
def a__ ( ):
__lowerCamelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
__lowerCamelCase = get_checkpoint_from_config_class(_UpperCamelCase )
__lowerCamelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
__lowerCamelCase = '''\n'''.join(sorted(_UpperCamelCase ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 622 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
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 ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = KandinskyVaaInpaintPipeline
lowerCAmelCase__ = ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
lowerCAmelCase__ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
lowerCAmelCase__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowerCAmelCase__ = False
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 100
@property
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__lowerCamelCase = UNetaDConditionModel(**__UpperCAmelCase )
return model
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.dummy_unet
__lowerCamelCase = self.dummy_movq
__lowerCamelCase = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.00_085 , beta_end=0.012 , clip_sample=__UpperCAmelCase , set_alpha_to_one=__UpperCAmelCase , steps_offset=1 , prediction_type='''epsilon''' , thresholding=__UpperCAmelCase , )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__UpperCAmelCase )
# create init_image
__lowerCamelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCamelCase = Image.fromarray(np.uinta(__UpperCAmelCase ) ).convert('''RGB''' ).resize((256, 256) )
# create mask
__lowerCamelCase = np.ones((64, 64) , dtype=np.floataa )
__lowerCamelCase = 0
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = self.pipeline_class(**__UpperCAmelCase )
__lowerCamelCase = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = pipe(**self.get_dummy_inputs(__UpperCAmelCase ) )
__lowerCamelCase = output.images
__lowerCamelCase = pipe(
**self.get_dummy_inputs(__UpperCAmelCase ) , return_dict=__UpperCAmelCase , )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array(
[0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848] )
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()}"""
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''' )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
__lowerCamelCase = np.ones((768, 768) , dtype=np.floataa )
__lowerCamelCase = 0
__lowerCamelCase = '''a hat'''
__lowerCamelCase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__UpperCAmelCase )
__lowerCamelCase = KandinskyVaaInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder-inpaint''' , torch_dtype=torch.floataa )
__lowerCamelCase = pipeline.to(__UpperCAmelCase )
pipeline.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
__lowerCamelCase ,__lowerCamelCase = pipe_prior(
__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__lowerCamelCase = pipeline(
image=__UpperCAmelCase , mask_image=__UpperCAmelCase , image_embeds=__UpperCAmelCase , negative_image_embeds=__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )
__lowerCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__UpperCAmelCase , __UpperCAmelCase )
| 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def a__ ( _UpperCamelCase : Callable[[int | float], int | float] ,_UpperCamelCase : int | float ,_UpperCamelCase : int | float ,_UpperCamelCase : int = 1_00 ,):
__lowerCamelCase = x_start
__lowerCamelCase = fnc(_UpperCamelCase )
__lowerCamelCase = 0.0
for _ in range(_UpperCamelCase ):
# Approximates curve as a sequence of linear lines and sums their length
__lowerCamelCase = (x_end - x_start) / steps + xa
__lowerCamelCase = fnc(_UpperCamelCase )
length += math.hypot(xa - xa ,fxa - fxa )
# Increment step
__lowerCamelCase = xa
__lowerCamelCase = fxa
return length
if __name__ == "__main__":
def a__ ( _UpperCamelCase : Optional[int] ):
return math.sin(10 * x )
print("""f(x) = sin(10 * x)""")
print("""The length of the curve from x = -10 to x = 10 is:""")
a_ = 10
while i <= 100_000:
print(f"With {i} steps: {line_length(f, -10, 10, i)}")
i *= 10
| 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
from __future__ import annotations
from math import pow, sqrt
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ,_UpperCamelCase : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance == 0:
return {"resistance": sqrt(pow(_UpperCamelCase ,2 ) - pow(_UpperCamelCase ,2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(_UpperCamelCase ,2 ) - pow(_UpperCamelCase ,2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(_UpperCamelCase ,2 ) + pow(_UpperCamelCase ,2 ) )}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 622 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 622 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 | 1 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = [1]
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, 0
__lowerCamelCase = ugly_nums[ia] * 2
__lowerCamelCase = ugly_nums[ia] * 3
__lowerCamelCase = ugly_nums[ia] * 5
for _ in range(1 ,_UpperCamelCase ):
__lowerCamelCase = min(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
ugly_nums.append(_UpperCamelCase )
if next_num == next_a:
ia += 1
__lowerCamelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__lowerCamelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__lowerCamelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(200) = }")
| 622 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : List[str] ,_UpperCamelCase : List[Any]=None ,_UpperCamelCase : Any=None ):
if attention_mask is None:
__lowerCamelCase = tf.cast(tf.math.not_equal(_UpperCamelCase ,config.pad_token_id ) ,tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __lowerCAmelCase :
lowerCAmelCase__ = OPTConfig
lowerCAmelCase__ = {}
lowerCAmelCase__ = """gelu"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=20 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = embed_dim
__lowerCamelCase = word_embed_proj_dim
__lowerCamelCase = False
def lowerCamelCase ( 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 = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__UpperCAmelCase , **self.config_updates , )
__lowerCamelCase = prepare_opt_inputs_dict(__UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel(config=__UpperCAmelCase )
__lowerCamelCase = inputs_dict['''input_ids''']
__lowerCamelCase = input_ids[:1, :]
__lowerCamelCase = inputs_dict['''attention_mask'''][:1, :]
__lowerCamelCase = 1
# first forward pass
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
__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(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__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(__UpperCAmelCase , __UpperCAmelCase , rtol=1E-3 )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__lowerCamelCase = model_class(config=__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__lowerCamelCase = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __UpperCAmelCase )
# check that weights remain the same after resizing
__lowerCamelCase = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __UpperCAmelCase )
__lowerCamelCase = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
def a__ ( _UpperCamelCase : Optional[Any] ):
return tf.constant(_UpperCamelCase ,dtype=tf.intaa )
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = 9_9
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__lowerCamelCase = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__lowerCamelCase = input_ids.shape[0]
__lowerCamelCase = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel.from_pretrained('''facebook/opt-350m''' )
__lowerCamelCase = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase = tf.not_equal(__UpperCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
__lowerCamelCase = model(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = (1, 11, 512)
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = tf.constant(
[[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-3 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = xla_generate(__UpperCAmelCase , __UpperCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = '''facebook/opt-350m'''
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(self.path_model )
__lowerCamelCase = GPTaTokenizer.from_pretrained(self.path_model )
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__lowerCamelCase = tf.constant(
[
[1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
[-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
[0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
[6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
] )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-125m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = '''left'''
# use different length sentences to test batching
__lowerCamelCase = [
'''Hello, my dog is a little''',
'''Today, I''',
]
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase )
__lowerCamelCase = inputs['''input_ids''']
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , attention_mask=inputs['''attention_mask'''] )
__lowerCamelCase = tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase )
__lowerCamelCase = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa ) )
__lowerCamelCase = tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , max_length=model.config.max_length - num_paddings )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , [non_padded_sentence, padded_sentence] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 | 1 |
from __future__ import annotations
import requests
a_ = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : int = 1 ,_UpperCamelCase : str = "new" ,_UpperCamelCase : list | None = None ):
__lowerCamelCase = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(_UpperCamelCase ) - valid_terms ) ):
__lowerCamelCase = F"""Invalid search term: {invalid_search_terms}"""
raise ValueError(_UpperCamelCase )
__lowerCamelCase = requests.get(
F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""" ,headers={'''User-agent''': '''A random string'''} ,)
if response.status_code == 4_29:
raise requests.HTTPError
__lowerCamelCase = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(_UpperCamelCase )}
__lowerCamelCase = {}
for id_ in range(_UpperCamelCase ):
__lowerCamelCase = {
item: data['''data''']['''children'''][id_]['''data'''][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ = logging.getLogger(__name__)
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[Any] ):
__lowerCamelCase = np.argmax(_UpperCamelCase ,axis=1 )
return np.sum(outputs == labels )
def a__ ( _UpperCamelCase : Optional[int] ):
with open(_UpperCamelCase ,encoding='''utf_8''' ) as f:
__lowerCamelCase = csv.reader(_UpperCamelCase )
__lowerCamelCase = []
next(_UpperCamelCase ) # skip the first line
for line in tqdm(_UpperCamelCase ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Dict ,_UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Dict ):
__lowerCamelCase = []
for dataset in encoded_datasets:
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
__lowerCamelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCamelCase ):
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = mc_label
__lowerCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,default='''openai-gpt''' ,help='''pretrained model name''' )
parser.add_argument('''--do_train''' ,action='''store_true''' ,help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' ,action='''store_true''' ,help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42 )
parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3 )
parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8 )
parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16 )
parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1 )
parser.add_argument(
'''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) ,)
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,)
parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5 )
parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01 )
parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9 )
parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74 )
parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
__lowerCamelCase = parser.parse_args()
print(_UpperCamelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
__lowerCamelCase = torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase ,_UpperCamelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase = ['''_start_''', '''_delimiter_''', '''_classify_''']
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCamelCase )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCamelCase ) )
model.to(_UpperCamelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCamelCase : Dict ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase ) )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
return obj
return [tokenize_and_encode(_UpperCamelCase ) for o in obj]
logger.info('''Encoding dataset...''' )
__lowerCamelCase = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase = (train_dataset, eval_dataset)
__lowerCamelCase = tokenize_and_encode(_UpperCamelCase )
# Compute the max input length for the Transformer
__lowerCamelCase = model.config.n_positions // 2 - 2
__lowerCamelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase = min(_UpperCamelCase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase = pre_process_datasets(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,*_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = RandomSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size )
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = SequentialSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase = args.max_steps
__lowerCamelCase = args.max_steps // (len(_UpperCamelCase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase = len(_UpperCamelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase = list(model.named_parameters() )
__lowerCamelCase = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
__lowerCamelCase = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
__lowerCamelCase = AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon )
__lowerCamelCase = get_linear_schedule_with_warmup(
_UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase )
if args.do_train:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc='''Epoch''' ):
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(_UpperCamelCase ,desc='''Training''' )
for step, batch in enumerate(_UpperCamelCase ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
__lowerCamelCase = model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase = '''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase = model.module if hasattr(_UpperCamelCase ,'''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
torch.save(model_to_save.state_dict() ,_UpperCamelCase )
model_to_save.config.to_json_file(_UpperCamelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCamelCase )
if args.do_eval:
model.eval()
__lowerCamelCase ,__lowerCamelCase = 0, 0
__lowerCamelCase ,__lowerCamelCase = 0, 0
for batch in tqdm(_UpperCamelCase ,desc='''Evaluating''' ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
with torch.no_grad():
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = model(
_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = mc_logits.detach().cpu().numpy()
__lowerCamelCase = mc_labels.to('''cpu''' ).numpy()
__lowerCamelCase = accuracy(_UpperCamelCase ,_UpperCamelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase = eval_loss / nb_eval_steps
__lowerCamelCase = eval_accuracy / nb_eval_examples
__lowerCamelCase = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
__lowerCamelCase = os.path.join(args.output_dir ,'''eval_results.txt''' )
with open(_UpperCamelCase ,'''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' ,_UpperCamelCase ,str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 622 | 1 |
def a__ ( _UpperCamelCase : dict ):
__lowerCamelCase = set()
# edges = list of graph's edges
__lowerCamelCase = get_edges(_UpperCamelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__lowerCamelCase ,__lowerCamelCase = edges.pop()
chosen_vertices.add(_UpperCamelCase )
chosen_vertices.add(_UpperCamelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_UpperCamelCase )
return chosen_vertices
def a__ ( _UpperCamelCase : dict ):
__lowerCamelCase = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 622 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAmelCase=1024 , __UpperCAmelCase=3.6 ):
'''simple docstring'''
__lowerCamelCase = tokenizer
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = dataset
__lowerCamelCase = seq_length
__lowerCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = iter(self.dataset )
__lowerCamelCase = True
while more_examples:
__lowerCamelCase ,__lowerCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__UpperCAmelCase )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
__lowerCamelCase = False
break
__lowerCamelCase = tokenizer(__UpperCAmelCase , truncation=__UpperCAmelCase )['''input_ids''']
__lowerCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__UpperCAmelCase ) , self.seq_length ):
__lowerCamelCase = all_token_ids[i : i + self.seq_length]
if len(__UpperCAmelCase ) == self.seq_length:
yield torch.tensor(__UpperCAmelCase )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = {'''streaming''': True}
__lowerCamelCase = load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase )
__lowerCamelCase = ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=args.batch_size )
return eval_dataloader
def a__ ( _UpperCamelCase : str ):
model.eval()
__lowerCamelCase = []
for step, batch in enumerate(_UpperCamelCase ):
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase ,labels=_UpperCamelCase )
__lowerCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__lowerCamelCase = torch.mean(torch.cat(_UpperCamelCase ) )
try:
__lowerCamelCase = torch.exp(_UpperCamelCase )
except OverflowError:
__lowerCamelCase = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
a_ = Accelerator()
# Parse configuration
a_ = HfArgumentParser(EvaluationArguments)
a_ = parser.parse_args()
set_seed(args.seed)
# Logging
a_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
a_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
a_ , a_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_ , a_ = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 622 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = CpmAntTokenizer
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = [
'''<d>''',
'''</d>''',
'''<s>''',
'''</s>''',
'''</_>''',
'''<unk>''',
'''<pad>''',
'''</n>''',
'''我''',
'''是''',
'''C''',
'''P''',
'''M''',
'''A''',
'''n''',
'''t''',
]
__lowerCamelCase = 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] ) )
@tooslow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''' )
__lowerCamelCase = '''今天天气真好！'''
__lowerCamelCase = ['''今天''', '''天气''', '''真''', '''好''', '''！''']
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = '''今天天气真好！'''
__lowerCamelCase = [tokenizer.bos_token] + tokens
__lowerCamelCase = [6, 9802, 14962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """lxmert"""
lowerCAmelCase__ = {}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=9500 , __UpperCAmelCase=1600 , __UpperCAmelCase=400 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=9 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=2048 , __UpperCAmelCase=4 , __UpperCAmelCase=6.67 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = num_qa_labels
__lowerCamelCase = num_object_labels
__lowerCamelCase = num_attr_labels
__lowerCamelCase = l_layers
__lowerCamelCase = x_layers
__lowerCamelCase = r_layers
__lowerCamelCase = visual_feat_dim
__lowerCamelCase = visual_pos_dim
__lowerCamelCase = visual_loss_normalizer
__lowerCamelCase = task_matched
__lowerCamelCase = task_mask_lm
__lowerCamelCase = task_obj_predict
__lowerCamelCase = task_qa
__lowerCamelCase = visual_obj_loss
__lowerCamelCase = visual_attr_loss
__lowerCamelCase = visual_feat_loss
__lowerCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__UpperCAmelCase )
| 622 | 1 |
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
a_ = logging.get_logger(__name__)
a_ = {
"""Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """marian"""
lowerCAmelCase__ = ["""past_key_values"""]
lowerCAmelCase__ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , __UpperCAmelCase=58101 , __UpperCAmelCase=None , __UpperCAmelCase=1024 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase="gelu" , __UpperCAmelCase=1024 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.02 , __UpperCAmelCase=58100 , __UpperCAmelCase=False , __UpperCAmelCase=58100 , __UpperCAmelCase=0 , __UpperCAmelCase=0 , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = decoder_vocab_size or vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = d_model
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , decoder_start_token_id=__UpperCAmelCase , forced_eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def lowerCamelCase ( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowerCamelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__lowerCamelCase = {0: '''batch'''}
__lowerCamelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
__lowerCamelCase = {0: '''batch''', 1: '''decoder_sequence'''}
__lowerCamelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
__lowerCamelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__lowerCamelCase ,__lowerCamelCase = self.num_layers
for i in range(__UpperCAmelCase ):
__lowerCamelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
__lowerCamelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
__lowerCamelCase = 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
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def lowerCamelCase ( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowerCamelCase = super().outputs
else:
__lowerCamelCase = super(__UpperCAmelCase , self ).outputs
if self.use_past:
__lowerCamelCase ,__lowerCamelCase = self.num_layers
for i in range(__UpperCAmelCase ):
__lowerCamelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
__lowerCamelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Generate decoder inputs
__lowerCamelCase = seq_length if not self.use_past else 1
__lowerCamelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
__lowerCamelCase = dict(**__UpperCAmelCase , **__UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__lowerCamelCase ,__lowerCamelCase = common_inputs['''input_ids'''].shape
__lowerCamelCase = common_inputs['''decoder_input_ids'''].shape[1]
__lowerCamelCase ,__lowerCamelCase = self.num_attention_heads
__lowerCamelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowerCamelCase = decoder_seq_length + 3
__lowerCamelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__lowerCamelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase )] , dim=1 )
__lowerCamelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__lowerCamelCase ,__lowerCamelCase = self.num_layers
__lowerCamelCase = min(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = max(__UpperCAmelCase , __UpperCAmelCase ) - min_num_layers
__lowerCamelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__UpperCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
) )
# TODO: test this.
__lowerCamelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__UpperCAmelCase , __UpperCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) )
return common_inputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__lowerCamelCase ,__lowerCamelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__lowerCamelCase = seqlen + 2
__lowerCamelCase ,__lowerCamelCase = self.num_layers
__lowerCamelCase ,__lowerCamelCase = self.num_attention_heads
__lowerCamelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowerCamelCase = common_inputs['''attention_mask'''].dtype
__lowerCamelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
__lowerCamelCase = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(__UpperCAmelCase )
]
return common_inputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
'''simple docstring'''
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
__lowerCamelCase = compute_effective_axis_dimension(
__UpperCAmelCase , 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
__lowerCamelCase = tokenizer.num_special_tokens_to_add(__UpperCAmelCase )
__lowerCamelCase = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
__lowerCamelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
__lowerCamelCase = dict(tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase ) )
return common_inputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowerCamelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
else:
__lowerCamelCase = self._generate_dummy_inputs_for_causal_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
return common_inputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowerCamelCase = super()._flatten_past_key_values_(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
__lowerCamelCase = super(__UpperCAmelCase , self )._flatten_past_key_values_(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 1E-4
| 622 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length, 2) ,_UpperCamelCase )
else:
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length) ,_UpperCamelCase )
for i, tensor in enumerate(_UpperCamelCase ):
if padding_side == "right":
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
else:
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = ord(_UpperCamelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__lowerCamelCase = unicodedata.category(_UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = True
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = -1_0_0
lowerCAmelCase__ = "pt"
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
import torch
__lowerCamelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCamelCase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCamelCase = self.tokenizer.pad(
__UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCamelCase = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCamelCase = self.tokenizer.padding_side
if padding_side == "right":
__lowerCamelCase = [
list(__UpperCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) for label in labels
]
else:
__lowerCamelCase = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) + list(__UpperCAmelCase ) for label in labels
]
__lowerCamelCase = [feature['''ner_tags'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , -1 , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = [feature['''original_entity_spans'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , (-1, -1) , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {k: torch.tensor(__UpperCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 622 | 1 |
def a__ ( _UpperCamelCase : int = 2_00_00_00 ):
__lowerCamelCase = [0 for i in range(n + 1 )]
__lowerCamelCase = 1
__lowerCamelCase = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ,n + 1 ,_UpperCamelCase ):
__lowerCamelCase = 1
__lowerCamelCase = 0
for i in range(_UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"{solution() = }")
| 622 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=[1, 1, 2] , __UpperCAmelCase=1 , __UpperCAmelCase=32 , __UpperCAmelCase=4 , __UpperCAmelCase=8 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=3 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=False , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = block_sizes
__lowerCamelCase = num_decoder_layers
__lowerCamelCase = d_model
__lowerCamelCase = n_head
__lowerCamelCase = d_head
__lowerCamelCase = d_inner
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = 2
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = initializer_std
# Used in the tests to check the size of the first attention layer
__lowerCamelCase = n_head
# Used in the tests to check the size of the first hidden state
__lowerCamelCase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
__lowerCamelCase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
__lowerCamelCase = self.num_hidden_layers + 2
def lowerCamelCase ( 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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__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 = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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, 2, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForPreTraining(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForMaskedLM(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForSequenceClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = TFFunnelForMultipleChoice(config=__UpperCAmelCase )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForTokenClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForQuestionAnswering(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self , base=__UpperCAmelCase )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
| 622 | 1 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(_UpperCamelCase ).content ).xpath(_UpperCamelCase ) )
a_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 622 | 1 |
def a__ ( _UpperCamelCase : list ):
if len(_UpperCamelCase ) <= 1:
return lst
__lowerCamelCase = 1
while i < len(_UpperCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__lowerCamelCase ,__lowerCamelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
__lowerCamelCase = 1
return lst
if __name__ == "__main__":
a_ = input("""Enter numbers separated by a comma:\n""").strip()
a_ = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__lowerCamelCase = index + 1
elif index + 1 == len(_UpperCamelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 622 | 1 |
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 __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=64 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = vocab_size - 1
def lowerCamelCase ( 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
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 lowerCamelCase ( self ):
'''simple docstring'''
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=__UpperCAmelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = True
return config, input_ids, input_mask, token_labels
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = GPTNeoXModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = GPTNeoXModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = GPTNeoXForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = GPTNeoXForQuestionAnswering(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = GPTNeoXForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = GPTNeoXForTokenClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = GPTNeoXForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
# first forward pass
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
__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(__UpperCAmelCase , attention_mask=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase )
__lowerCamelCase = output_from_no_past['''hidden_states'''][0]
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , )['''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(__UpperCAmelCase , __UpperCAmelCase , atol=1E-3 ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__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 __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCAmelCase__ = (
{
"""feature-extraction""": GPTNeoXModel,
"""question-answering""": GPTNeoXForQuestionAnswering,
"""text-classification""": GPTNeoXForSequenceClassification,
"""text-generation""": GPTNeoXForCausalLM,
"""token-classification""": GPTNeoXForTokenClassification,
"""zero-shot""": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = GPTNeoXModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=64 , num_attention_heads=8 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# 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(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@unittest.skip(reason='''Feed forward chunking is not implemented''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__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(__UpperCAmelCase )
original_model.to(__UpperCAmelCase )
original_model.eval()
__lowerCamelCase = original_model(__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = original_model(__UpperCAmelCase ).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''': 10.0}
__lowerCamelCase = GPTNeoXModel(__UpperCAmelCase )
scaled_model.to(__UpperCAmelCase )
scaled_model.eval()
__lowerCamelCase = scaled_model(__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = scaled_model(__UpperCAmelCase ).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(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__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(__UpperCAmelCase )
__lowerCamelCase = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(__UpperCAmelCase )
# 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(**__UpperCAmelCase , do_sample=__UpperCAmelCase , max_new_tokens=20 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase )[0]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = 8
# DPR tok
__lowerCamelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowerCamelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , DPR_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] ) )
# BART tok
__lowerCamelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__lowerCamelCase = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__lowerCamelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__lowerCamelCase = {'''unk_token''': '''<unk>'''}
__lowerCamelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCAmelCase ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
__lowerCamelCase = os.path.join(self.tmpdirname , '''dataset''' )
__lowerCamelCase = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase ) , )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
__lowerCamelCase = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
__lowerCamelCase = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
__lowerCamelCase = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(__UpperCAmelCase , open(__UpperCAmelCase , '''wb''' ) )
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = self.get_dummy_dataset()
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
import torch
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
__lowerCamelCase = retriever(
__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase , return_tensors='''pt''' , )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dpr_ctx_encoder_tokenizer()
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase )
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
self.assertEqual(
len(__UpperCAmelCase ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , __UpperCAmelCase ) # check for doc token related keys in dictionary.
| 622 | 1 |
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 ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lowerCAmelCase__ = """BlipImageProcessor"""
lowerCAmelCase__ = """AutoTokenizer"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
# add QFormer tokenizer
__lowerCamelCase = qformer_tokenizer
def __call__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = 0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
__lowerCamelCase = BatchFeature()
if text is not None:
__lowerCamelCase = self.tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
encoding.update(__UpperCAmelCase )
__lowerCamelCase = self.qformer_tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = qformer_text_encoding.pop('''input_ids''' )
__lowerCamelCase = qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
__lowerCamelCase = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase )
encoding.update(__UpperCAmelCase )
return encoding
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.model_input_names
__lowerCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase ( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
if os.path.isfile(__UpperCAmelCase ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(__UpperCAmelCase )
return super().save_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def lowerCamelCase ( cls , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(__UpperCAmelCase , subfolder='''qformer_tokenizer''' )
__lowerCamelCase = cls._get_arguments_from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
args.append(__UpperCAmelCase )
return cls(*__UpperCAmelCase )
| 622 |
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_ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """poolformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=4.0 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[64, 128, 320, 512] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[2, 1, 1, 1] , __UpperCAmelCase=4 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.02 , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = stride
__lowerCamelCase = padding
__lowerCamelCase = pool_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = mlp_ratio
__lowerCamelCase = depths
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_layer_scale
__lowerCamelCase = layer_scale_init_value
__lowerCamelCase = initializer_range
super().__init__(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 2E-3
| 622 | 1 |
import unittest
import numpy as np
def a__ ( _UpperCamelCase : np.ndarray ,_UpperCamelCase : np.ndarray ,_UpperCamelCase : np.ndarray ,_UpperCamelCase : np.ndarray | None = None ,):
__lowerCamelCase = np.shape(_UpperCamelCase )
__lowerCamelCase = np.shape(_UpperCamelCase )
__lowerCamelCase = np.shape(_UpperCamelCase )
if shape_a[0] != shape_b[0]:
__lowerCamelCase = (
'''Expected the same number of rows for A and B. '''
F"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(_UpperCamelCase )
if shape_b[1] != shape_c[1]:
__lowerCamelCase = (
'''Expected the same number of columns for B and C. '''
F"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(_UpperCamelCase )
__lowerCamelCase = pseudo_inv
if a_inv is None:
try:
__lowerCamelCase = np.linalg.inv(_UpperCamelCase )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCamelCase = np.array([[2, 1], [6, 3]] )
__lowerCamelCase = schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.block([[a, b], [b.T, c]] )
__lowerCamelCase = np.linalg.det(__UpperCAmelCase )
__lowerCamelCase = np.linalg.det(__UpperCAmelCase )
__lowerCamelCase = np.linalg.det(__UpperCAmelCase )
self.assertAlmostEqual(__UpperCAmelCase , det_a * det_s )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCamelCase = np.array([[2, 1], [6, 3]] )
with self.assertRaises(__UpperCAmelCase ):
schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCamelCase = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(__UpperCAmelCase ):
schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """visual_bert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=512 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = visual_embedding_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = bypass_transformer
__lowerCamelCase = special_visual_initialize
| 622 | 1 |
import inspect
import unittest
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowerCamelCase ( self ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
__lowerCamelCase = inspect.getmembers(__UpperCAmelCase , 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 = '''k-diffusion'''
elif backend == "invisible_watermark":
__lowerCamelCase = '''invisible-watermark'''
assert backend in deps, F"""{backend} is not in the deps table!"""
| 622 |
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 __lowerCAmelCase ( lowerCAmelCase__ ):
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 , ):
'''simple docstring'''
# 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.
__lowerCamelCase = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
__lowerCamelCase = {} 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 , )
__lowerCamelCase = do_lower_case
__lowerCamelCase = remove_space
__lowerCamelCase = keep_accents
__lowerCamelCase = vocab_file
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {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 ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.remove_space:
__lowerCamelCase = ''' '''.join(inputs.strip().split() )
else:
__lowerCamelCase = inputs
__lowerCamelCase = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__lowerCamelCase = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
__lowerCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
__lowerCamelCase = outputs.lower()
return outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.preprocess_text(__UpperCAmelCase )
__lowerCamelCase = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
__lowerCamelCase = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__lowerCamelCase = 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:
__lowerCamelCase = cur_pieces[1:]
else:
__lowerCamelCase = 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 ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = ''''''
__lowerCamelCase = 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
__lowerCamelCase = True
__lowerCamelCase = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__lowerCamelCase = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = 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:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 622 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
a_ = 6_37_81_37.0
a_ = 6_35_67_52.31_42_45
a_ = 6_378_137
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ,_UpperCamelCase : float ,_UpperCamelCase : float ):
__lowerCamelCase = (AXIS_A - AXIS_B) / AXIS_A
__lowerCamelCase = atan((1 - flattening) * tan(radians(_UpperCamelCase ) ) )
__lowerCamelCase = atan((1 - flattening) * tan(radians(_UpperCamelCase ) ) )
__lowerCamelCase = radians(_UpperCamelCase )
__lowerCamelCase = radians(_UpperCamelCase )
# Equation
__lowerCamelCase = sin((phi_a - phi_a) / 2 )
__lowerCamelCase = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
__lowerCamelCase = sqrt(sin_sq_phi + (cos(_UpperCamelCase ) * cos(_UpperCamelCase ) * sin_sq_lambda) )
return 2 * RADIUS * asin(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 | 1 |
def a__ ( _UpperCamelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 622 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = (CMStochasticIterativeScheduler,)
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {
'''num_train_timesteps''': 201,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
config.update(**__UpperCAmelCase )
return config
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 10
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = self.scheduler_classes[0](**__UpperCAmelCase )
scheduler.set_timesteps(__UpperCAmelCase )
__lowerCamelCase = scheduler.timesteps[0]
__lowerCamelCase = scheduler.timesteps[1]
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ).prev_sample
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase ( self ):
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = 1
scheduler.set_timesteps(__UpperCAmelCase )
__lowerCamelCase = scheduler.timesteps
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(__UpperCAmelCase ):
# 1. scale model input
__lowerCamelCase = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
# 2. predict noise residual
__lowerCamelCase = model(__UpperCAmelCase , __UpperCAmelCase )
# 3. predict previous sample x_t-1
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase ).prev_sample
__lowerCamelCase = pred_prev_sample
__lowerCamelCase = torch.sum(torch.abs(__UpperCAmelCase ) )
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 192.7_614 ) < 1E-2
assert abs(result_mean.item() - 0.2_510 ) < 1E-3
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = [106, 0]
scheduler.set_timesteps(timesteps=__UpperCAmelCase )
__lowerCamelCase = scheduler.timesteps
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
__lowerCamelCase = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
# 2. predict noise residual
__lowerCamelCase = model(__UpperCAmelCase , __UpperCAmelCase )
# 3. predict previous sample x_t-1
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase ).prev_sample
__lowerCamelCase = pred_prev_sample
__lowerCamelCase = torch.sum(torch.abs(__UpperCAmelCase ) )
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 347.6_357 ) < 1E-2
assert abs(result_mean.item() - 0.4_527 ) < 1E-3
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = [39, 30, 12, 15, 0]
with self.assertRaises(__UpperCAmelCase , msg='''`timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = [39, 30, 12, 1, 0]
__lowerCamelCase = len(__UpperCAmelCase )
with self.assertRaises(__UpperCAmelCase , msg='''Can only pass one of `num_inference_steps` or `timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase , timesteps=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__UpperCAmelCase , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__UpperCAmelCase )
| 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
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/data2vec-vision-base-ft""": (
"""https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """data2vec-vision"""
def __init__( self , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=224 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=True , __UpperCAmelCase=[3, 5, 7, 11] , __UpperCAmelCase=[1, 2, 3, 6] , __UpperCAmelCase=True , __UpperCAmelCase=0.4 , __UpperCAmelCase=256 , __UpperCAmelCase=1 , __UpperCAmelCase=False , __UpperCAmelCase=255 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__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 = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = use_mask_token
__lowerCamelCase = use_absolute_position_embeddings
__lowerCamelCase = use_relative_position_bias
__lowerCamelCase = use_shared_relative_position_bias
__lowerCamelCase = layer_scale_init_value
__lowerCamelCase = drop_path_rate
__lowerCamelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCamelCase = out_indices
__lowerCamelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCamelCase = use_auxiliary_head
__lowerCamelCase = auxiliary_loss_weight
__lowerCamelCase = auxiliary_channels
__lowerCamelCase = auxiliary_num_convs
__lowerCamelCase = auxiliary_concat_input
__lowerCamelCase = semantic_loss_ignore_index
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 1E-4
| 622 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = XLMProphetNetTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''[PAD]'''
__lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__UpperCAmelCase ) , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''Hello World!'''
__lowerCamelCase = [35389, 6672, 49, 2]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 622 | 1 |
import random
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = [ord(__UpperCAmelCase ) for i in text]
__lowerCamelCase = []
__lowerCamelCase = []
for i in plain:
__lowerCamelCase = random.randint(1 , 300 )
__lowerCamelCase = (i + k) * k
cipher.append(__UpperCAmelCase )
key.append(__UpperCAmelCase )
return cipher, key
@staticmethod
def lowerCamelCase ( __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
for i in range(len(__UpperCAmelCase ) ):
__lowerCamelCase = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(__UpperCAmelCase ) )
return "".join(__UpperCAmelCase )
if __name__ == "__main__":
a_ , a_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
a_ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = None
# source code of `config_class`
__lowerCamelCase = inspect.getsource(_UpperCamelCase )
__lowerCamelCase = _re_checkpoint.findall(_UpperCamelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('''/''' ):
__lowerCamelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
__lowerCamelCase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__lowerCamelCase = ckpt_name
break
return checkpoint
def a__ ( ):
__lowerCamelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
__lowerCamelCase = get_checkpoint_from_config_class(_UpperCamelCase )
__lowerCamelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
__lowerCamelCase = '''\n'''.join(sorted(_UpperCamelCase ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 622 | 1 |
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 __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase="None" , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = relative_attention
__lowerCamelCase = position_biased_input
__lowerCamelCase = pos_att_type
__lowerCamelCase = scope
def lowerCamelCase ( 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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__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 = 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=__UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaForMaskedLM(config=__UpperCAmelCase )
__lowerCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFDebertaVaForSequenceClassification(config=__UpperCAmelCase )
__lowerCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFDebertaVaForTokenClassification(config=__UpperCAmelCase )
__lowerCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaForQuestionAnswering(config=__UpperCAmelCase )
__lowerCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": TFDebertaVaModel,
"""fill-mask""": TFDebertaVaForMaskedLM,
"""question-answering""": TFDebertaVaForQuestionAnswering,
"""text-classification""": TFDebertaVaForSequenceClassification,
"""token-classification""": TFDebertaVaForTokenClassification,
"""zero-shot""": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
self.assertIsNotNone(__UpperCAmelCase )
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
__lowerCamelCase = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
__lowerCamelCase = tf.constant(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , __UpperCAmelCase , atol=1E-4 )
| 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 | 1 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def a__ ( ):
__lowerCamelCase = Github(os.environ['''GITHUB_TOKEN'''] )
__lowerCamelCase = g.get_repo('''huggingface/transformers''' )
__lowerCamelCase = repo.get_issues(state='''open''' )
for issue in open_issues:
__lowerCamelCase = sorted([comment for comment in issue.get_comments()] ,key=lambda _UpperCamelCase : i.created_at ,reverse=_UpperCamelCase )
__lowerCamelCase = comments[0] if len(_UpperCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main()
| 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger()
@dataclass
class __lowerCAmelCase :
lowerCAmelCase__ = 42
lowerCAmelCase__ = field(default_factory=lowerCAmelCase__ )
lowerCAmelCase__ = field(default_factory=lowerCAmelCase__ )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(list(m.modules() ) ) == 1 or isinstance(__UpperCAmelCase , nn.Convad ) or isinstance(__UpperCAmelCase , nn.BatchNormad )
if has_not_submodules:
self.traced.append(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(__UpperCAmelCase )
[x.remove() for x in self.handles]
return self
@property
def lowerCamelCase ( self ):
'''simple docstring'''
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda __UpperCAmelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __lowerCAmelCase :
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 0
lowerCAmelCase__ = field(default_factory=lowerCAmelCase__ )
lowerCAmelCase__ = field(default_factory=lowerCAmelCase__ )
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = Tracker(self.dest )(__UpperCAmelCase ).parametrized
__lowerCamelCase = Tracker(self.src )(__UpperCAmelCase ).parametrized
__lowerCamelCase = list(filter(lambda __UpperCAmelCase : type(__UpperCAmelCase ) not in self.src_skip , __UpperCAmelCase ) )
__lowerCamelCase = list(filter(lambda __UpperCAmelCase : type(__UpperCAmelCase ) not in self.dest_skip , __UpperCAmelCase ) )
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise Exception(
F"""Numbers of operations are different. Source module has {len(__UpperCAmelCase )} operations while"""
F""" destination module has {len(__UpperCAmelCase )}.""" )
for dest_m, src_m in zip(__UpperCAmelCase , __UpperCAmelCase ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : ResNetConfig ,_UpperCamelCase : Path ,_UpperCamelCase : bool = True ):
print(F"""Converting {name}...""" )
with torch.no_grad():
__lowerCamelCase = timm.create_model(_UpperCamelCase ,pretrained=_UpperCamelCase ).eval()
__lowerCamelCase = ResNetForImageClassification(_UpperCamelCase ).eval()
__lowerCamelCase = ModuleTransfer(src=_UpperCamelCase ,dest=_UpperCamelCase )
__lowerCamelCase = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(_UpperCamelCase )
assert torch.allclose(from_model(_UpperCamelCase ) ,our_model(_UpperCamelCase ).logits ), "The model logits don't match the original one."
__lowerCamelCase = F"""resnet{"-".join(name.split("resnet" ) )}"""
print(_UpperCamelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message='''Add model''' ,use_temp_dir=_UpperCamelCase ,)
# we can use the convnext one
__lowerCamelCase = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message='''Add image processor''' ,use_temp_dir=_UpperCamelCase ,)
print(F"""Pushed {checkpoint_name}""" )
def a__ ( _UpperCamelCase : Path ,_UpperCamelCase : str = None ,_UpperCamelCase : bool = True ):
__lowerCamelCase = '''imagenet-1k-id2label.json'''
__lowerCamelCase = 10_00
__lowerCamelCase = (1, num_labels)
__lowerCamelCase = '''huggingface/label-files'''
__lowerCamelCase = num_labels
__lowerCamelCase = json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset''' ) ,'''r''' ) )
__lowerCamelCase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = partial(_UpperCamelCase ,num_labels=_UpperCamelCase ,idalabel=_UpperCamelCase ,labelaid=_UpperCamelCase )
__lowerCamelCase = {
'''resnet18''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 1_28, 2_56, 5_12] ,layer_type='''basic''' ),
'''resnet26''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck''' ),
'''resnet34''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 1_28, 2_56, 5_12] ,layer_type='''basic''' ),
'''resnet50''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck''' ),
'''resnet101''': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck''' ),
'''resnet152''': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck''' ),
}
if model_name:
convert_weight_and_push(_UpperCamelCase ,names_to_config[model_name] ,_UpperCamelCase ,_UpperCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
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 resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
a_ = parser.parse_args()
a_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 622 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[int]=0.999 ,_UpperCamelCase : List[Any]="cosine" ,):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_UpperCamelCase : Tuple ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_UpperCamelCase : Optional[int] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
__lowerCamelCase = []
for i in range(_UpperCamelCase ):
__lowerCamelCase = i / num_diffusion_timesteps
__lowerCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_UpperCamelCase ) / alpha_bar_fn(_UpperCamelCase ) ,_UpperCamelCase ) )
return torch.tensor(_UpperCamelCase ,dtype=torch.floataa )
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = [e.name for e in KarrasDiffusionSchedulers]
lowerCAmelCase__ = 2
@register_to_config
def __init__( self , __UpperCAmelCase = 1000 , __UpperCAmelCase = 0.00_085 , __UpperCAmelCase = 0.012 , __UpperCAmelCase = "linear" , __UpperCAmelCase = None , __UpperCAmelCase = "epsilon" , __UpperCAmelCase = "linspace" , __UpperCAmelCase = 0 , ):
'''simple docstring'''
if trained_betas is not None:
__lowerCamelCase = torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
__lowerCamelCase = torch.linspace(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCamelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __UpperCAmelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCamelCase = betas_for_alpha_bar(__UpperCAmelCase )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
__lowerCamelCase = 1.0 - self.betas
__lowerCamelCase = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
if schedule_timesteps is None:
__lowerCamelCase = self.timesteps
__lowerCamelCase = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__lowerCamelCase = 1 if len(__UpperCAmelCase ) > 1 else 0
else:
__lowerCamelCase = timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase ) else timestep
__lowerCamelCase = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.index_for_timestep(__UpperCAmelCase )
if self.state_in_first_order:
__lowerCamelCase = self.sigmas[step_index]
else:
__lowerCamelCase = self.sigmas_interpol[step_index]
__lowerCamelCase = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = num_inference_steps
__lowerCamelCase = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
__lowerCamelCase = np.linspace(0 , num_train_timesteps - 1 , __UpperCAmelCase , dtype=__UpperCAmelCase )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__lowerCamelCase = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowerCamelCase = (np.arange(0 , __UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__lowerCamelCase = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowerCamelCase = (np.arange(__UpperCAmelCase , 0 , -step_ratio )).round().copy().astype(__UpperCAmelCase )
timesteps -= 1
else:
raise ValueError(
F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
__lowerCamelCase = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__lowerCamelCase = torch.from_numpy(np.log(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = np.interp(__UpperCAmelCase , np.arange(0 , len(__UpperCAmelCase ) ) , __UpperCAmelCase )
__lowerCamelCase = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase )
# interpolate sigmas
__lowerCamelCase = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
__lowerCamelCase = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
__lowerCamelCase = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__UpperCAmelCase ).startswith('''mps''' ):
# mps does not support float64
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase , dtype=torch.floataa )
else:
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase )
# interpolate timesteps
__lowerCamelCase = self.sigma_to_t(__UpperCAmelCase ).to(__UpperCAmelCase , dtype=timesteps.dtype )
__lowerCamelCase = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
__lowerCamelCase = torch.cat([timesteps[:1], interleaved_timesteps] )
__lowerCamelCase = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__lowerCamelCase = defaultdict(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# get log sigma
__lowerCamelCase = sigma.log()
# get distribution
__lowerCamelCase = log_sigma - self.log_sigmas[:, None]
# get sigmas range
__lowerCamelCase = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
__lowerCamelCase = low_idx + 1
__lowerCamelCase = self.log_sigmas[low_idx]
__lowerCamelCase = self.log_sigmas[high_idx]
# interpolate sigmas
__lowerCamelCase = (low - log_sigma) / (low - high)
__lowerCamelCase = w.clamp(0 , 1 )
# transform interpolation to time range
__lowerCamelCase = (1 - w) * low_idx + w * high_idx
__lowerCamelCase = t.view(sigma.shape )
return t
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self.sample is None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = True , ):
'''simple docstring'''
__lowerCamelCase = self.index_for_timestep(__UpperCAmelCase )
# advance index counter by 1
__lowerCamelCase = timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__lowerCamelCase = self.sigmas[step_index]
__lowerCamelCase = self.sigmas_interpol[step_index + 1]
__lowerCamelCase = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
__lowerCamelCase = self.sigmas[step_index - 1]
__lowerCamelCase = self.sigmas_interpol[step_index]
__lowerCamelCase = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
__lowerCamelCase = 0
__lowerCamelCase = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__lowerCamelCase = sigma_hat if self.state_in_first_order else sigma_interpol
__lowerCamelCase = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__lowerCamelCase = sigma_hat if self.state_in_first_order else sigma_interpol
__lowerCamelCase = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('''prediction_type not implemented yet: sample''' )
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
__lowerCamelCase = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__lowerCamelCase = sigma_interpol - sigma_hat
# store for 2nd order step
__lowerCamelCase = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
__lowerCamelCase = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
__lowerCamelCase = sigma_next - sigma_hat
__lowerCamelCase = self.sample
__lowerCamelCase = None
__lowerCamelCase = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__lowerCamelCase = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase ):
# mps does not support float64
__lowerCamelCase = self.timesteps.to(original_samples.device , dtype=torch.floataa )
__lowerCamelCase = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
__lowerCamelCase = self.timesteps.to(original_samples.device )
__lowerCamelCase = timesteps.to(original_samples.device )
__lowerCamelCase = [self.index_for_timestep(__UpperCAmelCase , __UpperCAmelCase ) for t in timesteps]
__lowerCamelCase = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__lowerCamelCase = sigma.unsqueeze(-1 )
__lowerCamelCase = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 622 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : List[str] ,_UpperCamelCase : List[Any]=None ,_UpperCamelCase : Any=None ):
if attention_mask is None:
__lowerCamelCase = tf.cast(tf.math.not_equal(_UpperCamelCase ,config.pad_token_id ) ,tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __lowerCAmelCase :
lowerCAmelCase__ = OPTConfig
lowerCAmelCase__ = {}
lowerCAmelCase__ = """gelu"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=20 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = embed_dim
__lowerCamelCase = word_embed_proj_dim
__lowerCamelCase = False
def lowerCamelCase ( 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 = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__UpperCAmelCase , **self.config_updates , )
__lowerCamelCase = prepare_opt_inputs_dict(__UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel(config=__UpperCAmelCase )
__lowerCamelCase = inputs_dict['''input_ids''']
__lowerCamelCase = input_ids[:1, :]
__lowerCamelCase = inputs_dict['''attention_mask'''][:1, :]
__lowerCamelCase = 1
# first forward pass
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
__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(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__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(__UpperCAmelCase , __UpperCAmelCase , rtol=1E-3 )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__lowerCamelCase = model_class(config=__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__lowerCamelCase = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __UpperCAmelCase )
# check that weights remain the same after resizing
__lowerCamelCase = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __UpperCAmelCase )
__lowerCamelCase = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
def a__ ( _UpperCamelCase : Optional[Any] ):
return tf.constant(_UpperCamelCase ,dtype=tf.intaa )
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = 9_9
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__lowerCamelCase = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__lowerCamelCase = input_ids.shape[0]
__lowerCamelCase = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel.from_pretrained('''facebook/opt-350m''' )
__lowerCamelCase = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase = tf.not_equal(__UpperCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
__lowerCamelCase = model(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = (1, 11, 512)
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = tf.constant(
[[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-3 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = xla_generate(__UpperCAmelCase , __UpperCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = '''facebook/opt-350m'''
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(self.path_model )
__lowerCamelCase = GPTaTokenizer.from_pretrained(self.path_model )
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__lowerCamelCase = tf.constant(
[
[1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
[-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
[0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
[6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
] )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-125m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = '''left'''
# use different length sentences to test batching
__lowerCamelCase = [
'''Hello, my dog is a little''',
'''Today, I''',
]
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase )
__lowerCamelCase = inputs['''input_ids''']
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , attention_mask=inputs['''attention_mask'''] )
__lowerCamelCase = tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase )
__lowerCamelCase = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa ) )
__lowerCamelCase = tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , max_length=model.config.max_length - num_paddings )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , [non_padded_sentence, padded_sentence] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 | 1 |
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
__lowerCamelCase = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
__lowerCamelCase = max(len(_UpperCamelCase ) ,len(_UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase ) ,b_binary.zfill(_UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ = logging.getLogger(__name__)
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[Any] ):
__lowerCamelCase = np.argmax(_UpperCamelCase ,axis=1 )
return np.sum(outputs == labels )
def a__ ( _UpperCamelCase : Optional[int] ):
with open(_UpperCamelCase ,encoding='''utf_8''' ) as f:
__lowerCamelCase = csv.reader(_UpperCamelCase )
__lowerCamelCase = []
next(_UpperCamelCase ) # skip the first line
for line in tqdm(_UpperCamelCase ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Dict ,_UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Dict ):
__lowerCamelCase = []
for dataset in encoded_datasets:
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
__lowerCamelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCamelCase ):
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = mc_label
__lowerCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,default='''openai-gpt''' ,help='''pretrained model name''' )
parser.add_argument('''--do_train''' ,action='''store_true''' ,help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' ,action='''store_true''' ,help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42 )
parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3 )
parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8 )
parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16 )
parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1 )
parser.add_argument(
'''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) ,)
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,)
parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5 )
parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01 )
parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9 )
parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74 )
parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
__lowerCamelCase = parser.parse_args()
print(_UpperCamelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
__lowerCamelCase = torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase ,_UpperCamelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase = ['''_start_''', '''_delimiter_''', '''_classify_''']
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCamelCase )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCamelCase ) )
model.to(_UpperCamelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCamelCase : Dict ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase ) )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
return obj
return [tokenize_and_encode(_UpperCamelCase ) for o in obj]
logger.info('''Encoding dataset...''' )
__lowerCamelCase = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase = (train_dataset, eval_dataset)
__lowerCamelCase = tokenize_and_encode(_UpperCamelCase )
# Compute the max input length for the Transformer
__lowerCamelCase = model.config.n_positions // 2 - 2
__lowerCamelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase = min(_UpperCamelCase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase = pre_process_datasets(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,*_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = RandomSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size )
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = SequentialSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase = args.max_steps
__lowerCamelCase = args.max_steps // (len(_UpperCamelCase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase = len(_UpperCamelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase = list(model.named_parameters() )
__lowerCamelCase = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
__lowerCamelCase = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
__lowerCamelCase = AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon )
__lowerCamelCase = get_linear_schedule_with_warmup(
_UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase )
if args.do_train:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc='''Epoch''' ):
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(_UpperCamelCase ,desc='''Training''' )
for step, batch in enumerate(_UpperCamelCase ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
__lowerCamelCase = model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase = '''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase = model.module if hasattr(_UpperCamelCase ,'''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
torch.save(model_to_save.state_dict() ,_UpperCamelCase )
model_to_save.config.to_json_file(_UpperCamelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCamelCase )
if args.do_eval:
model.eval()
__lowerCamelCase ,__lowerCamelCase = 0, 0
__lowerCamelCase ,__lowerCamelCase = 0, 0
for batch in tqdm(_UpperCamelCase ,desc='''Evaluating''' ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
with torch.no_grad():
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = model(
_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = mc_logits.detach().cpu().numpy()
__lowerCamelCase = mc_labels.to('''cpu''' ).numpy()
__lowerCamelCase = accuracy(_UpperCamelCase ,_UpperCamelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase = eval_loss / nb_eval_steps
__lowerCamelCase = eval_accuracy / nb_eval_examples
__lowerCamelCase = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
__lowerCamelCase = os.path.join(args.output_dir ,'''eval_results.txt''' )
with open(_UpperCamelCase ,'''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' ,_UpperCamelCase ,str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 622 | 1 |
import qiskit
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ):
__lowerCamelCase = qiskit.Aer.get_backend('''aer_simulator''' )
__lowerCamelCase = qiskit.QuantumCircuit(4 ,2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 ,2 )
qc_ha.cx(1 ,2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 ,1 ,3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 ,0 ) # extract XOR value
qc_ha.measure(3 ,1 ) # extract AND value
# Execute the circuit on the qasm simulator
__lowerCamelCase = qiskit.execute(_UpperCamelCase ,_UpperCamelCase ,shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(_UpperCamelCase )
if __name__ == "__main__":
a_ = half_adder(1, 1)
print(f"Half Adder Output Qubit Counts: {counts}")
| 622 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAmelCase=1024 , __UpperCAmelCase=3.6 ):
'''simple docstring'''
__lowerCamelCase = tokenizer
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = dataset
__lowerCamelCase = seq_length
__lowerCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = iter(self.dataset )
__lowerCamelCase = True
while more_examples:
__lowerCamelCase ,__lowerCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__UpperCAmelCase )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
__lowerCamelCase = False
break
__lowerCamelCase = tokenizer(__UpperCAmelCase , truncation=__UpperCAmelCase )['''input_ids''']
__lowerCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__UpperCAmelCase ) , self.seq_length ):
__lowerCamelCase = all_token_ids[i : i + self.seq_length]
if len(__UpperCAmelCase ) == self.seq_length:
yield torch.tensor(__UpperCAmelCase )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = {'''streaming''': True}
__lowerCamelCase = load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase )
__lowerCamelCase = ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=args.batch_size )
return eval_dataloader
def a__ ( _UpperCamelCase : str ):
model.eval()
__lowerCamelCase = []
for step, batch in enumerate(_UpperCamelCase ):
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase ,labels=_UpperCamelCase )
__lowerCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__lowerCamelCase = torch.mean(torch.cat(_UpperCamelCase ) )
try:
__lowerCamelCase = torch.exp(_UpperCamelCase )
except OverflowError:
__lowerCamelCase = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
a_ = Accelerator()
# Parse configuration
a_ = HfArgumentParser(EvaluationArguments)
a_ = parser.parse_args()
set_seed(args.seed)
# Logging
a_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
a_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
a_ , a_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_ , a_ = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 622 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=3 , __UpperCAmelCase=32 , __UpperCAmelCase=3 , __UpperCAmelCase=10 , __UpperCAmelCase=[10, 20, 30, 40] , __UpperCAmelCase=[1, 1, 2, 1] , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase="relu" , __UpperCAmelCase=3 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = embeddings_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = depths
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_act
__lowerCamelCase = num_labels
__lowerCamelCase = scope
__lowerCamelCase = len(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = self.get_config()
return config, pixel_values
def lowerCamelCase ( self ):
'''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 , image_size=self.image_size , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = FlaxRegNetModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = FlaxRegNetForImageClassification(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = FlaxRegNetModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''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 lowerCamelCase ( self ):
'''simple docstring'''
return
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( 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 )
__lowerCamelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(__UpperCAmelCase ) , expected_num_stages + 1 )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowerCamelCase = self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = model_class(__UpperCAmelCase )
@jax.jit
def model_jitted(__UpperCAmelCase , **__UpperCAmelCase ):
return model(pixel_values=__UpperCAmelCase , **__UpperCAmelCase )
with self.subTest('''JIT Enabled''' ):
__lowerCamelCase = model_jitted(**__UpperCAmelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowerCamelCase = model_jitted(**__UpperCAmelCase ).to_tuple()
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for jitted_output, output in zip(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def a__ ( ):
__lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=__UpperCAmelCase , return_tensors='''np''' )
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = (1, 1000)
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = jnp.array([-0.4_180, -1.5_051, -3.4_836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """lxmert"""
lowerCAmelCase__ = {}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=9500 , __UpperCAmelCase=1600 , __UpperCAmelCase=400 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=9 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=2048 , __UpperCAmelCase=4 , __UpperCAmelCase=6.67 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = num_qa_labels
__lowerCamelCase = num_object_labels
__lowerCamelCase = num_attr_labels
__lowerCamelCase = l_layers
__lowerCamelCase = x_layers
__lowerCamelCase = r_layers
__lowerCamelCase = visual_feat_dim
__lowerCamelCase = visual_pos_dim
__lowerCamelCase = visual_loss_normalizer
__lowerCamelCase = task_matched
__lowerCamelCase = task_mask_lm
__lowerCamelCase = task_obj_predict
__lowerCamelCase = task_qa
__lowerCamelCase = visual_obj_loss
__lowerCamelCase = visual_attr_loss
__lowerCamelCase = visual_feat_loss
__lowerCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__UpperCAmelCase )
| 622 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 622 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length, 2) ,_UpperCamelCase )
else:
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length) ,_UpperCamelCase )
for i, tensor in enumerate(_UpperCamelCase ):
if padding_side == "right":
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
else:
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = ord(_UpperCamelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__lowerCamelCase = unicodedata.category(_UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = True
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = -1_0_0
lowerCAmelCase__ = "pt"
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
import torch
__lowerCamelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCamelCase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCamelCase = self.tokenizer.pad(
__UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCamelCase = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCamelCase = self.tokenizer.padding_side
if padding_side == "right":
__lowerCamelCase = [
list(__UpperCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) for label in labels
]
else:
__lowerCamelCase = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) + list(__UpperCAmelCase ) for label in labels
]
__lowerCamelCase = [feature['''ner_tags'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , -1 , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = [feature['''original_entity_spans'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , (-1, -1) , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {k: torch.tensor(__UpperCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 622 | 1 |
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = [0] * len(_UpperCamelCase )
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(_UpperCamelCase )
while queue:
__lowerCamelCase = queue.pop(0 )
cnt += 1
topo.append(_UpperCamelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_UpperCamelCase )
if cnt != len(_UpperCamelCase ):
print('''Cycle exists''' )
else:
print(_UpperCamelCase )
# Adjacency List of Graph
a_ = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 622 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=[1, 1, 2] , __UpperCAmelCase=1 , __UpperCAmelCase=32 , __UpperCAmelCase=4 , __UpperCAmelCase=8 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=3 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=False , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = block_sizes
__lowerCamelCase = num_decoder_layers
__lowerCamelCase = d_model
__lowerCamelCase = n_head
__lowerCamelCase = d_head
__lowerCamelCase = d_inner
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = 2
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = initializer_std
# Used in the tests to check the size of the first attention layer
__lowerCamelCase = n_head
# Used in the tests to check the size of the first hidden state
__lowerCamelCase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
__lowerCamelCase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
__lowerCamelCase = self.num_hidden_layers + 2
def lowerCamelCase ( 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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__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 = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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, 2, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForPreTraining(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForMaskedLM(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForSequenceClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = TFFunnelForMultipleChoice(config=__UpperCAmelCase )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForTokenClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForQuestionAnswering(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self , base=__UpperCAmelCase )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
| 622 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __lowerCAmelCase ( unittest.TestCase , lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = load_tool('''text-to-speech''' )
self.tool.setup()
def lowerCamelCase ( self ):
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__lowerCamelCase = self.tool('''hey''' )
__lowerCamelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
def lowerCamelCase ( self ):
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__lowerCamelCase = self.tool('''hey''' )
__lowerCamelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
| 622 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(_UpperCamelCase ).content ).xpath(_UpperCamelCase ) )
a_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 622 | 1 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def a__ ( _UpperCamelCase : Optional[int] ):
__lowerCamelCase = int(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = t // 36_00, (t // 60) % 60, t % 60
return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""
def a__ ( _UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : List[Any] ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[int]=3_00 ):
# docstyle-ignore
return F"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def a__ ( _UpperCamelCase : List[str] ):
__lowerCamelCase = '''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCamelCase = F"""{elt:.6f}""" if isinstance(_UpperCamelCase ,_UpperCamelCase ) else str(_UpperCamelCase )
html_code += F""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __lowerCAmelCase :
lowerCAmelCase__ = 5
lowerCAmelCase__ = 0.2
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = 300 , ):
'''simple docstring'''
__lowerCamelCase = total
__lowerCamelCase = '''''' if prefix is None else prefix
__lowerCamelCase = leave
__lowerCamelCase = parent
__lowerCamelCase = width
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = value
if comment is not None:
__lowerCamelCase = comment
if self.last_value is None:
__lowerCamelCase = __lowerCamelCase = time.time()
__lowerCamelCase = __lowerCamelCase = value
__lowerCamelCase = __lowerCamelCase = None
__lowerCamelCase = self.warmup
__lowerCamelCase = 1
self.update_bar(__UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCamelCase = time.time()
__lowerCamelCase = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCamelCase = self.elapsed_time / (value - self.start_value)
else:
__lowerCamelCase = None
if value >= self.total:
__lowerCamelCase = self.total
__lowerCamelCase = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCamelCase = self.average_time_per_item * (self.total - value)
self.update_bar(__UpperCAmelCase )
__lowerCamelCase = value
__lowerCamelCase = current_time
if self.average_time_per_item is None:
__lowerCamelCase = 1
else:
__lowerCamelCase = max(int(self.update_every / self.average_time_per_item ) , 1 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = ''' ''' * (len(str(self.total ) ) - len(str(__UpperCAmelCase ) )) + str(__UpperCAmelCase )
if self.elapsed_time is None:
__lowerCamelCase = F"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
__lowerCamelCase = F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"""
else:
__lowerCamelCase = (
F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"""
F""" {format_time(self.predicted_remaining )}"""
)
self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F""", {self.comment}]"""
self.display()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCamelCase = disp.display(disp.HTML(self.html_code ) , display_id=__UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCamelCase ( self ):
'''simple docstring'''
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('''''' ) )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
super().__init__(__UpperCAmelCase )
__lowerCamelCase = None if column_names is None else [column_names]
__lowerCamelCase = None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCamelCase = disp.display(disp.HTML(self.html_code ) , display_id=__UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.inner_table is None:
__lowerCamelCase = [list(values.keys() ), list(values.values() )]
else:
__lowerCamelCase = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(__UpperCAmelCase )
__lowerCamelCase = columns
self.inner_table.append([values[c] for c in columns] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=300 ):
'''simple docstring'''
__lowerCamelCase = NotebookProgressBar(__UpperCAmelCase , prefix=__UpperCAmelCase , parent=self , width=__UpperCAmelCase )
return self.child_bar
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = None
self.display()
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = False
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''' )
__lowerCamelCase = NotebookTrainingTracker(state.max_steps , __UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = int(state.epoch ) if int(state.epoch ) == state.epoch else F"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=F"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
__lowerCamelCase = False
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
if not has_length(__UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCamelCase = self.training_tracker.add_child(len(__UpperCAmelCase ) )
else:
__lowerCamelCase = NotebookProgressBar(len(__UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCamelCase = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCamelCase = state.global_step
self.training_tracker.write_line(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
if self.training_tracker is not None:
__lowerCamelCase = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCamelCase = log['''loss''']
break
if self.first_column == "Epoch":
__lowerCamelCase = int(state.epoch )
else:
__lowerCamelCase = state.global_step
__lowerCamelCase = '''eval'''
for k in metrics:
if k.endswith('''_loss''' ):
__lowerCamelCase = re.sub(r'''\_loss$''' , '''''' , __UpperCAmelCase )
__lowerCamelCase = metrics.pop('''total_flos''' , __UpperCAmelCase )
__lowerCamelCase = metrics.pop('''epoch''' , __UpperCAmelCase )
__lowerCamelCase = metrics.pop(F"""{metric_key_prefix}_runtime""" , __UpperCAmelCase )
__lowerCamelCase = metrics.pop(F"""{metric_key_prefix}_samples_per_second""" , __UpperCAmelCase )
__lowerCamelCase = metrics.pop(F"""{metric_key_prefix}_steps_per_second""" , __UpperCAmelCase )
__lowerCamelCase = metrics.pop(F"""{metric_key_prefix}_jit_compilation_time""" , __UpperCAmelCase )
for k, v in metrics.items():
if k == F"""{metric_key_prefix}_loss""":
__lowerCamelCase = v
else:
__lowerCamelCase = k.split('''_''' )
__lowerCamelCase = ''' '''.join([part.capitalize() for part in splits[1:]] )
__lowerCamelCase = v
self.training_tracker.write_line(__UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCamelCase = None
# Evaluation takes a long time so we should force the next update.
__lowerCamelCase = True
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
self.training_tracker.update(
state.global_step , comment=F"""Epoch {int(state.epoch )}/{state.num_train_epochs}""" , force_update=__UpperCAmelCase )
__lowerCamelCase = None
| 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__lowerCamelCase = index + 1
elif index + 1 == len(_UpperCamelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 622 | 1 |
a_ = """Tobias Carryer"""
from time import time
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=int(time() ) ): # noqa: B008
'''simple docstring'''
__lowerCamelCase = multiplier
__lowerCamelCase = increment
__lowerCamelCase = modulo
__lowerCamelCase = seed
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
a_ = LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
while True:
print(lcg.next_number())
| 622 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = 8
# DPR tok
__lowerCamelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowerCamelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , DPR_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] ) )
# BART tok
__lowerCamelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__lowerCamelCase = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__lowerCamelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__lowerCamelCase = {'''unk_token''': '''<unk>'''}
__lowerCamelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCAmelCase ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
__lowerCamelCase = os.path.join(self.tmpdirname , '''dataset''' )
__lowerCamelCase = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase ) , )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
__lowerCamelCase = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
__lowerCamelCase = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
__lowerCamelCase = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(__UpperCAmelCase , open(__UpperCAmelCase , '''wb''' ) )
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = self.get_dummy_dataset()
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
import torch
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
__lowerCamelCase = retriever(
__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase , return_tensors='''pt''' , )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dpr_ctx_encoder_tokenizer()
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase )
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
self.assertEqual(
len(__UpperCAmelCase ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , __UpperCAmelCase ) # check for doc token related keys in dictionary.
| 622 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all BART models at https://huggingface.co/models?filter=bart
a_ = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
},
}
a_ = {
"""facebook/bart-base""": 1_024,
"""facebook/bart-large""": 1_024,
"""facebook/bart-large-mnli""": 1_024,
"""facebook/bart-large-cnn""": 1_024,
"""facebook/bart-large-xsum""": 1_024,
"""yjernite/bart_eli5""": 1_024,
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["""input_ids""", """attention_mask"""]
lowerCAmelCase__ = BartTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="replace" , __UpperCAmelCase="<s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="<s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="<mask>" , __UpperCAmelCase=False , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , errors=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __UpperCAmelCase ) != add_prefix_space:
__lowerCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**__UpperCAmelCase )
__lowerCamelCase = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__lowerCamelCase = '''post_processor'''
__lowerCamelCase = getattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
if tokenizer_component_instance:
__lowerCamelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__lowerCamelCase = tuple(state['''sep'''] )
if "cls" in state:
__lowerCamelCase = tuple(state['''cls'''] )
__lowerCamelCase = False
if state.get('''add_prefix_space''' , __UpperCAmelCase ) != add_prefix_space:
__lowerCamelCase = add_prefix_space
__lowerCamelCase = True
if state.get('''trim_offsets''' , __UpperCAmelCase ) != trim_offsets:
__lowerCamelCase = trim_offsets
__lowerCamelCase = True
if changes_to_apply:
__lowerCamelCase = getattr(__UpperCAmelCase , state.pop('''type''' ) )
__lowerCamelCase = component_class(**__UpperCAmelCase )
setattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else value
__lowerCamelCase = value
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = [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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__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 + sep + token_ids_a + sep ) * [0]
| 622 |
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_ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """poolformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=4.0 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[64, 128, 320, 512] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[2, 1, 1, 1] , __UpperCAmelCase=4 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.02 , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = stride
__lowerCamelCase = padding
__lowerCamelCase = pool_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = mlp_ratio
__lowerCamelCase = depths
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_layer_scale
__lowerCamelCase = layer_scale_init_value
__lowerCamelCase = initializer_range
super().__init__(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 2E-3
| 622 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""input_features""", """is_longer"""]
def __init__( self , __UpperCAmelCase=64 , __UpperCAmelCase=48000 , __UpperCAmelCase=480 , __UpperCAmelCase=10 , __UpperCAmelCase=1024 , __UpperCAmelCase=0.0 , __UpperCAmelCase=False , __UpperCAmelCase = 0 , __UpperCAmelCase = 14000 , __UpperCAmelCase = None , __UpperCAmelCase = "fusion" , __UpperCAmelCase = "repeatpad" , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
feature_size=__UpperCAmelCase , sampling_rate=__UpperCAmelCase , padding_value=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = top_db
__lowerCamelCase = truncation
__lowerCamelCase = padding
__lowerCamelCase = fft_window_size
__lowerCamelCase = (fft_window_size >> 1) + 1
__lowerCamelCase = hop_length
__lowerCamelCase = max_length_s
__lowerCamelCase = max_length_s * sampling_rate
__lowerCamelCase = sampling_rate
__lowerCamelCase = frequency_min
__lowerCamelCase = frequency_max
__lowerCamelCase = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__UpperCAmelCase , min_frequency=__UpperCAmelCase , max_frequency=__UpperCAmelCase , sampling_rate=__UpperCAmelCase , norm=__UpperCAmelCase , mel_scale='''htk''' , )
__lowerCamelCase = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__UpperCAmelCase , min_frequency=__UpperCAmelCase , max_frequency=__UpperCAmelCase , sampling_rate=__UpperCAmelCase , norm='''slaney''' , mel_scale='''slaney''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = spectrogram(
__UpperCAmelCase , window_function(self.fft_window_size , '''hann''' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=__UpperCAmelCase , log_mel='''dB''' , )
return log_mel_spectrogram.T
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
__lowerCamelCase = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
__lowerCamelCase = [0]
# randomly choose index for each part
__lowerCamelCase = np.random.choice(ranges[0] )
__lowerCamelCase = np.random.choice(ranges[1] )
__lowerCamelCase = np.random.choice(ranges[2] )
__lowerCamelCase = mel[idx_front : idx_front + chunk_frames, :]
__lowerCamelCase = mel[idx_middle : idx_middle + chunk_frames, :]
__lowerCamelCase = mel[idx_back : idx_back + chunk_frames, :]
__lowerCamelCase = torch.tensor(mel[None, None, :] )
__lowerCamelCase = torch.nn.functional.interpolate(
__UpperCAmelCase , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=__UpperCAmelCase )
__lowerCamelCase = mel_shrink[0][0].numpy()
__lowerCamelCase = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__lowerCamelCase = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__lowerCamelCase = len(__UpperCAmelCase ) - max_length
__lowerCamelCase = np.random.randint(0 , overflow + 1 )
__lowerCamelCase = waveform[idx : idx + max_length]
__lowerCamelCase = self._np_extract_fbank_features(__UpperCAmelCase , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
__lowerCamelCase = self._np_extract_fbank_features(__UpperCAmelCase , self.mel_filters )
__lowerCamelCase = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__lowerCamelCase = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__lowerCamelCase = np.stack([mel, mel, mel, mel] , axis=0 )
__lowerCamelCase = False
else:
__lowerCamelCase = self._random_mel_fusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
__lowerCamelCase = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__lowerCamelCase = int(max_length / len(__UpperCAmelCase ) )
__lowerCamelCase = np.stack(np.tile(__UpperCAmelCase , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
__lowerCamelCase = int(max_length / len(__UpperCAmelCase ) )
__lowerCamelCase = np.stack(np.tile(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = np.pad(__UpperCAmelCase , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0 )
if truncation == "fusion":
__lowerCamelCase = self._np_extract_fbank_features(__UpperCAmelCase , self.mel_filters )
__lowerCamelCase = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
__lowerCamelCase = self._np_extract_fbank_features(__UpperCAmelCase , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = truncation if truncation is not None else self.truncation
__lowerCamelCase = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__lowerCamelCase = isinstance(__UpperCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
__lowerCamelCase = is_batched_numpy or (
isinstance(__UpperCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__lowerCamelCase = [np.asarray(__UpperCAmelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__UpperCAmelCase , np.ndarray ):
__lowerCamelCase = np.asarray(__UpperCAmelCase , dtype=np.floataa )
elif isinstance(__UpperCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__lowerCamelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__lowerCamelCase = [np.asarray(__UpperCAmelCase )]
# convert to mel spectrogram, truncate and pad if needed.
__lowerCamelCase = [
self._get_input_mel(__UpperCAmelCase , max_length if max_length else self.nb_max_samples , __UpperCAmelCase , __UpperCAmelCase )
for waveform in raw_speech
]
__lowerCamelCase = []
__lowerCamelCase = []
for mel, longer in padded_inputs:
input_mel.append(__UpperCAmelCase )
is_longer.append(__UpperCAmelCase )
if truncation == "fusion" and sum(__UpperCAmelCase ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__lowerCamelCase = np.random.randint(0 , len(__UpperCAmelCase ) )
__lowerCamelCase = True
if isinstance(input_mel[0] , __UpperCAmelCase ):
__lowerCamelCase = [np.asarray(__UpperCAmelCase , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
__lowerCamelCase = [[longer] for longer in is_longer]
__lowerCamelCase = {'''input_features''': input_mel, '''is_longer''': is_longer}
__lowerCamelCase = BatchFeature(__UpperCAmelCase )
if return_tensors is not None:
__lowerCamelCase = input_features.convert_to_tensors(__UpperCAmelCase )
return input_features
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """visual_bert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=512 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = visual_embedding_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = bypass_transformer
__lowerCamelCase = special_visual_initialize
| 622 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = (DEISMultistepScheduler,)
lowerCAmelCase__ = (("""num_inference_steps""", 2_5),)
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
}
config.update(**__UpperCAmelCase )
return config
def lowerCamelCase ( self , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('''num_inference_steps''' , __UpperCAmelCase )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config(**__UpperCAmelCase )
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(__UpperCAmelCase )
# copy over dummy past residuals
__lowerCamelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCAmelCase )
__lowerCamelCase = scheduler_class.from_pretrained(__UpperCAmelCase )
new_scheduler.set_timesteps(__UpperCAmelCase )
# copy over dummy past residuals
__lowerCamelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCamelCase ,__lowerCamelCase = sample, sample
for t in range(__UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
__lowerCamelCase = new_scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('''num_inference_steps''' , __UpperCAmelCase )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(__UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCamelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCAmelCase )
__lowerCamelCase = scheduler_class.from_pretrained(__UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCamelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
__lowerCamelCase = new_scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase ( self , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
if scheduler is None:
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(**__UpperCAmelCase )
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(**__UpperCAmelCase )
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = 10
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCamelCase = model(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ).prev_sample
return sample
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('''num_inference_steps''' , __UpperCAmelCase )
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__UpperCAmelCase , '''set_timesteps''' ):
scheduler.set_timesteps(__UpperCAmelCase )
elif num_inference_steps is not None and not hasattr(__UpperCAmelCase , '''set_timesteps''' ):
__lowerCamelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
__lowerCamelCase = dummy_past_residuals[: scheduler.config.solver_order]
__lowerCamelCase = scheduler.timesteps[5]
__lowerCamelCase = scheduler.timesteps[6]
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase ( self ):
'''simple docstring'''
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCamelCase = DEISMultistepScheduler(**self.get_scheduler_config() )
__lowerCamelCase = self.full_loop(scheduler=__UpperCAmelCase )
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
__lowerCamelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCamelCase = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCamelCase = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCamelCase = self.full_loop(scheduler=__UpperCAmelCase )
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def lowerCamelCase ( self ):
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.check_over_configs(thresholding=__UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCAmelCase , prediction_type=__UpperCAmelCase , sample_max_value=__UpperCAmelCase , algorithm_type='''deis''' , solver_order=__UpperCAmelCase , solver_type=__UpperCAmelCase , )
def lowerCamelCase ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCAmelCase , solver_type=__UpperCAmelCase , prediction_type=__UpperCAmelCase , algorithm_type=__UpperCAmelCase , )
__lowerCamelCase = self.full_loop(
solver_order=__UpperCAmelCase , solver_type=__UpperCAmelCase , prediction_type=__UpperCAmelCase , algorithm_type=__UpperCAmelCase , )
assert not torch.isnan(__UpperCAmelCase ).any(), "Samples have nan numbers"
def lowerCamelCase ( self ):
'''simple docstring'''
self.check_over_configs(lower_order_final=__UpperCAmelCase )
self.check_over_configs(lower_order_final=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__UpperCAmelCase , time_step=0 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.full_loop()
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.full_loop(prediction_type='''v_prediction''' )
__lowerCamelCase = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(thresholding=__UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCamelCase = scheduler_class(**__UpperCAmelCase )
__lowerCamelCase = 10
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCamelCase = model(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 622 |
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 __lowerCAmelCase ( lowerCAmelCase__ ):
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 , ):
'''simple docstring'''
# 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.
__lowerCamelCase = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
__lowerCamelCase = {} 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 , )
__lowerCamelCase = do_lower_case
__lowerCamelCase = remove_space
__lowerCamelCase = keep_accents
__lowerCamelCase = vocab_file
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {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 ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.remove_space:
__lowerCamelCase = ''' '''.join(inputs.strip().split() )
else:
__lowerCamelCase = inputs
__lowerCamelCase = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__lowerCamelCase = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
__lowerCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
__lowerCamelCase = outputs.lower()
return outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.preprocess_text(__UpperCAmelCase )
__lowerCamelCase = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
__lowerCamelCase = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__lowerCamelCase = 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:
__lowerCamelCase = cur_pieces[1:]
else:
__lowerCamelCase = 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 ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = ''''''
__lowerCamelCase = 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
__lowerCamelCase = True
__lowerCamelCase = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__lowerCamelCase = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = 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:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 622 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
__lowerCamelCase = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__UpperCAmelCase , cache_dir=__UpperCAmelCase )
__lowerCamelCase = [t[-1] for t in os.walk(os.path.join(__UpperCAmelCase , os.listdir(__UpperCAmelCase )[0] , '''snapshots''' ) )]
__lowerCamelCase = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__UpperCAmelCase )
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.random.PRNGKey(0 )
__lowerCamelCase = 4
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
# shard inputs and rng
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = jax.random.split(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_514_745 ) < 1E-3
assert np.abs(np.abs(__UpperCAmelCase , dtype=np.floataa ).sum() - 49_947.875 ) < 5E-1
__lowerCamelCase = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(__UpperCAmelCase ) == num_samples
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__UpperCAmelCase )
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.random.PRNGKey(0 )
__lowerCamelCase = 50
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
# shard inputs and rng
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = jax.random.split(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_652_401) ) < 1E-3
assert np.abs((np.abs(__UpperCAmelCase , dtype=np.floataa ).sum() - 2_383_808.2) ) < 5E-1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__UpperCAmelCase )
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.random.PRNGKey(0 )
__lowerCamelCase = 50
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
# shard inputs and rng
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = jax.random.split(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_003_906) ) < 1E-3
assert np.abs((np.abs(__UpperCAmelCase , dtype=np.floataa ).sum() - 2_373_516.75) ) < 5E-1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa )
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.random.PRNGKey(0 )
__lowerCamelCase = 50
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
# shard inputs and rng
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = jax.random.split(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_003_906) ) < 1E-3
assert np.abs((np.abs(__UpperCAmelCase , dtype=np.floataa ).sum() - 2_373_516.75) ) < 5E-1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = FlaxDDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__UpperCAmelCase , steps_offset=1 , )
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__UpperCAmelCase , safety_checker=__UpperCAmelCase , )
__lowerCamelCase = scheduler.create_state()
__lowerCamelCase = scheduler_state
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.random.PRNGKey(0 )
__lowerCamelCase = 50
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
# shard inputs and rng
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = jax.random.split(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.045_043_945) ) < 1E-3
assert np.abs((np.abs(__UpperCAmelCase , dtype=np.floataa ).sum() - 2_347_693.5) ) < 5E-1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__lowerCamelCase = jax.device_count()
__lowerCamelCase = num_samples * [prompt]
__lowerCamelCase = jax.random.split(jax.random.PRNGKey(0 ) , __UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__UpperCAmelCase , )
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
__lowerCamelCase = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
__lowerCamelCase ,__lowerCamelCase = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__UpperCAmelCase , use_memory_efficient_attention=__UpperCAmelCase , )
__lowerCamelCase = replicate(__UpperCAmelCase )
__lowerCamelCase = pipeline.prepare_inputs(__UpperCAmelCase )
__lowerCamelCase = shard(__UpperCAmelCase )
__lowerCamelCase = pipeline(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , jit=__UpperCAmelCase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
__lowerCamelCase = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 1 |
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = {}
def lowerCamelCase ( self ):
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(__UpperCAmelCase , ''' -> ''' , ''' -> '''.join([str(__UpperCAmelCase ) for j in self.vertex[i]] ) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__UpperCAmelCase )
else:
# else make a new vertex
__lowerCamelCase = [to_vertex]
def lowerCamelCase ( self ):
'''simple docstring'''
# visited array for storing already visited nodes
__lowerCamelCase = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# mark start vertex as visited
__lowerCamelCase = True
print(__UpperCAmelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
a_ = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("""DFS:""")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 | 1 |
def a__ ( _UpperCamelCase : int = 60_08_51_47_51_43 ):
try:
__lowerCamelCase = int(_UpperCamelCase )
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.''' )
__lowerCamelCase = 2
__lowerCamelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
__lowerCamelCase = i
while n % i == 0:
__lowerCamelCase = n // i
i += 1
return int(_UpperCamelCase )
if __name__ == "__main__":
print(f"{solution() = }")
| 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 622 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAmelCase=1024 , __UpperCAmelCase=3.6 ):
'''simple docstring'''
__lowerCamelCase = tokenizer
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = dataset
__lowerCamelCase = seq_length
__lowerCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = iter(self.dataset )
__lowerCamelCase = True
while more_examples:
__lowerCamelCase ,__lowerCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__UpperCAmelCase )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
__lowerCamelCase = False
break
__lowerCamelCase = tokenizer(__UpperCAmelCase , truncation=__UpperCAmelCase )['''input_ids''']
__lowerCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__UpperCAmelCase ) , self.seq_length ):
__lowerCamelCase = all_token_ids[i : i + self.seq_length]
if len(__UpperCAmelCase ) == self.seq_length:
yield torch.tensor(__UpperCAmelCase )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = {'''streaming''': True}
__lowerCamelCase = load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase )
__lowerCamelCase = ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=args.batch_size )
return eval_dataloader
def a__ ( _UpperCamelCase : str ):
model.eval()
__lowerCamelCase = []
for step, batch in enumerate(_UpperCamelCase ):
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase ,labels=_UpperCamelCase )
__lowerCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__lowerCamelCase = torch.mean(torch.cat(_UpperCamelCase ) )
try:
__lowerCamelCase = torch.exp(_UpperCamelCase )
except OverflowError:
__lowerCamelCase = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
a_ = Accelerator()
# Parse configuration
a_ = HfArgumentParser(EvaluationArguments)
a_ = parser.parse_args()
set_seed(args.seed)
# Logging
a_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
a_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
a_ , a_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_ , a_ = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """instructblip_vision_model"""
def __init__( self , __UpperCAmelCase=1408 , __UpperCAmelCase=6144 , __UpperCAmelCase=39 , __UpperCAmelCase=16 , __UpperCAmelCase=224 , __UpperCAmelCase=14 , __UpperCAmelCase="gelu" , __UpperCAmelCase=1E-6 , __UpperCAmelCase=0.0 , __UpperCAmelCase=1E-1_0 , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = hidden_size
__lowerCamelCase = intermediate_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = patch_size
__lowerCamelCase = image_size
__lowerCamelCase = initializer_range
__lowerCamelCase = attention_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = hidden_act
__lowerCamelCase = qkv_bias
@classmethod
def lowerCamelCase ( cls , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
cls._set_token_in_kwargs(__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__lowerCamelCase = 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 __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """instructblip_qformer"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=0 , __UpperCAmelCase="absolute" , __UpperCAmelCase=2 , __UpperCAmelCase=1408 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = position_embedding_type
__lowerCamelCase = cross_attention_frequency
__lowerCamelCase = encoder_hidden_size
@classmethod
def lowerCamelCase ( cls , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
cls._set_token_in_kwargs(__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__lowerCamelCase = config_dict['''qformer_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 __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """instructblip"""
lowerCAmelCase__ = True
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=32 , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
if vision_config is None:
__lowerCamelCase = {}
logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )
if qformer_config is None:
__lowerCamelCase = {}
logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )
if text_config is None:
__lowerCamelCase = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
__lowerCamelCase = InstructBlipVisionConfig(**__UpperCAmelCase )
__lowerCamelCase = InstructBlipQFormerConfig(**__UpperCAmelCase )
__lowerCamelCase = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__lowerCamelCase = CONFIG_MAPPING[text_model_type](**__UpperCAmelCase )
__lowerCamelCase = self.text_config.tie_word_embeddings
__lowerCamelCase = self.text_config.is_encoder_decoder
__lowerCamelCase = num_query_tokens
__lowerCamelCase = self.vision_config.hidden_size
__lowerCamelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__lowerCamelCase = 1.0
__lowerCamelCase = 0.02
@classmethod
def lowerCamelCase ( cls , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase , ):
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__UpperCAmelCase , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.vision_config.to_dict()
__lowerCamelCase = self.qformer_config.to_dict()
__lowerCamelCase = self.text_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 622 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = XLMProphetNetTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''[PAD]'''
__lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__UpperCAmelCase ) , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''Hello World!'''
__lowerCamelCase = [35389, 6672, 49, 2]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 622 | 1 |
import sys
a_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def a__ ( _UpperCamelCase : str = N ):
__lowerCamelCase = -sys.maxsize - 1
for i in range(len(_UpperCamelCase ) - 12 ):
__lowerCamelCase = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
__lowerCamelCase = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
a_ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = None
# source code of `config_class`
__lowerCamelCase = inspect.getsource(_UpperCamelCase )
__lowerCamelCase = _re_checkpoint.findall(_UpperCamelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('''/''' ):
__lowerCamelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
__lowerCamelCase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__lowerCamelCase = ckpt_name
break
return checkpoint
def a__ ( ):
__lowerCamelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
__lowerCamelCase = get_checkpoint_from_config_class(_UpperCamelCase )
__lowerCamelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
__lowerCamelCase = '''\n'''.join(sorted(_UpperCamelCase ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 622 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None ):
'''simple docstring'''
if components is None:
__lowerCamelCase = []
__lowerCamelCase = list(__UpperCAmelCase )
def __len__( self ):
'''simple docstring'''
return len(self.__components )
def __str__( self ):
'''simple docstring'''
return "(" + ",".join(map(__UpperCAmelCase , self.__components ) ) + ")"
def __add__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self )
if size == len(__UpperCAmelCase ):
__lowerCamelCase = [self.__components[i] + other.component(__UpperCAmelCase ) for i in range(__UpperCAmelCase )]
return Vector(__UpperCAmelCase )
else:
raise Exception('''must have the same size''' )
def __sub__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self )
if size == len(__UpperCAmelCase ):
__lowerCamelCase = [self.__components[i] - other.component(__UpperCAmelCase ) for i in range(__UpperCAmelCase )]
return Vector(__UpperCAmelCase )
else: # error case
raise Exception('''must have the same size''' )
@overload
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
...
@overload
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
...
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , (float, int) ):
__lowerCamelCase = [c * other for c in self.__components]
return Vector(__UpperCAmelCase )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(self ) == len(__UpperCAmelCase ):
__lowerCamelCase = len(self )
__lowerCamelCase = [self.__components[i] * other.component(__UpperCAmelCase ) for i in range(__UpperCAmelCase )]
return sum(__UpperCAmelCase )
else: # error case
raise Exception('''invalid operand!''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return Vector(self.__components )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('''index out of range''' )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
assert -len(self.__components ) <= pos < len(self.__components )
__lowerCamelCase = value
def lowerCamelCase ( self ):
'''simple docstring'''
if len(self.__components ) == 0:
raise Exception('''Vector is empty''' )
__lowerCamelCase = [c**2 for c in self.__components]
return math.sqrt(sum(__UpperCAmelCase ) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
__lowerCamelCase = self * other
__lowerCamelCase = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def a__ ( _UpperCamelCase : int ):
assert isinstance(_UpperCamelCase ,_UpperCamelCase )
return Vector([0] * dimension )
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ):
assert isinstance(_UpperCamelCase ,_UpperCamelCase ) and (isinstance(_UpperCamelCase ,_UpperCamelCase ))
__lowerCamelCase = [0] * dimension
__lowerCamelCase = 1
return Vector(_UpperCamelCase )
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : Vector ,_UpperCamelCase : Vector ):
assert (
isinstance(_UpperCamelCase ,_UpperCamelCase )
and isinstance(_UpperCamelCase ,_UpperCamelCase )
and (isinstance(_UpperCamelCase ,(int, float) ))
)
return x * scalar + y
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ,_UpperCamelCase : int ):
random.seed(_UpperCamelCase )
__lowerCamelCase = [random.randint(_UpperCamelCase ,_UpperCamelCase ) for _ in range(_UpperCamelCase )]
return Vector(_UpperCamelCase )
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = matrix
__lowerCamelCase = w
__lowerCamelCase = h
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = ''''''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , __UpperCAmelCase ):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
__lowerCamelCase = []
for i in range(self.__height ):
__lowerCamelCase = [
self.__matrix[i][j] + other.component(__UpperCAmelCase , __UpperCAmelCase )
for j in range(self.__width )
]
matrix.append(__UpperCAmelCase )
return Matrix(__UpperCAmelCase , self.__width , self.__height )
else:
raise Exception('''matrix must have the same dimension!''' )
def __sub__( self , __UpperCAmelCase ):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
__lowerCamelCase = []
for i in range(self.__height ):
__lowerCamelCase = [
self.__matrix[i][j] - other.component(__UpperCAmelCase , __UpperCAmelCase )
for j in range(self.__width )
]
matrix.append(__UpperCAmelCase )
return Matrix(__UpperCAmelCase , self.__width , self.__height )
else:
raise Exception('''matrices must have the same dimension!''' )
@overload
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
...
@overload
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
...
def __mul__( self , __UpperCAmelCase ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ): # matrix-vector
if len(__UpperCAmelCase ) == self.__width:
__lowerCamelCase = zero_vector(self.__height )
for i in range(self.__height ):
__lowerCamelCase = [
self.__matrix[i][j] * other.component(__UpperCAmelCase )
for j in range(self.__width )
]
ans.change_component(__UpperCAmelCase , sum(__UpperCAmelCase ) )
return ans
else:
raise Exception(
'''vector must have the same size as the '''
'''number of columns of the matrix!''' )
elif isinstance(__UpperCAmelCase , (int, float) ): # matrix-scalar
__lowerCamelCase = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(__UpperCAmelCase , self.__width , self.__height )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
return self.__height
def lowerCamelCase ( self ):
'''simple docstring'''
return self.__width
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('''change_component: indices out of bounds''' )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
__lowerCamelCase = value
else:
raise Exception('''change_component: indices out of bounds''' )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
__lowerCamelCase = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(__UpperCAmelCase ) ):
__lowerCamelCase = minor[i][:y] + minor[i][y + 1 :]
return Matrix(__UpperCAmelCase , self.__width - 1 , self.__height - 1 ).determinant()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(__UpperCAmelCase , __UpperCAmelCase )
else:
raise Exception('''Indices out of bounds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if self.__height < 1:
raise Exception('''Matrix has no element''' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__lowerCamelCase = [
self.__matrix[0][y] * self.cofactor(0 , __UpperCAmelCase ) for y in range(self.__width )
]
return sum(__UpperCAmelCase )
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = [[0] * n for _ in range(_UpperCamelCase )]
return Matrix(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ,_UpperCamelCase : int ,_UpperCamelCase : int ):
random.seed(_UpperCamelCase )
__lowerCamelCase = [
[random.randint(_UpperCamelCase ,_UpperCamelCase ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )
]
return Matrix(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
| 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
a_ = """platform"""
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ,_UpperCamelCase : List[str]=None ,_UpperCamelCase : int=None ,_UpperCamelCase : Dict=None ,_UpperCamelCase : List[Any]=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : int=None ,):
if attention_mask is None:
__lowerCamelCase = np.where(input_ids != config.pad_token_id ,1 ,0 )
if decoder_attention_mask is None:
__lowerCamelCase = np.where(decoder_input_ids != config.pad_token_id ,1 ,0 )
if head_mask is None:
__lowerCamelCase = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowerCamelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowerCamelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=0.02 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = initializer_range
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
__lowerCamelCase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
__lowerCamelCase = shift_tokens_right(__UpperCAmelCase , 1 , 2 )
__lowerCamelCase = BlenderbotSmallConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=__UpperCAmelCase , )
__lowerCamelCase = prepare_blenderbot_inputs_dict(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(__UpperCAmelCase )
__lowerCamelCase = model.encode(inputs_dict['''input_ids'''] )
__lowerCamelCase ,__lowerCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowerCamelCase = model.init_cache(decoder_input_ids.shape[0] , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__lowerCamelCase = model.decode(
decoder_input_ids[:, :-1] , __UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase , decoder_position_ids=__UpperCAmelCase , )
__lowerCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__lowerCamelCase = model.decode(
decoder_input_ids[:, -1:] , __UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__UpperCAmelCase , )
__lowerCamelCase = model.decode(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = 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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(__UpperCAmelCase )
__lowerCamelCase = model.encode(inputs_dict['''input_ids'''] )
__lowerCamelCase ,__lowerCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowerCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__lowerCamelCase = model.init_cache(decoder_input_ids.shape[0] , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__lowerCamelCase = model.decode(
decoder_input_ids[:, :-1] , __UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase , decoder_position_ids=__UpperCAmelCase , )
__lowerCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__lowerCamelCase = model.decode(
decoder_input_ids[:, -1:] , __UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__UpperCAmelCase , decoder_position_ids=__UpperCAmelCase , )
__lowerCamelCase = model.decode(__UpperCAmelCase , __UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase )
__lowerCamelCase = 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 __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = 9_9
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
__lowerCamelCase = input_ids.shape[0]
__lowerCamelCase = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self._get_config_and_data()
__lowerCamelCase = FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase )
__lowerCamelCase = lm_model(input_ids=__UpperCAmelCase )
__lowerCamelCase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
__lowerCamelCase = FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase )
__lowerCamelCase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
__lowerCamelCase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
__lowerCamelCase = lm_model(input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase )
__lowerCamelCase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
__lowerCamelCase = shift_tokens_right(__UpperCAmelCase , 1 , 2 )
__lowerCamelCase = np.equal(__UpperCAmelCase , 1 ).astype(np.floataa ).sum()
__lowerCamelCase = np.equal(__UpperCAmelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(__UpperCAmelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase , lowerCAmelCase__ ):
lowerCAmelCase__ = True
lowerCAmelCase__ = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowerCAmelCase__ = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = FlaxBlenderbotSmallModelTester(self )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowerCamelCase = self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = model_class(__UpperCAmelCase )
@jax.jit
def encode_jitted(__UpperCAmelCase , __UpperCAmelCase=None , **__UpperCAmelCase ):
return model.encode(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase )
with self.subTest('''JIT Enabled''' ):
__lowerCamelCase = encode_jitted(**__UpperCAmelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowerCamelCase = encode_jitted(**__UpperCAmelCase ).to_tuple()
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for jitted_output, output in zip(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
__lowerCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return model.decode(
decoder_input_ids=__UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , encoder_outputs=__UpperCAmelCase , )
with self.subTest('''JIT Enabled''' ):
__lowerCamelCase = decode_jitted(**__UpperCAmelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowerCamelCase = decode_jitted(**__UpperCAmelCase ).to_tuple()
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for jitted_output, output in zip(__UpperCAmelCase , __UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('''facebook/blenderbot_small-90M''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
__lowerCamelCase = np.ones((1, 1) ) * model.config.eos_token_id
__lowerCamelCase = model(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
| 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __UpperCAmelCase , )
super().__init__(args=__UpperCAmelCase , **__UpperCAmelCase )
| 622 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , use_stable_embedding=__UpperCAmelCase , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = OpenLlamaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = OpenLlamaModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = OpenLlamaForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = OpenLlamaForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
# first forward pass
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase , )
__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(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , )['''hidden_states'''][0]
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , )['''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(__UpperCAmelCase , __UpperCAmelCase , atol=1E-3 ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowerCAmelCase__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowerCAmelCase__ = (
{
"""feature-extraction""": OpenLlamaModel,
"""text-classification""": OpenLlamaForSequenceClassification,
"""text-generation""": OpenLlamaForCausalLM,
"""zero-shot""": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = OpenLlamaModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = input_dict['''input_ids''']
__lowerCamelCase = input_ids.ne(1 ).to(__UpperCAmelCase )
__lowerCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCamelCase = OpenLlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = '''single_label_classification'''
__lowerCamelCase = input_dict['''input_ids''']
__lowerCamelCase = input_ids.ne(1 ).to(__UpperCAmelCase )
__lowerCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCamelCase = OpenLlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = '''multi_label_classification'''
__lowerCamelCase = input_dict['''input_ids''']
__lowerCamelCase = input_ids.ne(1 ).to(__UpperCAmelCase )
__lowerCamelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__lowerCamelCase = OpenLlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__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 = OpenLlamaModel(__UpperCAmelCase )
original_model.to(__UpperCAmelCase )
original_model.eval()
__lowerCamelCase = original_model(__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = original_model(__UpperCAmelCase ).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''': 10.0}
__lowerCamelCase = OpenLlamaModel(__UpperCAmelCase )
scaled_model.to(__UpperCAmelCase )
scaled_model.eval()
__lowerCamelCase = scaled_model(__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = scaled_model(__UpperCAmelCase ).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(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
| 622 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=12 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=0.02 , __UpperCAmelCase=0 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = projection_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = bos_token_id
def lowerCamelCase ( 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] )
if input_mask is not None:
__lowerCamelCase = input_mask.numpy()
__lowerCamelCase ,__lowerCamelCase = input_mask.shape
__lowerCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__UpperCAmelCase ):
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFBlipTextModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , training=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TFBlipTextModel,) if is_tf_available() else ()
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BlipTextModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TFBlipTextModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase=True ):
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase )
| 622 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : List[str] ,_UpperCamelCase : List[Any]=None ,_UpperCamelCase : Any=None ):
if attention_mask is None:
__lowerCamelCase = tf.cast(tf.math.not_equal(_UpperCamelCase ,config.pad_token_id ) ,tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __lowerCAmelCase :
lowerCAmelCase__ = OPTConfig
lowerCAmelCase__ = {}
lowerCAmelCase__ = """gelu"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=20 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = embed_dim
__lowerCamelCase = word_embed_proj_dim
__lowerCamelCase = False
def lowerCamelCase ( 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 = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__UpperCAmelCase , **self.config_updates , )
__lowerCamelCase = prepare_opt_inputs_dict(__UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel(config=__UpperCAmelCase )
__lowerCamelCase = inputs_dict['''input_ids''']
__lowerCamelCase = input_ids[:1, :]
__lowerCamelCase = inputs_dict['''attention_mask'''][:1, :]
__lowerCamelCase = 1
# first forward pass
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
__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(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__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(__UpperCAmelCase , __UpperCAmelCase , rtol=1E-3 )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__lowerCamelCase = model_class(config=__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__lowerCamelCase = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __UpperCAmelCase )
# check that weights remain the same after resizing
__lowerCamelCase = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __UpperCAmelCase )
__lowerCamelCase = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
def a__ ( _UpperCamelCase : Optional[Any] ):
return tf.constant(_UpperCamelCase ,dtype=tf.intaa )
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = 9_9
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__lowerCamelCase = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__lowerCamelCase = input_ids.shape[0]
__lowerCamelCase = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel.from_pretrained('''facebook/opt-350m''' )
__lowerCamelCase = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase = tf.not_equal(__UpperCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
__lowerCamelCase = model(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = (1, 11, 512)
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = tf.constant(
[[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-3 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = xla_generate(__UpperCAmelCase , __UpperCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = '''facebook/opt-350m'''
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(self.path_model )
__lowerCamelCase = GPTaTokenizer.from_pretrained(self.path_model )
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__lowerCamelCase = tf.constant(
[
[1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
[-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
[0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
[6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
] )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-125m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = '''left'''
# use different length sentences to test batching
__lowerCamelCase = [
'''Hello, my dog is a little''',
'''Today, I''',
]
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase )
__lowerCamelCase = inputs['''input_ids''']
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , attention_mask=inputs['''attention_mask'''] )
__lowerCamelCase = tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase )
__lowerCamelCase = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa ) )
__lowerCamelCase = tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , max_length=model.config.max_length - num_paddings )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , [non_padded_sentence, padded_sentence] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 | 1 |
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.25) = }")
print(f"{price_plus_tax(1_25.50, 0.05) = }")
| 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ = logging.getLogger(__name__)
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[Any] ):
__lowerCamelCase = np.argmax(_UpperCamelCase ,axis=1 )
return np.sum(outputs == labels )
def a__ ( _UpperCamelCase : Optional[int] ):
with open(_UpperCamelCase ,encoding='''utf_8''' ) as f:
__lowerCamelCase = csv.reader(_UpperCamelCase )
__lowerCamelCase = []
next(_UpperCamelCase ) # skip the first line
for line in tqdm(_UpperCamelCase ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Dict ,_UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Dict ):
__lowerCamelCase = []
for dataset in encoded_datasets:
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
__lowerCamelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCamelCase ):
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = mc_label
__lowerCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,default='''openai-gpt''' ,help='''pretrained model name''' )
parser.add_argument('''--do_train''' ,action='''store_true''' ,help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' ,action='''store_true''' ,help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42 )
parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3 )
parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8 )
parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16 )
parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1 )
parser.add_argument(
'''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) ,)
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,)
parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5 )
parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01 )
parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9 )
parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74 )
parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
__lowerCamelCase = parser.parse_args()
print(_UpperCamelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
__lowerCamelCase = torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase ,_UpperCamelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase = ['''_start_''', '''_delimiter_''', '''_classify_''']
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCamelCase )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCamelCase ) )
model.to(_UpperCamelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCamelCase : Dict ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase ) )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
return obj
return [tokenize_and_encode(_UpperCamelCase ) for o in obj]
logger.info('''Encoding dataset...''' )
__lowerCamelCase = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase = (train_dataset, eval_dataset)
__lowerCamelCase = tokenize_and_encode(_UpperCamelCase )
# Compute the max input length for the Transformer
__lowerCamelCase = model.config.n_positions // 2 - 2
__lowerCamelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase = min(_UpperCamelCase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase = pre_process_datasets(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,*_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = RandomSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size )
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = SequentialSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase = args.max_steps
__lowerCamelCase = args.max_steps // (len(_UpperCamelCase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase = len(_UpperCamelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase = list(model.named_parameters() )
__lowerCamelCase = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
__lowerCamelCase = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
__lowerCamelCase = AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon )
__lowerCamelCase = get_linear_schedule_with_warmup(
_UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase )
if args.do_train:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc='''Epoch''' ):
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(_UpperCamelCase ,desc='''Training''' )
for step, batch in enumerate(_UpperCamelCase ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
__lowerCamelCase = model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase = '''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase = model.module if hasattr(_UpperCamelCase ,'''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
torch.save(model_to_save.state_dict() ,_UpperCamelCase )
model_to_save.config.to_json_file(_UpperCamelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCamelCase )
if args.do_eval:
model.eval()
__lowerCamelCase ,__lowerCamelCase = 0, 0
__lowerCamelCase ,__lowerCamelCase = 0, 0
for batch in tqdm(_UpperCamelCase ,desc='''Evaluating''' ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
with torch.no_grad():
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = model(
_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = mc_logits.detach().cpu().numpy()
__lowerCamelCase = mc_labels.to('''cpu''' ).numpy()
__lowerCamelCase = accuracy(_UpperCamelCase ,_UpperCamelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase = eval_loss / nb_eval_steps
__lowerCamelCase = eval_accuracy / nb_eval_examples
__lowerCamelCase = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
__lowerCamelCase = os.path.join(args.output_dir ,'''eval_results.txt''' )
with open(_UpperCamelCase ,'''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' ,_UpperCamelCase ,str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 622 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a__ ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_UpperCamelCase ):
requests.request('''GET''' ,'''https://huggingface.co''' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('''GET''' ,'''https://huggingface.co''' ,timeout=1.0 )
@pytest.mark.integration
def a__ ( ):
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('''GET''' ,'''https://huggingface.co''' )
def a__ ( ):
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_UpperCamelCase ):
http_head('''https://huggingface.co''' )
| 622 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAmelCase=1024 , __UpperCAmelCase=3.6 ):
'''simple docstring'''
__lowerCamelCase = tokenizer
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = dataset
__lowerCamelCase = seq_length
__lowerCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = iter(self.dataset )
__lowerCamelCase = True
while more_examples:
__lowerCamelCase ,__lowerCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__UpperCAmelCase )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
__lowerCamelCase = False
break
__lowerCamelCase = tokenizer(__UpperCAmelCase , truncation=__UpperCAmelCase )['''input_ids''']
__lowerCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__UpperCAmelCase ) , self.seq_length ):
__lowerCamelCase = all_token_ids[i : i + self.seq_length]
if len(__UpperCAmelCase ) == self.seq_length:
yield torch.tensor(__UpperCAmelCase )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = {'''streaming''': True}
__lowerCamelCase = load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase )
__lowerCamelCase = ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=args.batch_size )
return eval_dataloader
def a__ ( _UpperCamelCase : str ):
model.eval()
__lowerCamelCase = []
for step, batch in enumerate(_UpperCamelCase ):
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase ,labels=_UpperCamelCase )
__lowerCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__lowerCamelCase = torch.mean(torch.cat(_UpperCamelCase ) )
try:
__lowerCamelCase = torch.exp(_UpperCamelCase )
except OverflowError:
__lowerCamelCase = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
a_ = Accelerator()
# Parse configuration
a_ = HfArgumentParser(EvaluationArguments)
a_ = parser.parse_args()
set_seed(args.seed)
# Logging
a_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
a_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
a_ , a_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_ , a_ = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 622 | 1 |
# 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.test_utils import execute_subprocess_async
def a__ ( _UpperCamelCase : List[str]=None ):
if subparsers is not None:
__lowerCamelCase = subparsers.add_parser('''test''' )
else:
__lowerCamelCase = argparse.ArgumentParser('''Accelerate test command''' )
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 : Optional[Any] ):
__lowerCamelCase = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
__lowerCamelCase = script_name
else:
__lowerCamelCase = F"""--config_file={args.config_file} {script_name}"""
__lowerCamelCase = ['''accelerate-launch'''] + test_args.split()
__lowerCamelCase = execute_subprocess_async(_UpperCamelCase ,env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def a__ ( ):
__lowerCamelCase = test_command_parser()
__lowerCamelCase = parser.parse_args()
test_command(_UpperCamelCase )
if __name__ == "__main__":
main()
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """lxmert"""
lowerCAmelCase__ = {}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=9500 , __UpperCAmelCase=1600 , __UpperCAmelCase=400 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=9 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=2048 , __UpperCAmelCase=4 , __UpperCAmelCase=6.67 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = num_qa_labels
__lowerCamelCase = num_object_labels
__lowerCamelCase = num_attr_labels
__lowerCamelCase = l_layers
__lowerCamelCase = x_layers
__lowerCamelCase = r_layers
__lowerCamelCase = visual_feat_dim
__lowerCamelCase = visual_pos_dim
__lowerCamelCase = visual_loss_normalizer
__lowerCamelCase = task_matched
__lowerCamelCase = task_mask_lm
__lowerCamelCase = task_obj_predict
__lowerCamelCase = task_qa
__lowerCamelCase = visual_obj_loss
__lowerCamelCase = visual_attr_loss
__lowerCamelCase = visual_feat_loss
__lowerCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__UpperCAmelCase )
| 622 | 1 |
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict=False ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ) and isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = len(set_a.intersection(_UpperCamelCase ) )
if alternative_union:
__lowerCamelCase = len(_UpperCamelCase ) + len(_UpperCamelCase )
else:
__lowerCamelCase = len(set_a.union(_UpperCamelCase ) )
return intersection / union
if isinstance(_UpperCamelCase ,(list, tuple) ) and isinstance(_UpperCamelCase ,(list, tuple) ):
__lowerCamelCase = [element for element in set_a if element in set_b]
if alternative_union:
__lowerCamelCase = len(_UpperCamelCase ) + len(_UpperCamelCase )
return len(_UpperCamelCase ) / union
else:
__lowerCamelCase = set_a + [element for element in set_b if element not in set_a]
return len(_UpperCamelCase ) / len(_UpperCamelCase )
return len(_UpperCamelCase ) / len(_UpperCamelCase )
return None
if __name__ == "__main__":
a_ = {"""a""", """b""", """c""", """d""", """e"""}
a_ = {"""c""", """d""", """e""", """f""", """h""", """i"""}
print(jaccard_similarity(set_a, set_b))
| 622 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length, 2) ,_UpperCamelCase )
else:
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length) ,_UpperCamelCase )
for i, tensor in enumerate(_UpperCamelCase ):
if padding_side == "right":
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
else:
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = ord(_UpperCamelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__lowerCamelCase = unicodedata.category(_UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = True
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = -1_0_0
lowerCAmelCase__ = "pt"
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
import torch
__lowerCamelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCamelCase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCamelCase = self.tokenizer.pad(
__UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCamelCase = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCamelCase = self.tokenizer.padding_side
if padding_side == "right":
__lowerCamelCase = [
list(__UpperCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) for label in labels
]
else:
__lowerCamelCase = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) + list(__UpperCAmelCase ) for label in labels
]
__lowerCamelCase = [feature['''ner_tags'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , -1 , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = [feature['''original_entity_spans'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , (-1, -1) , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {k: torch.tensor(__UpperCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 622 | 1 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = BarthezTokenizer
lowerCAmelCase__ = BarthezTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=__UpperCAmelCase )
__lowerCamelCase = tokenizer
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''<pad>'''
__lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__UpperCAmelCase ) , 101122 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
__lowerCamelCase = [0, 57, 3018, 70307, 91, 2]
__lowerCamelCase = self.tokenizer(
__UpperCAmelCase , max_length=len(__UpperCAmelCase ) , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors='''pt''' )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
__lowerCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = '''I was born in 92000, and this is falsé.'''
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
__lowerCamelCase = rust_tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__lowerCamelCase = rust_tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = tokenizer.encode(__UpperCAmelCase )
__lowerCamelCase = rust_tokenizer.encode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
__lowerCamelCase = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=__UpperCAmelCase , )
| 622 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=[1, 1, 2] , __UpperCAmelCase=1 , __UpperCAmelCase=32 , __UpperCAmelCase=4 , __UpperCAmelCase=8 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=3 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=False , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = block_sizes
__lowerCamelCase = num_decoder_layers
__lowerCamelCase = d_model
__lowerCamelCase = n_head
__lowerCamelCase = d_head
__lowerCamelCase = d_inner
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = 2
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = initializer_std
# Used in the tests to check the size of the first attention layer
__lowerCamelCase = n_head
# Used in the tests to check the size of the first hidden state
__lowerCamelCase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
__lowerCamelCase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
__lowerCamelCase = self.num_hidden_layers + 2
def lowerCamelCase ( 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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__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 = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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, 2, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForPreTraining(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForMaskedLM(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForSequenceClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = TFFunnelForMultipleChoice(config=__UpperCAmelCase )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForTokenClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForQuestionAnswering(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self , base=__UpperCAmelCase )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
| 622 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(_UpperCamelCase ).content ).xpath(_UpperCamelCase ) )
a_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 622 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(_UpperCamelCase ).content ).xpath(_UpperCamelCase ) )
a_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__lowerCamelCase = index + 1
elif index + 1 == len(_UpperCamelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 622 | 1 |
def a__ ( _UpperCamelCase : str ):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = credit_card_number
__lowerCamelCase = 0
__lowerCamelCase = len(_UpperCamelCase ) - 2
for i in range(_UpperCamelCase ,-1 ,-2 ):
# double the value of every second digit
__lowerCamelCase = 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
__lowerCamelCase = cc_number[:i] + str(_UpperCamelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCamelCase ) - 1 ,-1 ,-2 ):
total += int(cc_number[i] )
return total % 10 == 0
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = 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(_UpperCamelCase ) <= 16:
print(F"""{error_message} of its length.""" )
return False
if not validate_initial_digits(_UpperCamelCase ):
print(F"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(_UpperCamelCase ):
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""")
| 622 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = 8
# DPR tok
__lowerCamelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowerCamelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , DPR_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] ) )
# BART tok
__lowerCamelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__lowerCamelCase = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__lowerCamelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__lowerCamelCase = {'''unk_token''': '''<unk>'''}
__lowerCamelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCAmelCase ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
__lowerCamelCase = os.path.join(self.tmpdirname , '''dataset''' )
__lowerCamelCase = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase ) , )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
__lowerCamelCase = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
__lowerCamelCase = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
__lowerCamelCase = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(__UpperCAmelCase , open(__UpperCAmelCase , '''wb''' ) )
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = self.get_dummy_dataset()
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
import torch
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
__lowerCamelCase = retriever(
__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase , return_tensors='''pt''' , )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dpr_ctx_encoder_tokenizer()
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase )
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
self.assertEqual(
len(__UpperCAmelCase ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , __UpperCAmelCase ) # check for doc token related keys in dictionary.
| 622 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 |
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_ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """poolformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=4.0 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[64, 128, 320, 512] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[2, 1, 1, 1] , __UpperCAmelCase=4 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.02 , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = stride
__lowerCamelCase = padding
__lowerCamelCase = pool_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = mlp_ratio
__lowerCamelCase = depths
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_layer_scale
__lowerCamelCase = layer_scale_init_value
__lowerCamelCase = initializer_range
super().__init__(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 2E-3
| 622 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
a_ = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
a_ = {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}
a_ = """zero2"""
a_ = """zero3"""
a_ = [ZEROa, ZEROa]
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Tuple ,_UpperCamelCase : Any ):
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__lowerCamelCase = parameterized.to_safe_name('''_'''.join(str(_UpperCamelCase ) for x in param.args ) )
return F"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
a_ = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
@parameterized.expand(__UpperCAmelCase , name_func=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.run_and_check(
stage=__UpperCAmelCase , model=__UpperCAmelCase , distributed=__UpperCAmelCase , fpaa=__UpperCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(__UpperCAmelCase , name_func=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.run_and_check(
stage=__UpperCAmelCase , model=__UpperCAmelCase , distributed=__UpperCAmelCase , fpaa=__UpperCAmelCase , )
@parameterized.expand(__UpperCAmelCase , name_func=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.run_and_check(
stage=__UpperCAmelCase , model=__UpperCAmelCase , distributed=__UpperCAmelCase , fpaa=__UpperCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(__UpperCAmelCase , name_func=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.run_and_check(
stage=__UpperCAmelCase , model=__UpperCAmelCase , distributed=__UpperCAmelCase , fpaa=__UpperCAmelCase , )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 10 , __UpperCAmelCase = True , __UpperCAmelCase = True , __UpperCAmelCase = True , ):
'''simple docstring'''
__lowerCamelCase = models[model]
__lowerCamelCase = self.run_trainer(
stage=__UpperCAmelCase , model_name=__UpperCAmelCase , eval_steps=__UpperCAmelCase , num_train_epochs=1 , distributed=__UpperCAmelCase , fpaa=__UpperCAmelCase , )
self.do_checks(__UpperCAmelCase )
return output_dir
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 10 , __UpperCAmelCase = 1 , __UpperCAmelCase = True , __UpperCAmelCase = True , ):
'''simple docstring'''
__lowerCamelCase = self.get_auto_remove_tmp_dir('''./xxx''' , after=__UpperCAmelCase )
__lowerCamelCase = F"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(__UpperCAmelCase )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__lowerCamelCase = F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__lowerCamelCase = [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__lowerCamelCase = self.get_launcher(__UpperCAmelCase )
__lowerCamelCase = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__UpperCAmelCase , env=self.get_env() )
return output_dir
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
__lowerCamelCase = min(2 , get_gpu_count() ) if distributed else 1
return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """visual_bert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=512 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = visual_embedding_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = bypass_transformer
__lowerCamelCase = special_visual_initialize
| 622 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def a__ ( _UpperCamelCase : Any ):
__lowerCamelCase = torch.exp(_UpperCamelCase )
__lowerCamelCase = torch.sum(_UpperCamelCase ,dim=1 ) # sum of exp(x_i)
__lowerCamelCase = torch.sum(x * exp_x ,dim=1 ) # sum of x_i * exp(x_i)
return torch.log(_UpperCamelCase ) - B / A
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = config.output_attentions
__lowerCamelCase = config.output_hidden_states
__lowerCamelCase = nn.ModuleList([BertLayer(__UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
__lowerCamelCase = nn.ModuleList([BertHighway(__UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
__lowerCamelCase = [-1 for _ in range(config.num_hidden_layers )]
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if (type(__UpperCAmelCase ) is float) or (type(__UpperCAmelCase ) is int):
for i in range(len(self.early_exit_entropy ) ):
__lowerCamelCase = x
else:
__lowerCamelCase = x
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = ()
__lowerCamelCase = ()
__lowerCamelCase = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
__lowerCamelCase = all_hidden_states + (hidden_states,)
__lowerCamelCase = layer_module(
__UpperCAmelCase , __UpperCAmelCase , head_mask[i] , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = layer_outputs[0]
if self.output_attentions:
__lowerCamelCase = all_attentions + (layer_outputs[1],)
__lowerCamelCase = (hidden_states,)
if self.output_hidden_states:
__lowerCamelCase = current_outputs + (all_hidden_states,)
if self.output_attentions:
__lowerCamelCase = current_outputs + (all_attentions,)
__lowerCamelCase = self.highway[i](__UpperCAmelCase )
# logits, pooled_output
if not self.training:
__lowerCamelCase = highway_exit[0]
__lowerCamelCase = entropy(__UpperCAmelCase )
__lowerCamelCase = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
__lowerCamelCase = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
__lowerCamelCase = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(__UpperCAmelCase , i + 1 )
else:
__lowerCamelCase = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
__lowerCamelCase = all_hidden_states + (hidden_states,)
__lowerCamelCase = (hidden_states,)
if self.output_hidden_states:
__lowerCamelCase = outputs + (all_hidden_states,)
if self.output_attentions:
__lowerCamelCase = outputs + (all_attentions,)
__lowerCamelCase = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"""The Bert Model transformer with early exiting (DeeBERT). """ , lowerCAmelCase__ , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
super().__init__(__UpperCAmelCase )
__lowerCamelCase = config
__lowerCamelCase = BertEmbeddings(__UpperCAmelCase )
__lowerCamelCase = DeeBertEncoder(__UpperCAmelCase )
__lowerCamelCase = BertPooler(__UpperCAmelCase )
self.init_weights()
def lowerCamelCase ( self ):
'''simple docstring'''
self.encoder.init_highway_pooler(self.pooler )
def lowerCamelCase ( self ):
'''simple docstring'''
return self.embeddings.word_embeddings
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = value
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(__UpperCAmelCase )
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ):
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
__lowerCamelCase = input_ids.size()
elif inputs_embeds is not None:
__lowerCamelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
__lowerCamelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__lowerCamelCase = torch.ones(__UpperCAmelCase , device=__UpperCAmelCase )
if encoder_attention_mask is None:
__lowerCamelCase = torch.ones(__UpperCAmelCase , device=__UpperCAmelCase )
if token_type_ids is None:
__lowerCamelCase = torch.zeros(__UpperCAmelCase , dtype=torch.long , device=__UpperCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__lowerCamelCase = self.get_extended_attention_mask(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
__lowerCamelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
__lowerCamelCase = encoder_attention_mask[:, None, None, :]
__lowerCamelCase = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
__lowerCamelCase = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__lowerCamelCase = self.get_head_mask(__UpperCAmelCase , self.config.num_hidden_layers )
__lowerCamelCase = self.embeddings(
input_ids=__UpperCAmelCase , position_ids=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , inputs_embeds=__UpperCAmelCase )
__lowerCamelCase = self.encoder(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , head_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = encoder_outputs[0]
__lowerCamelCase = self.pooler(__UpperCAmelCase )
__lowerCamelCase = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = message
__lowerCamelCase = exit_layer # start from 1!
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = BertPooler(__UpperCAmelCase )
__lowerCamelCase = nn.Dropout(config.hidden_dropout_prob )
__lowerCamelCase = nn.Linear(config.hidden_size , config.num_labels )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# Pooler
__lowerCamelCase = encoder_outputs[0]
__lowerCamelCase = self.pooler(__UpperCAmelCase )
# "return" pooler_output
# BertModel
__lowerCamelCase = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
__lowerCamelCase = bmodel_output[1]
__lowerCamelCase = self.dropout(__UpperCAmelCase )
__lowerCamelCase = self.classifier(__UpperCAmelCase )
return logits, pooled_output
@add_start_docstrings(
"""Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. """ , lowerCAmelCase__ , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
super().__init__(__UpperCAmelCase )
__lowerCamelCase = config.num_labels
__lowerCamelCase = config.num_hidden_layers
__lowerCamelCase = DeeBertModel(__UpperCAmelCase )
__lowerCamelCase = nn.Dropout(config.hidden_dropout_prob )
__lowerCamelCase = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=-1 , __UpperCAmelCase=False , ):
'''simple docstring'''
__lowerCamelCase = self.num_layers
try:
__lowerCamelCase = self.bert(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , position_ids=__UpperCAmelCase , head_mask=__UpperCAmelCase , inputs_embeds=__UpperCAmelCase , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
__lowerCamelCase = outputs[1]
__lowerCamelCase = self.dropout(__UpperCAmelCase )
__lowerCamelCase = self.classifier(__UpperCAmelCase )
__lowerCamelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
__lowerCamelCase = e.message
__lowerCamelCase = e.exit_layer
__lowerCamelCase = outputs[0]
if not self.training:
__lowerCamelCase = entropy(__UpperCAmelCase )
__lowerCamelCase = []
__lowerCamelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
__lowerCamelCase = MSELoss()
__lowerCamelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
__lowerCamelCase = CrossEntropyLoss()
__lowerCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
__lowerCamelCase = []
for highway_exit in outputs[-1]:
__lowerCamelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(__UpperCAmelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
__lowerCamelCase = MSELoss()
__lowerCamelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
__lowerCamelCase = CrossEntropyLoss()
__lowerCamelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__UpperCAmelCase )
if train_highway:
__lowerCamelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
__lowerCamelCase = (loss,) + outputs
if not self.training:
__lowerCamelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
__lowerCamelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 622 |
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 __lowerCAmelCase ( lowerCAmelCase__ ):
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 , ):
'''simple docstring'''
# 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.
__lowerCamelCase = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
__lowerCamelCase = {} 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 , )
__lowerCamelCase = do_lower_case
__lowerCamelCase = remove_space
__lowerCamelCase = keep_accents
__lowerCamelCase = vocab_file
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {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 ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.remove_space:
__lowerCamelCase = ''' '''.join(inputs.strip().split() )
else:
__lowerCamelCase = inputs
__lowerCamelCase = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__lowerCamelCase = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
__lowerCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
__lowerCamelCase = outputs.lower()
return outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.preprocess_text(__UpperCAmelCase )
__lowerCamelCase = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
__lowerCamelCase = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__lowerCamelCase = 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:
__lowerCamelCase = cur_pieces[1:]
else:
__lowerCamelCase = 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 ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = ''''''
__lowerCamelCase = 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
__lowerCamelCase = True
__lowerCamelCase = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__lowerCamelCase = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = 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:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 622 | 1 |
def a__ ( _UpperCamelCase : list ):
if len(_UpperCamelCase ) < 2:
return collection
def circle_sort_util(_UpperCamelCase : list ,_UpperCamelCase : int ,_UpperCamelCase : int ) -> bool:
__lowerCamelCase = False
if low == high:
return swapped
__lowerCamelCase = low
__lowerCamelCase = high
while left < right:
if collection[left] > collection[right]:
__lowerCamelCase ,__lowerCamelCase = (
collection[right],
collection[left],
)
__lowerCamelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
__lowerCamelCase ,__lowerCamelCase = (
collection[right + 1],
collection[left],
)
__lowerCamelCase = True
__lowerCamelCase = low + int((high - low) / 2 )
__lowerCamelCase = circle_sort_util(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = circle_sort_util(_UpperCamelCase ,mid + 1 ,_UpperCamelCase )
return swapped or left_swap or right_swap
__lowerCamelCase = True
while is_not_sorted is True:
__lowerCamelCase = circle_sort_util(_UpperCamelCase ,0 ,len(_UpperCamelCase ) - 1 )
return collection
if __name__ == "__main__":
a_ = input("""Enter numbers separated by a comma:\n""").strip()
a_ = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
a_ = {
"""vocab_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
),
},
"""merges_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
),
},
"""tokenizer_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
"""roberta-base-openai-detector""": (
"""https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
),
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
),
},
}
a_ = {
"""roberta-base""": 512,
"""roberta-large""": 512,
"""roberta-large-mnli""": 512,
"""distilroberta-base""": 512,
"""roberta-base-openai-detector""": 512,
"""roberta-large-openai-detector""": 512,
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["""input_ids""", """attention_mask"""]
lowerCAmelCase__ = RobertaTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="replace" , __UpperCAmelCase="<s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="<s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="<mask>" , __UpperCAmelCase=False , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , errors=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __UpperCAmelCase ) != add_prefix_space:
__lowerCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**__UpperCAmelCase )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = '''post_processor'''
__lowerCamelCase = getattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
if tokenizer_component_instance:
__lowerCamelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__lowerCamelCase = tuple(state['''sep'''] )
if "cls" in state:
__lowerCamelCase = tuple(state['''cls'''] )
__lowerCamelCase = False
if state.get('''add_prefix_space''' , __UpperCAmelCase ) != add_prefix_space:
__lowerCamelCase = add_prefix_space
__lowerCamelCase = True
if state.get('''trim_offsets''' , __UpperCAmelCase ) != trim_offsets:
__lowerCamelCase = trim_offsets
__lowerCamelCase = True
if changes_to_apply:
__lowerCamelCase = getattr(__UpperCAmelCase , state.pop('''type''' ) )
__lowerCamelCase = component_class(**__UpperCAmelCase )
setattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else value
__lowerCamelCase = value
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
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(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
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(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = [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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__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 + sep + token_ids_a + sep ) * [0]
| 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 | 1 |
from math import loga
def a__ ( _UpperCamelCase : int ):
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 622 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
__lowerCamelCase = MaskFormerConfig(backbone_config=_UpperCamelCase )
__lowerCamelCase = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
__lowerCamelCase = 8_47
__lowerCamelCase = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
__lowerCamelCase = 1_50
__lowerCamelCase = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
__lowerCamelCase = 1_71
__lowerCamelCase = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
__lowerCamelCase = 1_33
__lowerCamelCase = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
__lowerCamelCase = 19
__lowerCamelCase = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
__lowerCamelCase = 65
__lowerCamelCase = '''mapillary-vistas-id2label.json'''
__lowerCamelCase = json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset''' ) ,'''r''' ) )
__lowerCamelCase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
return config
def a__ ( _UpperCamelCase : Optional[int] ):
__lowerCamelCase = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : Optional[int] ,_UpperCamelCase : Dict ):
__lowerCamelCase = dct.pop(_UpperCamelCase )
__lowerCamelCase = val
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Optional[int] ):
__lowerCamelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowerCamelCase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
__lowerCamelCase = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[:dim, :]
__lowerCamelCase = in_proj_bias[: dim]
__lowerCamelCase = in_proj_weight[
dim : dim * 2, :
]
__lowerCamelCase = in_proj_bias[
dim : dim * 2
]
__lowerCamelCase = in_proj_weight[
-dim :, :
]
__lowerCamelCase = in_proj_bias[-dim :]
# fmt: on
def a__ ( _UpperCamelCase : Optional[Any] ,_UpperCamelCase : List[str] ):
# fmt: off
__lowerCamelCase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
__lowerCamelCase = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[: hidden_size, :]
__lowerCamelCase = in_proj_bias[:config.hidden_size]
__lowerCamelCase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowerCamelCase = in_proj_bias[hidden_size : hidden_size * 2]
__lowerCamelCase = in_proj_weight[-hidden_size :, :]
__lowerCamelCase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
__lowerCamelCase = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[: hidden_size, :]
__lowerCamelCase = in_proj_bias[:config.hidden_size]
__lowerCamelCase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowerCamelCase = in_proj_bias[hidden_size : hidden_size * 2]
__lowerCamelCase = in_proj_weight[-hidden_size :, :]
__lowerCamelCase = in_proj_bias[-hidden_size :]
# fmt: on
def a__ ( ):
__lowerCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__lowerCamelCase = Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase ).raw )
return im
@torch.no_grad()
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : bool = False ):
__lowerCamelCase = get_maskformer_config(_UpperCamelCase )
# load original state_dict
with open(_UpperCamelCase ,'''rb''' ) as f:
__lowerCamelCase = pickle.load(_UpperCamelCase )
__lowerCamelCase = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__lowerCamelCase = create_rename_keys(_UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
read_in_swin_q_k_v(_UpperCamelCase ,config.backbone_config )
read_in_decoder_q_k_v(_UpperCamelCase ,_UpperCamelCase )
# update to torch tensors
for key, value in state_dict.items():
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
# load 🤗 model
__lowerCamelCase = MaskFormerForInstanceSegmentation(_UpperCamelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCamelCase ,param.shape )
__lowerCamelCase ,__lowerCamelCase = model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCamelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
__lowerCamelCase = prepare_img()
if "vistas" in model_name:
__lowerCamelCase = 65
elif "cityscapes" in model_name:
__lowerCamelCase = 6_55_35
else:
__lowerCamelCase = 2_55
__lowerCamelCase = True if '''ade''' in model_name else False
__lowerCamelCase = MaskFormerImageProcessor(ignore_index=_UpperCamelCase ,reduce_labels=_UpperCamelCase )
__lowerCamelCase = image_processor(_UpperCamelCase ,return_tensors='''pt''' )
__lowerCamelCase = model(**_UpperCamelCase )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__lowerCamelCase = torch.tensor(
[[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,_UpperCamelCase ,atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
image_processor.save_pretrained(_UpperCamelCase )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
a_ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = XLMProphetNetTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''[PAD]'''
__lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__UpperCAmelCase ) , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''Hello World!'''
__lowerCamelCase = [35389, 6672, 49, 2]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 622 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """roformer"""
def __init__( self , __UpperCAmelCase=50000 , __UpperCAmelCase=None , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1536 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=0 , __UpperCAmelCase=False , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size if embedding_size is None else embedding_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = rotary_value
__lowerCamelCase = use_cache
class __lowerCAmelCase ( lowerCAmelCase__ ):
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__lowerCamelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__lowerCamelCase = {0: '''batch''', 1: '''sequence'''}
__lowerCamelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
a_ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = None
# source code of `config_class`
__lowerCamelCase = inspect.getsource(_UpperCamelCase )
__lowerCamelCase = _re_checkpoint.findall(_UpperCamelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('''/''' ):
__lowerCamelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
__lowerCamelCase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__lowerCamelCase = ckpt_name
break
return checkpoint
def a__ ( ):
__lowerCamelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
__lowerCamelCase = get_checkpoint_from_config_class(_UpperCamelCase )
__lowerCamelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
__lowerCamelCase = '''\n'''.join(sorted(_UpperCamelCase ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 622 | 1 |
import warnings
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_ = {
"""nvidia/segformer-b0-finetuned-ade-512-512""": (
"""https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """segformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=[2, 2, 2, 2] , __UpperCAmelCase=[8, 4, 2, 1] , __UpperCAmelCase=[32, 64, 160, 256] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[1, 2, 5, 8] , __UpperCAmelCase=[4, 4, 4, 4] , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.02 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1E-6 , __UpperCAmelCase=256 , __UpperCAmelCase=255 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
''' removed, as the behaviour will default to that of reshape_last_stage = True.''' , __UpperCAmelCase , )
__lowerCamelCase = num_channels
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = depths
__lowerCamelCase = sr_ratios
__lowerCamelCase = hidden_sizes
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = mlp_ratios
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = classifier_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = drop_path_rate
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = decoder_hidden_size
__lowerCamelCase = kwargs.get('''reshape_last_stage''' , __UpperCAmelCase )
__lowerCamelCase = semantic_loss_ignore_index
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 1E-4
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 12
| 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=64 , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase=[1, 16, 4, 4] , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
__lowerCamelCase = (self.image_size // 32) ** 2
__lowerCamelCase = num_patches + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
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=__UpperCAmelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__UpperCAmelCase , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = ViTHybridModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.type_sequence_label_size
__lowerCamelCase = ViTHybridForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ViTHybridModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( 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 )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( 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 )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = _config_zero_init(__UpperCAmelCase )
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(config=__UpperCAmelCase )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
__lowerCamelCase = [F"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = ViTHybridModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=__UpperCAmelCase , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-1.9_090, -0.4_993, -0.2_389] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
@require_accelerate
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
__lowerCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''' )
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=__UpperCAmelCase , return_tensors='''pt''' )
__lowerCamelCase = model(**__UpperCAmelCase )
__lowerCamelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
__lowerCamelCase = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''' )
| 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
a_ = logging.getLogger(__name__)
@dataclass(frozen=lowerCAmelCase__ )
class __lowerCAmelCase :
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
@dataclass(frozen=lowerCAmelCase__ )
class __lowerCAmelCase :
lowerCAmelCase__ = 42
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase=False , __UpperCAmelCase = False , ):
'''simple docstring'''
__lowerCamelCase = hans_processors[task]()
__lowerCamelCase = os.path.join(
__UpperCAmelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(__UpperCAmelCase ) , __UpperCAmelCase , ) , )
__lowerCamelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowerCamelCase ,__lowerCamelCase = label_list[2], label_list[1]
__lowerCamelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCamelCase = cached_features_file + '''.lock'''
with FileLock(__UpperCAmelCase ):
if os.path.exists(__UpperCAmelCase ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
__lowerCamelCase = torch.load(__UpperCAmelCase )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
__lowerCamelCase = (
processor.get_dev_examples(__UpperCAmelCase ) if evaluate else processor.get_train_examples(__UpperCAmelCase )
)
logger.info('''Training examples: %s''' , len(__UpperCAmelCase ) )
__lowerCamelCase = hans_convert_examples_to_features(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
logger.info('''Saving features into cached file %s''' , __UpperCAmelCase )
torch.save(self.features , __UpperCAmelCase )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
return self.features[i]
def lowerCamelCase ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class __lowerCAmelCase :
lowerCAmelCase__ = 42
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 128 , __UpperCAmelCase=False , __UpperCAmelCase = False , ):
'''simple docstring'''
__lowerCamelCase = hans_processors[task]()
__lowerCamelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowerCamelCase ,__lowerCamelCase = label_list[2], label_list[1]
__lowerCamelCase = label_list
__lowerCamelCase = processor.get_dev_examples(__UpperCAmelCase ) if evaluate else processor.get_train_examples(__UpperCAmelCase )
__lowerCamelCase = hans_convert_examples_to_features(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(__UpperCAmelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
__lowerCamelCase = tf.data.Dataset.from_generator(
__UpperCAmelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def lowerCamelCase ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
return self.features[i]
def lowerCamelCase ( self ):
'''simple docstring'''
return self.label_list
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(__UpperCAmelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(__UpperCAmelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
for i, line in enumerate(__UpperCAmelCase ):
if i == 0:
continue
__lowerCamelCase = '''%s-%s''' % (set_type, line[0])
__lowerCamelCase = line[5]
__lowerCamelCase = line[6]
__lowerCamelCase = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
__lowerCamelCase = line[0]
examples.append(InputExample(guid=__UpperCAmelCase , text_a=__UpperCAmelCase , text_b=__UpperCAmelCase , label=__UpperCAmelCase , pairID=__UpperCAmelCase ) )
return examples
def a__ ( _UpperCamelCase : List[InputExample] ,_UpperCamelCase : List[str] ,_UpperCamelCase : int ,_UpperCamelCase : PreTrainedTokenizer ,):
__lowerCamelCase = {label: i for i, label in enumerate(_UpperCamelCase )}
__lowerCamelCase = []
for ex_index, example in tqdm.tqdm(enumerate(_UpperCamelCase ) ,desc='''convert examples to features''' ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d''' % (ex_index) )
__lowerCamelCase = tokenizer(
example.text_a ,example.text_b ,add_special_tokens=_UpperCamelCase ,max_length=_UpperCamelCase ,padding='''max_length''' ,truncation=_UpperCamelCase ,return_overflowing_tokens=_UpperCamelCase ,)
__lowerCamelCase = label_map[example.label] if example.label in label_map else 0
__lowerCamelCase = int(example.pairID )
features.append(InputFeatures(**_UpperCamelCase ,label=_UpperCamelCase ,pairID=_UpperCamelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
a_ = {
"""hans""": 3,
}
a_ = {
"""hans""": HansProcessor,
}
| 622 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
a_ = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase = 101 ):
'''simple docstring'''
__lowerCamelCase = length
def __len__( self ):
'''simple docstring'''
return self.length
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
return i
class __lowerCAmelCase :
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
return {"input_ids": torch.tensor(__UpperCAmelCase ), "labels": torch.tensor(__UpperCAmelCase )}
class __lowerCAmelCase ( nn.Module ):
def __init__( self ):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
__lowerCamelCase = nn.Linear(120 , 80 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class __lowerCAmelCase ( lowerCAmelCase__ ):
@require_torch_neuroncore
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = F"""--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = F"""--output_dir {output_dir}""".split()
__lowerCamelCase = ['''torchrun'''] + distributed_args + args
execute_subprocess_async(__UpperCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class __lowerCAmelCase ( lowerCAmelCase__ ):
@require_torch_multi_gpu
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = F"""--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = F"""--output_dir {output_dir}""".split()
__lowerCamelCase = ['''torchrun'''] + distributed_args + args
execute_subprocess_async(__UpperCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
a_ = HfArgumentParser((TrainingArguments,))
a_ = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
a_ = DummyDataset(dataset_length)
def a__ ( _UpperCamelCase : EvalPrediction ):
__lowerCamelCase = list(range(len(_UpperCamelCase ) ) )
__lowerCamelCase = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
'''Predictions and/or labels do not match expected results:\n - predictions: '''
F"""{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}""" )
return {"success": success}
a_ = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
a_ = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a_ = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a_ = 2
a_ = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a_ = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a_ = None
| 622 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 | 1 |
import os
def a__ ( _UpperCamelCase : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(_UpperCamelCase ) ,_UpperCamelCase ) ) as in_file:
__lowerCamelCase = in_file.read()
__lowerCamelCase = [[int(_UpperCamelCase ) for cell in row.split(''',''' )] for row in data.strip().splitlines()]
__lowerCamelCase = [[0 for cell in row] for row in grid]
__lowerCamelCase = len(grid[0] )
__lowerCamelCase = [[0 for i in range(_UpperCamelCase )] for j in range(_UpperCamelCase )]
__lowerCamelCase = grid[0][0]
for i in range(1 ,_UpperCamelCase ):
__lowerCamelCase = grid[0][i] + dp[0][i - 1]
for i in range(1 ,_UpperCamelCase ):
__lowerCamelCase = grid[i][0] + dp[i - 1][0]
for i in range(1 ,_UpperCamelCase ):
for j in range(1 ,_UpperCamelCase ):
__lowerCamelCase = grid[i][j] + min(dp[i - 1][j] ,dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(f"{solution() = }")
| 622 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : List[str] ,_UpperCamelCase : List[Any]=None ,_UpperCamelCase : Any=None ):
if attention_mask is None:
__lowerCamelCase = tf.cast(tf.math.not_equal(_UpperCamelCase ,config.pad_token_id ) ,tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __lowerCAmelCase :
lowerCAmelCase__ = OPTConfig
lowerCAmelCase__ = {}
lowerCAmelCase__ = """gelu"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=20 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = embed_dim
__lowerCamelCase = word_embed_proj_dim
__lowerCamelCase = False
def lowerCamelCase ( 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 = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__UpperCAmelCase , **self.config_updates , )
__lowerCamelCase = prepare_opt_inputs_dict(__UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel(config=__UpperCAmelCase )
__lowerCamelCase = inputs_dict['''input_ids''']
__lowerCamelCase = input_ids[:1, :]
__lowerCamelCase = inputs_dict['''attention_mask'''][:1, :]
__lowerCamelCase = 1
# first forward pass
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
__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(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__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(__UpperCAmelCase , __UpperCAmelCase , rtol=1E-3 )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__UpperCAmelCase , '''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__lowerCamelCase = model_class(config=__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__UpperCAmelCase )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_input_embeddings() )
__lowerCamelCase = _get_word_embedding_weight(__UpperCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__lowerCamelCase = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __UpperCAmelCase )
# check that weights remain the same after resizing
__lowerCamelCase = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __UpperCAmelCase )
__lowerCamelCase = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__lowerCamelCase = False
self.assertTrue(__UpperCAmelCase )
def a__ ( _UpperCamelCase : Optional[Any] ):
return tf.constant(_UpperCamelCase ,dtype=tf.intaa )
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = 9_9
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__lowerCamelCase = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__lowerCamelCase = input_ids.shape[0]
__lowerCamelCase = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTModel.from_pretrained('''facebook/opt-350m''' )
__lowerCamelCase = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase = tf.not_equal(__UpperCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
__lowerCamelCase = model(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase ).last_hidden_state
__lowerCamelCase = (1, 11, 512)
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = tf.constant(
[[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-3 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = xla_generate(__UpperCAmelCase , __UpperCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__lowerCamelCase = '''facebook/opt-350m'''
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(self.path_model )
__lowerCamelCase = GPTaTokenizer.from_pretrained(self.path_model )
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__lowerCamelCase = tf.constant(
[
[1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
[-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
[0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
[6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
] )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
__lowerCamelCase = tf.function(__UpperCAmelCase , jit_compile=__UpperCAmelCase )
__lowerCamelCase = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-125m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = '''left'''
# use different length sentences to test batching
__lowerCamelCase = [
'''Hello, my dog is a little''',
'''Today, I''',
]
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' , padding=__UpperCAmelCase )
__lowerCamelCase = inputs['''input_ids''']
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , attention_mask=inputs['''attention_mask'''] )
__lowerCamelCase = tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase )
__lowerCamelCase = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa ) )
__lowerCamelCase = tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(input_ids=__UpperCAmelCase , max_length=model.config.max_length - num_paddings )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , [non_padded_sentence, padded_sentence] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
__lowerCamelCase = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__lowerCamelCase = []
__lowerCamelCase = GPTaTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = TFOPTForCausalLM.from_pretrained(__UpperCAmelCase )
for prompt in self.prompts:
__lowerCamelCase = tokenizer(__UpperCAmelCase , return_tensors='''tf''' ).input_ids
__lowerCamelCase = model.generate(__UpperCAmelCase , max_length=10 )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
| 622 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
a_ = {
"""vocab_file""": {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
),
}
}
a_ = {
"""junnyu/roformer_chinese_small""": 1_536,
"""junnyu/roformer_chinese_base""": 1_536,
"""junnyu/roformer_chinese_char_small""": 512,
"""junnyu/roformer_chinese_char_base""": 512,
"""junnyu/roformer_small_discriminator""": 128,
"""junnyu/roformer_small_generator""": 128,
}
a_ = {
"""junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
"""junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
"""junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
"""junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
"""junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
"""junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase__ = RoFormerTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase="[UNK]" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="[PAD]" , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[MASK]" , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
pre_tok_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or pre_tok_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
):
__lowerCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = do_lower_case
__lowerCamelCase = strip_accents
__lowerCamelCase = pre_tok_class(**__UpperCAmelCase )
__lowerCamelCase = do_lower_case
def __getstate__( self ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = BertPreTokenizer()
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
__lowerCamelCase = self.__dict__['''_tokenizer'''].get_vocab()
__lowerCamelCase = PreTokenizer.custom(JiebaPreTokenizer(__UpperCAmelCase ) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=False , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = BertPreTokenizer()
return super().save_pretrained(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
| 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ = logging.getLogger(__name__)
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[Any] ):
__lowerCamelCase = np.argmax(_UpperCamelCase ,axis=1 )
return np.sum(outputs == labels )
def a__ ( _UpperCamelCase : Optional[int] ):
with open(_UpperCamelCase ,encoding='''utf_8''' ) as f:
__lowerCamelCase = csv.reader(_UpperCamelCase )
__lowerCamelCase = []
next(_UpperCamelCase ) # skip the first line
for line in tqdm(_UpperCamelCase ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Dict ,_UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Dict ):
__lowerCamelCase = []
for dataset in encoded_datasets:
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
__lowerCamelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCamelCase ):
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = mc_label
__lowerCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,default='''openai-gpt''' ,help='''pretrained model name''' )
parser.add_argument('''--do_train''' ,action='''store_true''' ,help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' ,action='''store_true''' ,help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42 )
parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3 )
parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8 )
parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16 )
parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1 )
parser.add_argument(
'''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) ,)
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,)
parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5 )
parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01 )
parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9 )
parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74 )
parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
__lowerCamelCase = parser.parse_args()
print(_UpperCamelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
__lowerCamelCase = torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase ,_UpperCamelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase = ['''_start_''', '''_delimiter_''', '''_classify_''']
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCamelCase )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCamelCase ) )
model.to(_UpperCamelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCamelCase : Dict ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase ) )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
return obj
return [tokenize_and_encode(_UpperCamelCase ) for o in obj]
logger.info('''Encoding dataset...''' )
__lowerCamelCase = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase = (train_dataset, eval_dataset)
__lowerCamelCase = tokenize_and_encode(_UpperCamelCase )
# Compute the max input length for the Transformer
__lowerCamelCase = model.config.n_positions // 2 - 2
__lowerCamelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase = min(_UpperCamelCase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase = pre_process_datasets(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,*_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = RandomSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size )
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = SequentialSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase = args.max_steps
__lowerCamelCase = args.max_steps // (len(_UpperCamelCase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase = len(_UpperCamelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase = list(model.named_parameters() )
__lowerCamelCase = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
__lowerCamelCase = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
__lowerCamelCase = AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon )
__lowerCamelCase = get_linear_schedule_with_warmup(
_UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase )
if args.do_train:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc='''Epoch''' ):
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(_UpperCamelCase ,desc='''Training''' )
for step, batch in enumerate(_UpperCamelCase ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
__lowerCamelCase = model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase = '''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase = model.module if hasattr(_UpperCamelCase ,'''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
torch.save(model_to_save.state_dict() ,_UpperCamelCase )
model_to_save.config.to_json_file(_UpperCamelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCamelCase )
if args.do_eval:
model.eval()
__lowerCamelCase ,__lowerCamelCase = 0, 0
__lowerCamelCase ,__lowerCamelCase = 0, 0
for batch in tqdm(_UpperCamelCase ,desc='''Evaluating''' ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
with torch.no_grad():
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = model(
_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = mc_logits.detach().cpu().numpy()
__lowerCamelCase = mc_labels.to('''cpu''' ).numpy()
__lowerCamelCase = accuracy(_UpperCamelCase ,_UpperCamelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase = eval_loss / nb_eval_steps
__lowerCamelCase = eval_accuracy / nb_eval_examples
__lowerCamelCase = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
__lowerCamelCase = os.path.join(args.output_dir ,'''eval_results.txt''' )
with open(_UpperCamelCase ,'''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' ,_UpperCamelCase ,str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 622 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAmelCase=1024 , __UpperCAmelCase=3.6 ):
'''simple docstring'''
__lowerCamelCase = tokenizer
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = dataset
__lowerCamelCase = seq_length
__lowerCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = iter(self.dataset )
__lowerCamelCase = True
while more_examples:
__lowerCamelCase ,__lowerCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__UpperCAmelCase )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
__lowerCamelCase = False
break
__lowerCamelCase = tokenizer(__UpperCAmelCase , truncation=__UpperCAmelCase )['''input_ids''']
__lowerCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__UpperCAmelCase ) , self.seq_length ):
__lowerCamelCase = all_token_ids[i : i + self.seq_length]
if len(__UpperCAmelCase ) == self.seq_length:
yield torch.tensor(__UpperCAmelCase )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = {'''streaming''': True}
__lowerCamelCase = load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase )
__lowerCamelCase = ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=args.batch_size )
return eval_dataloader
def a__ ( _UpperCamelCase : str ):
model.eval()
__lowerCamelCase = []
for step, batch in enumerate(_UpperCamelCase ):
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase ,labels=_UpperCamelCase )
__lowerCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__lowerCamelCase = torch.mean(torch.cat(_UpperCamelCase ) )
try:
__lowerCamelCase = torch.exp(_UpperCamelCase )
except OverflowError:
__lowerCamelCase = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
a_ = Accelerator()
# Parse configuration
a_ = HfArgumentParser(EvaluationArguments)
a_ = parser.parse_args()
set_seed(args.seed)
# Logging
a_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
a_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
a_ , a_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_ , a_ = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 622 | 1 |
def a__ ( ):
return [
a * b * (10_00 - a - b)
for a in range(1 ,9_99 )
for b in range(_UpperCamelCase ,9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"{solution() = }")
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """lxmert"""
lowerCAmelCase__ = {}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=9500 , __UpperCAmelCase=1600 , __UpperCAmelCase=400 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=9 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=2048 , __UpperCAmelCase=4 , __UpperCAmelCase=6.67 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = num_qa_labels
__lowerCamelCase = num_object_labels
__lowerCamelCase = num_attr_labels
__lowerCamelCase = l_layers
__lowerCamelCase = x_layers
__lowerCamelCase = r_layers
__lowerCamelCase = visual_feat_dim
__lowerCamelCase = visual_pos_dim
__lowerCamelCase = visual_loss_normalizer
__lowerCamelCase = task_matched
__lowerCamelCase = task_mask_lm
__lowerCamelCase = task_obj_predict
__lowerCamelCase = task_qa
__lowerCamelCase = visual_obj_loss
__lowerCamelCase = visual_attr_loss
__lowerCamelCase = visual_feat_loss
__lowerCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__UpperCAmelCase )
| 622 | 1 |
from functools import lru_cache
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_UpperCamelCase )
if n > 1:
factors.add(_UpperCamelCase )
return factors
@lru_cache
def a__ ( _UpperCamelCase : int ):
return len(unique_prime_factors(_UpperCamelCase ) )
def a__ ( _UpperCamelCase : list ):
return len(set(_UpperCamelCase ) ) in (0, 1)
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = 2
while True:
# Increment each value of a generated range
__lowerCamelCase = [base + i for i in range(_UpperCamelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
__lowerCamelCase = [upf_len(_UpperCamelCase ) for x in group]
checker.append(_UpperCamelCase )
# If all numbers in the list are equal, return the group variable.
if equality(_UpperCamelCase ):
return group
# Increment our base variable by 1
base += 1
def a__ ( _UpperCamelCase : int = 4 ):
__lowerCamelCase = run(_UpperCamelCase )
return results[0] if len(_UpperCamelCase ) else None
if __name__ == "__main__":
print(solution())
| 622 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length, 2) ,_UpperCamelCase )
else:
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length) ,_UpperCamelCase )
for i, tensor in enumerate(_UpperCamelCase ):
if padding_side == "right":
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
else:
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = ord(_UpperCamelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__lowerCamelCase = unicodedata.category(_UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = True
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = -1_0_0
lowerCAmelCase__ = "pt"
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
import torch
__lowerCamelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCamelCase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCamelCase = self.tokenizer.pad(
__UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCamelCase = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCamelCase = self.tokenizer.padding_side
if padding_side == "right":
__lowerCamelCase = [
list(__UpperCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) for label in labels
]
else:
__lowerCamelCase = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) + list(__UpperCAmelCase ) for label in labels
]
__lowerCamelCase = [feature['''ner_tags'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , -1 , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = [feature['''original_entity_spans'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , (-1, -1) , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {k: torch.tensor(__UpperCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 622 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def a__ ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
__lowerCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching ,'''os.path.join''' ,_UpperCamelCase ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os ,_PatchedModuleObj )
assert isinstance(_test_patching.os.path ,_PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path ,_PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os ,_PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path ,_PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path ,_PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def a__ ( ):
assert _test_patching.open is open
__lowerCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching ,'''open''' ,_UpperCamelCase ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def a__ ( ):
# pandas.read_csv is not present in _test_patching
__lowerCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching ,'''pandas.read_csv''' ,_UpperCamelCase ):
pass
def a__ ( ):
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
__lowerCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching ,'''len''' ,_UpperCamelCase ) is None
with patch_submodule(_test_patching ,'''len''' ,_UpperCamelCase ):
assert _test_patching.len is mock
assert _test_patching.len is len
def a__ ( ):
__lowerCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
__lowerCamelCase = patch_submodule(_test_patching ,'''open''' ,_UpperCamelCase )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def a__ ( ):
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
__lowerCamelCase = '''__test_patch_submodule_successive_join__'''
__lowerCamelCase = '''__test_patch_submodule_successive_dirname__'''
__lowerCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching ,'''os.path.join''' ,_UpperCamelCase ):
with patch_submodule(_test_patching ,'''os.rename''' ,_UpperCamelCase ):
with patch_submodule(_test_patching ,'''os.path.dirname''' ,_UpperCamelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching ,'''os.rename''' ,_UpperCamelCase ):
with patch_submodule(_test_patching ,'''os.path.join''' ,_UpperCamelCase ):
with patch_submodule(_test_patching ,'''os.path.dirname''' ,_UpperCamelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def a__ ( ):
__lowerCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching ,'''__module_that_doesn_exist__.__attribute_that_doesn_exist__''' ,_UpperCamelCase ):
pass
with patch_submodule(_test_patching ,'''os.__attribute_that_doesn_exist__''' ,_UpperCamelCase ):
pass
| 622 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=[1, 1, 2] , __UpperCAmelCase=1 , __UpperCAmelCase=32 , __UpperCAmelCase=4 , __UpperCAmelCase=8 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=3 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=False , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = block_sizes
__lowerCamelCase = num_decoder_layers
__lowerCamelCase = d_model
__lowerCamelCase = n_head
__lowerCamelCase = d_head
__lowerCamelCase = d_inner
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = 2
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = initializer_std
# Used in the tests to check the size of the first attention layer
__lowerCamelCase = n_head
# Used in the tests to check the size of the first hidden state
__lowerCamelCase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
__lowerCamelCase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
__lowerCamelCase = self.num_hidden_layers + 2
def lowerCamelCase ( 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
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__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 = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__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, 2, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
__lowerCamelCase = False
__lowerCamelCase = TFFunnelBaseModel(config=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForPreTraining(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForMaskedLM(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForSequenceClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = TFFunnelForMultipleChoice(config=__UpperCAmelCase )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__lowerCamelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFFunnelForTokenClassification(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = TFFunnelForQuestionAnswering(config=__UpperCAmelCase )
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
@require_tf
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TFFunnelModelTester(self , base=__UpperCAmelCase )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
| 622 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
a_ = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
a_ = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
a_ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
] , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__UpperCAmelCase , __UpperCAmelCase , sample_weight=__UpperCAmelCase ) ),
}
| 622 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(_UpperCamelCase ).content ).xpath(_UpperCamelCase ) )
a_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 622 | 1 |
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()
a_ = logging.get_logger("""transformers.models.encodec""")
a_ = {
"""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""",
}
a_ = {
"""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""",
}
a_ = {
"""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""",
}
a_ = {
"""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""",
}
a_ = {
"""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""",
}
a_ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
a_ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
a_ = []
a_ = []
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : List[str] ,_UpperCamelCase : Dict ,_UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ):
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
elif weight_type == "running_mean":
__lowerCamelCase = value
elif weight_type == "running_var":
__lowerCamelCase = value
elif weight_type == "num_batches_tracked":
__lowerCamelCase = value
elif weight_type == "weight_ih_l0":
__lowerCamelCase = value
elif weight_type == "weight_hh_l0":
__lowerCamelCase = value
elif weight_type == "bias_ih_l0":
__lowerCamelCase = value
elif weight_type == "bias_hh_l0":
__lowerCamelCase = value
elif weight_type == "weight_ih_l1":
__lowerCamelCase = value
elif weight_type == "weight_hh_l1":
__lowerCamelCase = value
elif weight_type == "bias_ih_l1":
__lowerCamelCase = value
elif weight_type == "bias_hh_l1":
__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 : Dict ,_UpperCamelCase : Any ):
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__lowerCamelCase ,__lowerCamelCase = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Optional[Any] ):
__lowerCamelCase = []
if model_name == "encodec_24khz" or "encodec_32khz":
__lowerCamelCase = MAPPING_24K
elif model_name == "encodec_48khz":
__lowerCamelCase = MAPPING_48K
else:
raise ValueError(F"""Unsupported model: {model_name}""" )
for name, value in orig_dict.items():
if should_ignore(_UpperCamelCase ,_UpperCamelCase ):
logger.info(F"""{name} was ignored""" )
continue
__lowerCamelCase = False
for key, mapped_key in MAPPING.items():
if "*" in key:
__lowerCamelCase ,__lowerCamelCase = key.split('''.*.''' )
if prefix in name and suffix in name:
__lowerCamelCase = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ):
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 "weight_ih_l0" in name:
__lowerCamelCase = '''weight_ih_l0'''
elif "weight_hh_l0" in name:
__lowerCamelCase = '''weight_hh_l0'''
elif "bias_ih_l0" in name:
__lowerCamelCase = '''bias_ih_l0'''
elif "bias_hh_l0" in name:
__lowerCamelCase = '''bias_hh_l0'''
elif "weight_ih_l1" in name:
__lowerCamelCase = '''weight_ih_l1'''
elif "weight_hh_l1" in name:
__lowerCamelCase = '''weight_hh_l1'''
elif "bias_ih_l1" in name:
__lowerCamelCase = '''bias_ih_l1'''
elif "bias_hh_l1" in name:
__lowerCamelCase = '''bias_hh_l1'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
__lowerCamelCase = '''weight'''
elif "running_mean" in name:
__lowerCamelCase = '''running_mean'''
elif "running_var" in name:
__lowerCamelCase = '''running_var'''
elif "num_batches_tracked" in name:
__lowerCamelCase = '''num_batches_tracked'''
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}""" )
@torch.no_grad()
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[Any] ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[int]=None ,_UpperCamelCase : Tuple=None ,):
if config_path is not None:
__lowerCamelCase = EncodecConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
__lowerCamelCase = [8, 5, 4, 4]
__lowerCamelCase = [2.2]
__lowerCamelCase = 64
__lowerCamelCase = 3_20_00
__lowerCamelCase = 20_48
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
elif model_name == "encodec_48khz":
__lowerCamelCase = [8, 5, 4, 2]
__lowerCamelCase = [3.0, 6.0, 12.0, 24.0]
__lowerCamelCase = 4_80_00
__lowerCamelCase = 2
__lowerCamelCase = False
__lowerCamelCase = '''time_group_norm'''
__lowerCamelCase = True
__lowerCamelCase = 1.0
__lowerCamelCase = 0.01
else:
raise ValueError(F"""Unknown model name: {model_name}""" )
__lowerCamelCase = EncodecModel(_UpperCamelCase )
__lowerCamelCase = 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(_UpperCamelCase )
__lowerCamelCase = torch.load(_UpperCamelCase )
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
__lowerCamelCase = original_checkpoint['''best_state''']
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
if repo_id:
print('''Pushing to the hub...''' )
feature_extractor.push_to_hub(_UpperCamelCase )
model.push_to_hub(_UpperCamelCase )
if __name__ == "__main__":
a_ = 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."""
)
a_ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__lowerCamelCase = index + 1
elif index + 1 == len(_UpperCamelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 622 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = 8
# DPR tok
__lowerCamelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowerCamelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , DPR_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] ) )
# BART tok
__lowerCamelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__lowerCamelCase = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__lowerCamelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__lowerCamelCase = {'''unk_token''': '''<unk>'''}
__lowerCamelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCamelCase = os.path.join(__UpperCAmelCase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCAmelCase ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_dataset()
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
__lowerCamelCase = os.path.join(self.tmpdirname , '''dataset''' )
__lowerCamelCase = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase ) , )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
__lowerCamelCase = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
__lowerCamelCase = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
__lowerCamelCase = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(__UpperCAmelCase , open(__UpperCAmelCase , '''wb''' ) )
__lowerCamelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
__lowerCamelCase = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
__lowerCamelCase = self.get_dummy_dataset()
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=__UpperCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__UpperCAmelCase ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , __UpperCAmelCase )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = RagRetriever.from_pretrained(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever.retrieve(__UpperCAmelCase , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
import torch
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_canonical_hf_index_retriever()
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
__lowerCamelCase = retriever(
__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase , return_tensors='''pt''' , )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dpr_ctx_encoder_tokenizer()
__lowerCamelCase = 1
__lowerCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase )
retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase )
__lowerCamelCase = [[5, 7], [10, 11]]
__lowerCamelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__lowerCamelCase = retriever(__UpperCAmelCase , __UpperCAmelCase , prefix=retriever.config.generator.prefix , n_docs=__UpperCAmelCase )
self.assertEqual(
len(__UpperCAmelCase ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , __UpperCAmelCase ) # check for doc token related keys in dictionary.
| 622 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
"""tokenization_deberta""": ["""DebertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""DebertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DebertaForMaskedLM""",
"""DebertaForQuestionAnswering""",
"""DebertaForSequenceClassification""",
"""DebertaForTokenClassification""",
"""DebertaModel""",
"""DebertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDebertaForMaskedLM""",
"""TFDebertaForQuestionAnswering""",
"""TFDebertaForSequenceClassification""",
"""TFDebertaForTokenClassification""",
"""TFDebertaModel""",
"""TFDebertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 |
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_ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """poolformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=4.0 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[64, 128, 320, 512] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[2, 1, 1, 1] , __UpperCAmelCase=4 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.02 , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = stride
__lowerCamelCase = padding
__lowerCamelCase = pool_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = mlp_ratio
__lowerCamelCase = depths
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_layer_scale
__lowerCamelCase = layer_scale_init_value
__lowerCamelCase = initializer_range
super().__init__(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return 2E-3
| 622 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
a_ = getLogger(__name__)
def a__ ( _UpperCamelCase : Optional[Any] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : int = 10_24 ,_UpperCamelCase : str="val" ,_UpperCamelCase : Any=None ,_UpperCamelCase : Union[str, Any]=False ,_UpperCamelCase : Tuple="summarization" ,_UpperCamelCase : Any=None ,_UpperCamelCase : int=1 ,_UpperCamelCase : Dict = None ,_UpperCamelCase : Any="" ,**_UpperCamelCase : Optional[int] ,):
__lowerCamelCase = str(_UpperCamelCase )
assert local_rank is not None
torch.distributed.init_process_group(backend='''nccl''' ,rank=_UpperCamelCase )
__lowerCamelCase = Path(_UpperCamelCase )
__lowerCamelCase = save_dir.joinpath(F"""rank_{local_rank}_output.json""" )
torch.cuda.set_device(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).cuda()
if fpaa:
__lowerCamelCase = model.half()
# determine if we need to increase num_beams
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase ) # update config with task specific params
__lowerCamelCase = generate_kwargs.pop('''num_beams''' ,model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
__lowerCamelCase = num_return_sequences
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
if max_source_length is None:
__lowerCamelCase = tokenizer.model_max_length
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
__lowerCamelCase = SeqaSeqDataset(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,max_target_length=10_24 ,type_path=_UpperCamelCase ,n_obs=_UpperCamelCase ,prefix=_UpperCamelCase ,**_UpperCamelCase ,)
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
__lowerCamelCase = ds.make_sortish_sampler(_UpperCamelCase ,distributed=_UpperCamelCase ,add_extra_examples=_UpperCamelCase ,shuffle=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=_UpperCamelCase ,collate_fn=ds.collate_fn )
__lowerCamelCase = []
for batch in tqdm(_UpperCamelCase ):
__lowerCamelCase = model.generate(
input_ids=batch['''input_ids'''].to(model.device ) ,attention_mask=batch['''attention_mask'''].to(model.device ) ,num_return_sequences=_UpperCamelCase ,num_beams=_UpperCamelCase ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
__lowerCamelCase = batch['''ids''']
if num_return_sequences > 1:
__lowerCamelCase = chunks(_UpperCamelCase ,_UpperCamelCase ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(_UpperCamelCase ):
results.append({'''pred''': pred, '''id''': ids[i].item()} )
save_json(_UpperCamelCase ,_UpperCamelCase )
return results, sampler.num_replicas
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(
epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate''' )
parser.add_argument('''--data_dir''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument(
'''--model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' ,default='''sshleifer/distilbart-xsum-12-3''' ,)
parser.add_argument('''--save_dir''' ,type=_UpperCamelCase ,help='''where to save''' ,default='''tmp_gen''' )
parser.add_argument('''--max_source_length''' ,type=_UpperCamelCase ,default=_UpperCamelCase )
parser.add_argument(
'''--type_path''' ,type=_UpperCamelCase ,default='''test''' ,help='''which subset to evaluate typically train/val/test''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--local_rank''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''should be passed by distributed.launch''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument(
'''--num_return_sequences''' ,type=_UpperCamelCase ,default=1 ,required=_UpperCamelCase ,help='''How many sequences to return''' )
parser.add_argument(
'''--sync_timeout''' ,type=_UpperCamelCase ,default=6_00 ,required=_UpperCamelCase ,help='''How long should master process wait for other processes to finish.''' ,)
parser.add_argument('''--src_lang''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,required=_UpperCamelCase )
parser.add_argument('''--tgt_lang''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,required=_UpperCamelCase )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--debug''' ,action='''store_true''' )
__lowerCamelCase = time.time()
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if generate_kwargs and args.local_rank <= 0:
print(F"""parsed the following generate kwargs: {generate_kwargs}""" )
__lowerCamelCase = Path(args.save_dir + '''_tmp''' )
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase ) # this handles locking.
__lowerCamelCase = list(json_save_dir.glob('''rank_*.json''' ) )
if intermediate_files:
raise ValueError(F"""Found files at {json_save_dir} please move or remove them.""" )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
__lowerCamelCase = {}
if args.src_lang is not None:
__lowerCamelCase = args.src_lang
if args.tgt_lang is not None:
__lowerCamelCase = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = eval_data_dir(
args.data_dir ,_UpperCamelCase ,args.model_name ,type_path=args.type_path ,bs=args.bs ,fpaa=args.fpaa ,task=args.task ,local_rank=args.local_rank ,n_obs=args.n_obs ,max_source_length=args.max_source_length ,num_return_sequences=args.num_return_sequences ,prefix=args.prefix ,dataset_kwargs=_UpperCamelCase ,**_UpperCamelCase ,)
if args.local_rank <= 0:
__lowerCamelCase = Path(args.save_dir )
save_dir.mkdir(exist_ok=_UpperCamelCase )
__lowerCamelCase = gather_results_from_each_node(_UpperCamelCase ,_UpperCamelCase ,args.sync_timeout )
__lowerCamelCase = combine_partial_results(_UpperCamelCase )
if args.num_return_sequences > 1:
__lowerCamelCase = save_dir.joinpath('''pseudolabel_results.json''' )
print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/""" )
save_json(_UpperCamelCase ,_UpperCamelCase )
return
__lowerCamelCase = Path(args.data_dir ).joinpath(args.type_path + '''.target''' )
with open(_UpperCamelCase ) as f:
__lowerCamelCase = [x.rstrip() for x in f.readlines()][: len(_UpperCamelCase )]
# Calculate metrics, save metrics, and save _generations.txt
__lowerCamelCase = '''translation''' in args.task
__lowerCamelCase = calculate_bleu if calc_bleu else calculate_rouge
__lowerCamelCase = '''bleu''' if calc_bleu else '''rouge'''
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = time.time() - start_time
__lowerCamelCase = round(runtime / metrics['''n_obs'''] ,4 )
__lowerCamelCase = num_replicas
# TODO(@stas00): add whatever metadata to metrics
__lowerCamelCase = save_dir.joinpath(F"""{args.type_path}_{metric_name}.json""" )
save_json(_UpperCamelCase ,_UpperCamelCase ,indent=_UpperCamelCase )
print(_UpperCamelCase )
write_txt_file(_UpperCamelCase ,save_dir.joinpath(F"""{args.type_path}_generations.txt""" ) )
if args.debug:
write_txt_file(_UpperCamelCase ,save_dir.joinpath(F"""{args.type_path}.target""" ) )
else:
shutil.rmtree(_UpperCamelCase )
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = []
for partial_result in partial_results:
records.extend(_UpperCamelCase )
__lowerCamelCase = sorted(_UpperCamelCase ,key=lambda _UpperCamelCase : x["id"] )
__lowerCamelCase = [x['''pred'''] for x in records]
return preds
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
# WAIT FOR lots of .json files
__lowerCamelCase = time.time()
logger.info('''waiting for all nodes to finish''' )
__lowerCamelCase = None
while (time.time() - start_wait) < timeout:
__lowerCamelCase = list(save_dir.glob('''rank_*.json''' ) )
if len(_UpperCamelCase ) < num_replicas:
continue
try:
# make sure all json files are fully saved
__lowerCamelCase = lmap(_UpperCamelCase ,_UpperCamelCase )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError('''Rank 0 gave up on waiting for other processes''' )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
"""uclanlp/visualbert-vcr-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
),
"""uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
"""uclanlp/visualbert-nlvr2-coco-pre""": (
"""https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """visual_bert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=512 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = visual_embedding_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = bypass_transformer
__lowerCamelCase = special_visual_initialize
| 622 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( _lowerCamelCase ):
lowerCAmelCase__ = 4_2
lowerCAmelCase__ = 4_2
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self , __UpperCAmelCase = 1 , __UpperCAmelCase = 2000 , __UpperCAmelCase = None , __UpperCAmelCase = "pil" , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = self.unet.config.sample_size
__lowerCamelCase = (batch_size, 3, img_size, img_size)
__lowerCamelCase = self.unet
__lowerCamelCase = randn_tensor(A__ , generator=A__ ) * self.scheduler.init_noise_sigma
__lowerCamelCase = sample.to(self.device )
self.scheduler.set_timesteps(A__ )
self.scheduler.set_sigmas(A__ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
__lowerCamelCase = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
__lowerCamelCase = self.unet(A__ , A__ ).sample
__lowerCamelCase = self.scheduler.step_correct(A__ , A__ , generator=A__ ).prev_sample
# prediction step
__lowerCamelCase = model(A__ , A__ ).sample
__lowerCamelCase = self.scheduler.step_pred(A__ , A__ , A__ , generator=A__ )
__lowerCamelCase ,__lowerCamelCase = output.prev_sample, output.prev_sample_mean
__lowerCamelCase = sample_mean.clamp(0 , 1 )
__lowerCamelCase = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase = self.numpy_to_pil(A__ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=A__ )
| 700 |
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 __lowerCAmelCase ( lowerCAmelCase__ ):
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 , ):
'''simple docstring'''
# 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.
__lowerCamelCase = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
__lowerCamelCase = {} 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 , )
__lowerCamelCase = do_lower_case
__lowerCamelCase = remove_space
__lowerCamelCase = keep_accents
__lowerCamelCase = vocab_file
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {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 ):
'''simple docstring'''
__lowerCamelCase = self.__dict__.copy()
__lowerCamelCase = None
return state
def __setstate__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCamelCase = {}
__lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if self.remove_space:
__lowerCamelCase = ''' '''.join(inputs.strip().split() )
else:
__lowerCamelCase = inputs
__lowerCamelCase = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
__lowerCamelCase = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
__lowerCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
__lowerCamelCase = outputs.lower()
return outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.preprocess_text(__UpperCAmelCase )
__lowerCamelCase = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
__lowerCamelCase = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
__lowerCamelCase = 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:
__lowerCamelCase = cur_pieces[1:]
else:
__lowerCamelCase = 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 ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = ''''''
__lowerCamelCase = 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
__lowerCamelCase = True
__lowerCamelCase = []
else:
current_sub_tokens.append(__UpperCAmelCase )
__lowerCamelCase = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
__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 lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = 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:
__lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 622 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase = []
__lowerCamelCase = 0
__lowerCamelCase = 256
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = 0
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = cva.imread(lowerCAmelCase__ , 0 )
__lowerCamelCase = copy.deepcopy(self.img )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
__lowerCamelCase = np.sum(lowerCAmelCase__ )
for i in range(len(lowerCAmelCase__ ) ):
__lowerCamelCase = x[i] / self.k
self.sk += prk
__lowerCamelCase = (self.L - 1) * self.sk
if self.rem != 0:
__lowerCamelCase = int(last % last )
__lowerCamelCase = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase__ )
__lowerCamelCase = int(np.ma.count(self.img ) / self.img[1].size )
__lowerCamelCase = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
__lowerCamelCase = self.img[j][i]
if num != self.last_list[num]:
__lowerCamelCase = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def lowerCamelCase ( self ):
'''simple docstring'''
plt.hist(self.img.ravel() , 256 , [0, 256] )
def lowerCamelCase ( self ):
'''simple docstring'''
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 701 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 0 |
a_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 702 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionXLImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCamelCase = CLIPTextModel(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = CLIPTextModelWithProjection(__UpperCAmelCase )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCAmelCase )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
__lowerCamelCase = image / 2 + 0.5
if str(__UpperCAmelCase ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(__UpperCAmelCase )
else:
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = sd_pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
__lowerCamelCase = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# forward without prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = negative_prompt
__lowerCamelCase = 3 * [inputs['''prompt''']]
__lowerCamelCase = sd_pipe(**__UpperCAmelCase )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase = self.get_dummy_inputs(__UpperCAmelCase )
__lowerCamelCase = 3 * ['''this is a negative prompt''']
__lowerCamelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = sd_pipe.encode_prompt(__UpperCAmelCase , negative_prompt=__UpperCAmelCase )
__lowerCamelCase = sd_pipe(
**__UpperCAmelCase , prompt_embeds=__UpperCAmelCase , negative_prompt_embeds=__UpperCAmelCase , pooled_prompt_embeds=__UpperCAmelCase , negative_pooled_prompt_embeds=__UpperCAmelCase , )
__lowerCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase="cpu" , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
__lowerCamelCase = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase , dtype=__UpperCAmelCase )
__lowerCamelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = self.get_inputs(__UpperCAmelCase )
__lowerCamelCase = pipe(**__UpperCAmelCase ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 622 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
a_ = pd.read_csv("""sample_data.csv""", header=None)
a_ = df.shape[:1][0]
# If you're using some other dataset input the target column
a_ = df.iloc[:, 1:2]
a_ = actual_data.values.reshape(len_data, 1)
a_ = MinMaxScaler().fit_transform(actual_data)
a_ = 10
a_ = 5
a_ = 20
a_ = len_data - periods * look_back
a_ = actual_data[:division]
a_ = actual_data[division - look_back :]
a_ = [], []
a_ = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
a_ = np.array(train_x)
a_ = np.array(test_x)
a_ = np.array([list(i.ravel()) for i in train_y])
a_ = np.array([list(i.ravel()) for i in test_y])
a_ = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
a_ = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
a_ = model.predict(x_test)
| 703 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 622 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def a__ ( _UpperCamelCase : int ):
return 1.0 / (1.0 + np.exp(-_outputs ))
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = np.max(_outputs ,axis=-1 ,keepdims=_lowercase )
__lowerCamelCase = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 ,keepdims=_lowercase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """sigmoid"""
lowerCAmelCase__ = """softmax"""
lowerCAmelCase__ = """none"""
@add_end_docstrings(
lowerCAmelCase__ , r"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = False
lowerCAmelCase__ = ClassificationFunction.NONE
def __init__( self , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(**UpperCamelCase_ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="" , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = tokenizer_kwargs
__lowerCamelCase = {}
if hasattr(self.model.config , '''return_all_scores''' ) and return_all_scores is None:
__lowerCamelCase = self.model.config.return_all_scores
if isinstance(UpperCamelCase_ , UpperCamelCase_ ) or top_k is None:
__lowerCamelCase = top_k
__lowerCamelCase = False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' , UpperCamelCase_ , )
if return_all_scores:
__lowerCamelCase = None
else:
__lowerCamelCase = 1
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
__lowerCamelCase = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(*UpperCamelCase_ , **UpperCamelCase_ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
__lowerCamelCase = 'top_k' not in kwargs
if isinstance(args[0] , UpperCamelCase_ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase ( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.framework
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return self.tokenizer(**UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ) and len(UpperCamelCase_ ) == 1 and isinstance(inputs[0] , UpperCamelCase_ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.model(**UpperCamelCase_ )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=1 , __UpperCAmelCase=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
__lowerCamelCase = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
__lowerCamelCase = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , '''function_to_apply''' ) and function_to_apply is None:
__lowerCamelCase = self.model.config.function_to_apply
else:
__lowerCamelCase = ClassificationFunction.NONE
__lowerCamelCase = model_outputs['logits'][0]
__lowerCamelCase = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
__lowerCamelCase = sigmoid(UpperCamelCase_ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
__lowerCamelCase = softmax(UpperCamelCase_ )
elif function_to_apply == ClassificationFunction.NONE:
__lowerCamelCase = outputs
else:
raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
__lowerCamelCase = [
{'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCamelCase_ )
]
if not _legacy:
dict_scores.sort(key=lambda __UpperCAmelCase : x["score"] , reverse=UpperCamelCase_ )
if top_k is not None:
__lowerCamelCase = dict_scores[:top_k]
return dict_scores
| 704 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __lowerCAmelCase :
@staticmethod
def lowerCamelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def a__ ( _UpperCamelCase : List[str] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
__lowerCamelCase = '''What is the placebo?'''
__lowerCamelCase = [
{
'''image''': load_image(__UpperCAmelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
{'''score''': ANY(__UpperCAmelCase ), '''answer''': ANY(__UpperCAmelCase ), '''start''': ANY(__UpperCAmelCase ), '''end''': ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''How many cats are there?'''
__lowerCamelCase = [
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
__lowerCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.4_251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=__UpperCAmelCase )
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=__UpperCAmelCase , revision='''3dc6de3''' , max_seq_len=50 , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__lowerCamelCase = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__lowerCamelCase = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , '''''' ) ) )
# This model should also work if `image` is set to None
__lowerCamelCase = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''score''': 0.9_999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__lowerCamelCase = INVOICE_URL
__lowerCamelCase = '''What is the invoice number?'''
__lowerCamelCase = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
__lowerCamelCase = 10_24
__lowerCamelCase = 40_96
__lowerCamelCase = 24
__lowerCamelCase = 16
__lowerCamelCase = [5, 11, 17, 23]
__lowerCamelCase = [2_56, 5_12, 10_24, 10_24]
__lowerCamelCase = (1, 3_84, 3_84)
if "nyu" or "midas" in checkpoint_url:
__lowerCamelCase = 7_68
__lowerCamelCase = [1, 1, 1, 0.5]
__lowerCamelCase = [2_56, 5_12, 7_68, 7_68]
__lowerCamelCase = 1_50
__lowerCamelCase = 16
__lowerCamelCase = (1, 3_84, 3_84)
__lowerCamelCase = False
__lowerCamelCase = "project"
if "ade" in checkpoint_url:
__lowerCamelCase = True
__lowerCamelCase = 7_68
__lowerCamelCase = [1, 1, 1, 0.5]
__lowerCamelCase = 1_50
__lowerCamelCase = 16
__lowerCamelCase = "huggingface/label-files"
__lowerCamelCase = "ade20k-id2label.json"
__lowerCamelCase = json.load(open(cached_download(hf_hub_url(_A ,_A ,repo_type='''dataset''' ) ) ,'''r''' ) )
__lowerCamelCase = {int(_A ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def a__ ( _UpperCamelCase : List[str] ):
__lowerCamelCase = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
for k in ignore_keys:
state_dict.pop(_A ,_A )
def a__ ( _UpperCamelCase : Optional[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
__lowerCamelCase = name.replace('''pretrained.model''' ,'''dpt.encoder''' )
if "pretrained.model" in name:
__lowerCamelCase = name.replace('''pretrained.model''' ,'''dpt.embeddings''' )
if "patch_embed" in name:
__lowerCamelCase = name.replace('''patch_embed''' ,'''''' )
if "pos_embed" in name:
__lowerCamelCase = name.replace('''pos_embed''' ,'''position_embeddings''' )
if "attn.proj" in name:
__lowerCamelCase = name.replace('''attn.proj''' ,'''attention.output.dense''' )
if "proj" in name and "project" not in name:
__lowerCamelCase = name.replace('''proj''' ,'''projection''' )
if "blocks" in name:
__lowerCamelCase = name.replace('''blocks''' ,'''layer''' )
if "mlp.fc1" in name:
__lowerCamelCase = name.replace('''mlp.fc1''' ,'''intermediate.dense''' )
if "mlp.fc2" in name:
__lowerCamelCase = name.replace('''mlp.fc2''' ,'''output.dense''' )
if "norm1" in name and "backbone" not in name:
__lowerCamelCase = name.replace('''norm1''' ,'''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
__lowerCamelCase = name.replace('''norm2''' ,'''layernorm_after''' )
if "scratch.output_conv" in name:
__lowerCamelCase = name.replace('''scratch.output_conv''' ,'''head''' )
if "scratch" in name:
__lowerCamelCase = name.replace('''scratch''' ,'''neck''' )
if "layer1_rn" in name:
__lowerCamelCase = name.replace('''layer1_rn''' ,'''convs.0''' )
if "layer2_rn" in name:
__lowerCamelCase = name.replace('''layer2_rn''' ,'''convs.1''' )
if "layer3_rn" in name:
__lowerCamelCase = name.replace('''layer3_rn''' ,'''convs.2''' )
if "layer4_rn" in name:
__lowerCamelCase = name.replace('''layer4_rn''' ,'''convs.3''' )
if "refinenet" in name:
__lowerCamelCase = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
__lowerCamelCase = name.replace(F"""refinenet{layer_idx}""" ,F"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
__lowerCamelCase = name.replace('''out_conv''' ,'''projection''' )
if "resConfUnit1" in name:
__lowerCamelCase = name.replace('''resConfUnit1''' ,'''residual_layer1''' )
if "resConfUnit2" in name:
__lowerCamelCase = name.replace('''resConfUnit2''' ,'''residual_layer2''' )
if "conv1" in name:
__lowerCamelCase = name.replace('''conv1''' ,'''convolution1''' )
if "conv2" in name:
__lowerCamelCase = name.replace('''conv2''' ,'''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess1.0.project.0''' ,'''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess2.0.project.0''' ,'''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess3.0.project.0''' ,'''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess4.0.project.0''' ,'''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess1.3''' ,'''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess1.4''' ,'''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess2.3''' ,'''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess2.4''' ,'''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess3.3''' ,'''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess4.3''' ,'''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
__lowerCamelCase = name.replace('''pretrained.act_postprocess4.4''' ,'''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
__lowerCamelCase = name.replace('''pretrained''' ,'''dpt''' )
if "bn" in name:
__lowerCamelCase = name.replace('''bn''' ,'''batch_norm''' )
if "head" in name:
__lowerCamelCase = name.replace('''head''' ,'''head.head''' )
if "encoder.norm" in name:
__lowerCamelCase = name.replace('''encoder.norm''' ,'''layernorm''' )
if "auxlayer" in name:
__lowerCamelCase = name.replace('''auxlayer''' ,'''auxiliary_head.head''' )
if "backbone" in name:
__lowerCamelCase = name.replace('''backbone''' ,'''backbone.bit.encoder''' )
if ".." in name:
__lowerCamelCase = name.replace('''..''' ,'''.''' )
if "stem.conv" in name:
__lowerCamelCase = name.replace('''stem.conv''' ,'''bit.embedder.convolution''' )
if "blocks" in name:
__lowerCamelCase = name.replace('''blocks''' ,'''layers''' )
if "convolution" in name and "backbone" in name:
__lowerCamelCase = name.replace('''convolution''' ,'''conv''' )
if "layer" in name and "backbone" in name:
__lowerCamelCase = name.replace('''layer''' ,'''layers''' )
if "backbone.bit.encoder.bit" in name:
__lowerCamelCase = name.replace('''backbone.bit.encoder.bit''' ,'''backbone.bit''' )
if "embedder.conv" in name:
__lowerCamelCase = name.replace('''embedder.conv''' ,'''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
__lowerCamelCase = name.replace('''backbone.bit.encoder.stem.norm''' ,'''backbone.bit.embedder.norm''' )
return name
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Dict ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
__lowerCamelCase = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[: config.hidden_size, :]
__lowerCamelCase = in_proj_bias[: config.hidden_size]
__lowerCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase = in_proj_bias[-config.hidden_size :]
def a__ ( ):
__lowerCamelCase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowerCamelCase = Image.open(requests.get(_A ,stream=_A ).raw )
return im
@torch.no_grad()
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : List[str] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : List[str] ):
__lowerCamelCase = get_dpt_config(_A )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
__lowerCamelCase = torch.load(_A ,map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(_A )
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(_A )
__lowerCamelCase = val
# read in qkv matrices
read_in_q_k_v(_A ,_A )
# load HuggingFace model
__lowerCamelCase = DPTForSemanticSegmentation(_A ) if "ade" in checkpoint_url else DPTForDepthEstimation(_A )
model.load_state_dict(_A )
model.eval()
# Check outputs on an image
__lowerCamelCase = 4_80 if "ade" in checkpoint_url else 3_84
__lowerCamelCase = DPTImageProcessor(size=_A )
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(_A ,return_tensors='''pt''' )
# forward pass
__lowerCamelCase = model(**_A ).logits if "ade" in checkpoint_url else model(**_A ).predicted_depth
if show_prediction:
__lowerCamelCase = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) ,size=(image.size[1], image.size[0]) ,mode='''bicubic''' ,align_corners=_A ,)
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 2_55 ).show()
if pytorch_dump_folder_path is not None:
Path(_A ).mkdir(exist_ok=_A )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(_A )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_A )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
parser.add_argument(
"""--show_prediction""",
action="""store_true""",
)
a_ = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 705 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = XLMProphetNetTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''[PAD]'''
__lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__UpperCAmelCase ) , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = XLMProphetNetTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''Hello World!'''
__lowerCamelCase = [35389, 6672, 49, 2]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 622 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
a_ = 300 # TEMPERATURE (unit = K)
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ,):
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
a_ = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def a__ ( _UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = None
# source code of `config_class`
__lowerCamelCase = inspect.getsource(_UpperCamelCase )
__lowerCamelCase = _re_checkpoint.findall(_UpperCamelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('''/''' ):
__lowerCamelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
__lowerCamelCase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
__lowerCamelCase = ckpt_name
break
return checkpoint
def a__ ( ):
__lowerCamelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
__lowerCamelCase = get_checkpoint_from_config_class(_UpperCamelCase )
__lowerCamelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
__lowerCamelCase = '''\n'''.join(sorted(_UpperCamelCase ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 622 | 0 |
import math
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ):
if (
not isinstance(_UpperCamelCase ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * power_factor
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ):
if (
not isinstance(_UpperCamelCase ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 622 | 0 |
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""": 2_048,
}
class __lowerCAmelCase ( __A ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["""input_ids""", """attention_mask"""]
lowerCAmelCase__ = CodeGenTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase=False , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , unk_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , **__UpperCAmelCase , )
if kwargs.pop('''add_bos_token''' , __UpperCAmelCase ):
__lowerCamelCase = 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.''' )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __UpperCAmelCase ) != add_prefix_space:
__lowerCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**__UpperCAmelCase )
__lowerCamelCase = add_prefix_space
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
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(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = kwargs.get('''is_split_into_words''' , __UpperCAmelCase )
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(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = super().decode(
token_ids=__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , **__UpperCAmelCase , )
if truncate_before_pattern is not None and len(__UpperCAmelCase ) > 0:
__lowerCamelCase = self.truncate(__UpperCAmelCase , __UpperCAmelCase )
return decoded_text
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
def find_re(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = pattern.search(__UpperCAmelCase , __UpperCAmelCase )
return m.start() if m else -1
__lowerCamelCase = [re.compile(__UpperCAmelCase , re.MULTILINE ) for pattern in truncate_before_pattern]
__lowerCamelCase = list(re.finditer('''^print''' , __UpperCAmelCase , re.MULTILINE ) )
if len(__UpperCAmelCase ) > 1:
__lowerCamelCase = completion[: prints[1].start()]
__lowerCamelCase = list(re.finditer('''^def''' , __UpperCAmelCase , re.MULTILINE ) )
if len(__UpperCAmelCase ) > 1:
__lowerCamelCase = completion[: defs[1].start()]
__lowerCamelCase = 0
__lowerCamelCase = [
pos for pos in [find_re(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for terminal in terminals] if pos != -1
]
if len(__UpperCAmelCase ) > 0:
return completion[: min(__UpperCAmelCase )]
else:
return completion
| 708 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = RoFormerTokenizer
lowerCAmelCase__ = RoFormerTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
__lowerCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase ,__lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
| 622 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
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',
'adapter_layer': 'encoder.layers.*.adapter_layer',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
'pooling_layer.linear': 'projector',
'pooling_layer.projection': 'classifier',
}
a_ = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'projector',
'classifier',
]
def a__ ( _UpperCamelCase : List[str] ):
__lowerCamelCase = {}
with open(__lowerCAmelCase ,'''r''' ) as file:
for line_number, line in enumerate(__lowerCAmelCase ):
__lowerCamelCase = line.strip()
if line:
__lowerCamelCase = line.split()
__lowerCamelCase = line_number
__lowerCamelCase = words[0]
__lowerCamelCase = value
return result
def a__ ( _UpperCamelCase : Optional[Any] ,_UpperCamelCase : int ,_UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : List[Any] ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(__lowerCAmelCase ,__lowerCAmelCase )
__lowerCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCAmelCase ):
__lowerCamelCase = PARAM_MAPPING[full_name.split('''.''' )[-1]]
__lowerCamelCase = '''param'''
if weight_type is not None and weight_type != "param":
__lowerCamelCase = getattr(__lowerCAmelCase ,__lowerCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
__lowerCamelCase = hf_pointer
for attribute in hf_param_name.split('''.''' ):
__lowerCamelCase = getattr(__lowerCAmelCase ,__lowerCAmelCase )
__lowerCamelCase = shape_pointer.shape
# let's reduce dimension
__lowerCamelCase = value[0]
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
__lowerCamelCase = getattr(__lowerCAmelCase ,__lowerCAmelCase )
__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 : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : int ,_UpperCamelCase : int ,_UpperCamelCase : int ):
__lowerCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCAmelCase ):
__lowerCamelCase = PARAM_MAPPING[full_name.split('''.''' )[-1]]
__lowerCamelCase = '''param'''
if weight_type is not None and weight_type != "param":
__lowerCamelCase = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
__lowerCamelCase = '''.'''.join([key, hf_param_name] )
else:
__lowerCamelCase = key
__lowerCamelCase = value if '''lm_head''' in full_key else value[0]
a_ = {
'W_a': 'linear_1.weight',
'W_b': 'linear_2.weight',
'b_a': 'linear_1.bias',
'b_b': 'linear_2.bias',
'ln_W': 'norm.weight',
'ln_b': 'norm.bias',
}
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : str=None ,_UpperCamelCase : Optional[int]=None ):
__lowerCamelCase = False
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(__lowerCAmelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,__lowerCAmelCase )
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
if hf_dict is not None:
rename_dict(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase )
else:
set_recursively(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase )
return is_used
return is_used
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : List[Any] ,_UpperCamelCase : int ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
__lowerCamelCase = load_wavaveca_layer(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase )
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : List[Any] ,_UpperCamelCase : Optional[int] ,_UpperCamelCase : Tuple ,_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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__lowerCAmelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Optional[int]=None ,_UpperCamelCase : Any=None ,_UpperCamelCase : Any=True ,_UpperCamelCase : str=False ):
if config_path is not None:
__lowerCamelCase = WavaVecaConfig.from_pretrained(__lowerCAmelCase )
else:
__lowerCamelCase = WavaVecaConfig()
if is_seq_class:
__lowerCamelCase = read_txt_into_dict(__lowerCAmelCase )
__lowerCamelCase = idalabel
__lowerCamelCase = WavaVecaForSequenceClassification(__lowerCAmelCase )
__lowerCamelCase = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowerCAmelCase ,return_attention_mask=__lowerCAmelCase ,)
feature_extractor.save_pretrained(__lowerCAmelCase )
elif is_finetuned:
if dict_path:
__lowerCamelCase = Dictionary.load(__lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__lowerCamelCase = target_dict.pad_index
__lowerCamelCase = target_dict.bos_index
__lowerCamelCase = target_dict.eos_index
__lowerCamelCase = len(target_dict.symbols )
__lowerCamelCase = os.path.join(__lowerCAmelCase ,'''vocab.json''' )
if not os.path.isdir(__lowerCAmelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCAmelCase ) )
return
os.makedirs(__lowerCAmelCase ,exist_ok=__lowerCAmelCase )
__lowerCamelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
__lowerCamelCase = 0
__lowerCamelCase = 1
with open(__lowerCAmelCase ,'''w''' ,encoding='''utf-8''' ) as vocab_handle:
json.dump(__lowerCAmelCase ,__lowerCAmelCase )
__lowerCamelCase = WavaVecaCTCTokenizer(
__lowerCAmelCase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='''|''' ,do_lower_case=__lowerCAmelCase ,)
__lowerCamelCase = True if config.feat_extract_norm == '''layer''' else False
__lowerCamelCase = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowerCAmelCase ,return_attention_mask=__lowerCAmelCase ,)
__lowerCamelCase = WavaVecaProcessor(feature_extractor=__lowerCAmelCase ,tokenizer=__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
__lowerCamelCase = WavaVecaForCTC(__lowerCAmelCase )
else:
__lowerCamelCase = WavaVecaForPreTraining(__lowerCAmelCase )
if is_finetuned or is_seq_class:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__lowerCamelCase = argparse.Namespace(task='''audio_pretraining''' )
__lowerCamelCase = fairseq.tasks.setup_task(__lowerCAmelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=__lowerCAmelCase )
__lowerCamelCase = model[0].eval()
recursively_load_weights(__lowerCAmelCase ,__lowerCAmelCase ,not is_finetuned )
hf_wavavec.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
a_ = parser.parse_args()
a_ = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 709 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCAmelCase )
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt='''first prompt''' , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
__lowerCamelCase = '''cyberpunk 2077'''
__lowerCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.dual_guided(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , text_to_image_strength=0.75 , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = '''A painting of a squirrel eating a burger '''
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe.text_to_image(
prompt=__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__lowerCamelCase = pipe.image_variation(__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''numpy''' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 622 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a__ ( ):
__lowerCamelCase = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.py", "test_2.py", "unit_test.py"],
"content": ["a " * 20, "a " * 30, "b " * 7],
}
__lowerCamelCase = Dataset.from_dict(lowerCamelCase__ )
return dataset
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = get_dataset()
__lowerCamelCase = make_duplicate_clusters(UpperCamelCase_ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = get_dataset()
__lowerCamelCase = deduplicate_dataset(UpperCamelCase_ )
self.assertEqual(len(UpperCamelCase_ ) , 2 )
print(UpperCamelCase_ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , UpperCamelCase_ )
| 710 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
a_ = getLogger(__name__)
a_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int = 8 ,_UpperCamelCase : str = DEFAULT_DEVICE ,_UpperCamelCase : Dict=False ,_UpperCamelCase : Dict="summarization" ,_UpperCamelCase : Optional[int]=None ,**_UpperCamelCase : Dict ,):
__lowerCamelCase = Path(_UpperCamelCase ).open('''w''' ,encoding='''utf-8''' )
__lowerCamelCase = str(_UpperCamelCase )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCamelCase = model.half()
__lowerCamelCase = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCamelCase = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase ,_UpperCamelCase )
if prefix is None:
__lowerCamelCase = prefix or getattr(model.config ,'''prefix''' ,'''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase ) ) ):
__lowerCamelCase = [prefix + text for text in examples_chunk]
__lowerCamelCase = tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest''' ).to(_UpperCamelCase )
__lowerCamelCase = model.generate(
input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
__lowerCamelCase = tokenizer.batch_decode(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase ,clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__lowerCamelCase = int(time.time() - start_time ) # seconds
__lowerCamelCase = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4 )}
def a__ ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def a__ ( _UpperCamelCase : Union[str, Any]=True ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries''' )
parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics''' )
parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size''' )
parser.add_argument(
'''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' ,action='''store_true''' )
parser.add_argument('''--dump-args''' ,action='''store_true''' ,help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' ,nargs='''?''' ,type=_UpperCamelCase ,const=datetime_now() ,help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) ,)
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCamelCase ,__lowerCamelCase = parser.parse_known_args()
__lowerCamelCase = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCamelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__lowerCamelCase = generate_summaries_or_translations(
_UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)
if args.reference_path is None:
return {}
# Compute scores
__lowerCamelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__lowerCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCamelCase = score_fn(_UpperCamelCase ,_UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCamelCase = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase ,open(args.score_path ,'''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 622 | 0 |
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