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 |
|---|---|---|---|---|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '▁'
__A = {'vocab_file': 'sentencepiece.bpe.model'}
__A = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
),
}
}
__A = {
'facebook/nllb-200-distilled-600M': 1024,
}
# fmt: off
__A = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = ["input_ids", "attention_mask"]
A_ = []
A_ = []
def __init__( self: Dict , __A: List[Any] , __A: Optional[Any]="<s>" , __A: Any="</s>" , __A: List[Any]="</s>" , __A: Optional[Any]="<s>" , __A: Optional[int]="<unk>" , __A: Any="<pad>" , __A: List[str]="<mask>" , __A: Union[str, Any]=None , __A: Tuple=None , __A: Tuple=None , __A: Optional[Dict[str, Any]] = None , __A: Optional[Any]=None , __A: Optional[int]=False , **__A: Optional[Any] , ) -> str:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
_A = legacy_behaviour
super().__init__(
bos_token=__A , eos_token=__A , unk_token=__A , sep_token=__A , cls_token=__A , pad_token=__A , mask_token=__A , tokenizer_file=__A , src_lang=__A , tgt_lang=__A , additional_special_tokens=__A , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=__A , **__A , )
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__A ) )
_A = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
_A = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_A = 1
_A = len(self.sp_model )
_A = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A )
}
_A = {v: k for k, v in self.lang_code_to_id.items()}
_A = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_A = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
_A = src_lang if src_lang is not None else '''eng_Latn'''
_A = self.lang_code_to_id[self._src_lang]
_A = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self: Any ) -> Dict:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self: Any , __A: List[str] ) -> Any:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def __A ( self: Tuple ) -> Union[str, Any]:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __A ( self: Tuple ) -> str:
return self._src_lang
@src_lang.setter
def __A ( self: Any , __A: str ) -> None:
_A = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __A ( self: List[str] , __A: List[int] , __A: Optional[List[int]] = None , __A: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
_A = [1] * len(self.prefix_tokens )
_A = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__A )) + suffix_ones
return prefix_ones + ([0] * len(__A )) + ([0] * len(__A )) + suffix_ones
def __A ( self: int , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __A ( self: Optional[int] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __A ( self: List[str] , __A: Any , __A: str , __A: Optional[str] , __A: Optional[str] , **__A: Dict ) -> int:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_A = src_lang
_A = self(__A , add_special_tokens=__A , return_tensors=__A , **__A )
_A = self.convert_tokens_to_ids(__A )
_A = tgt_lang_id
return inputs
def __A ( self: str ) -> str:
_A = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __A ( self: Optional[Any] , __A: str ) -> List[str]:
return self.sp_model.encode(__A , out_type=__A )
def __A ( self: Any , __A: Optional[Any] ) -> str:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_A = self.sp_model.PieceToId(__A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __A ( self: List[str] , __A: int ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __A ( self: int , __A: Tuple ) -> str:
_A = ''''''.join(__A ).replace(__A , ''' ''' ).strip()
return out_string
def __A ( self: Any , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_A = os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
def __A ( self: List[Any] , __A: List[str] , __A: str = "eng_Latn" , __A: Optional[List[str]] = None , __A: str = "fra_Latn" , **__A: str , ) -> BatchEncoding:
_A = src_lang
_A = tgt_lang
return super().prepare_seqaseq_batch(__A , __A , **__A )
def __A ( self: Optional[int] ) -> Union[str, Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def __A ( self: str ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __A ( self: Any , __A: Any ) -> None:
_A = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
_A = []
_A = [self.eos_token_id, self.cur_lang_code]
else:
_A = [self.cur_lang_code]
_A = [self.eos_token_id]
def __A ( self: Union[str, Any] , __A: str ) -> None:
_A = self.lang_code_to_id[lang]
if self.legacy_behaviour:
_A = []
_A = [self.eos_token_id, self.cur_lang_code]
else:
_A = [self.cur_lang_code]
_A = [self.eos_token_id]
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
__A = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
__A = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
__A = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
if label_map is not None:
for old_id, new_id in label_map.items():
_A = new_id
# turn into Numpy arrays
_A = np.array(_lowercase )
_A = np.array(_lowercase )
if reduce_labels:
_A = 2_55
_A = label - 1
_A = 2_55
_A = label != ignore_index
_A = np.not_equal(_lowercase , _lowercase )
_A = pred_label[mask]
_A = np.array(_lowercase )[mask]
_A = pred_label[pred_label == label]
_A = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
_A = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
_A = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
_A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
_A = np.zeros((num_labels,) , dtype=np.floataa )
_A = np.zeros((num_labels,) , dtype=np.floataa )
_A = np.zeros((num_labels,) , dtype=np.floataa )
_A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(_lowercase , _lowercase ):
_A ,_A ,_A ,_A = intersect_and_union(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
_A ,_A ,_A ,_A = total_intersect_and_union(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# compute metrics
_A = {}
_A = total_area_intersect.sum() / total_area_label.sum()
_A = total_area_intersect / total_area_union
_A = total_area_intersect / total_area_label
_A = np.nanmean(_lowercase )
_A = np.nanmean(_lowercase )
_A = all_acc
_A = iou
_A = acc
if nan_to_num is not None:
_A = {metric: np.nan_to_num(_lowercase , nan=_lowercase ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __A ( self: int ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
'''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
} ) , reference_urls=[
'''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
] , )
def __A ( self: Optional[int] , __A: List[Any] , __A: Optional[Any] , __A: int , __A: bool , __A: Optional[int] = None , __A: Optional[Dict[int, int]] = None , __A: bool = False , ) -> Dict:
_A = mean_iou(
results=__A , gt_seg_maps=__A , num_labels=__A , ignore_index=__A , nan_to_num=__A , label_map=__A , reduce_labels=__A , )
return iou_result
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import sys
import turtle
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'Correct format for using this script: '
'python fractals.py <int:depth_for_fractal>'
)
__A = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('red')
__A = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __A ( _lowercase , _lowercase , _lowercase = 1 , _lowercase = 1 , _lowercase = 1.0e4 , _lowercase = False , _lowercase = 1.0 , ):
'''simple docstring'''
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
_A = float(embedding_dim // 2 )
_A = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
_A = min_timescale * jnp.exp(jnp.arange(_lowercase , dtype=jnp.floataa ) * -log_timescale_increment )
_A = jnp.expand_dims(_lowercase , 1 ) * jnp.expand_dims(_lowercase , 0 )
# scale embeddings
_A = scale * emb
if flip_sin_to_cos:
_A = jnp.concatenate([jnp.cos(_lowercase ), jnp.sin(_lowercase )] , axis=1 )
else:
_A = jnp.concatenate([jnp.sin(_lowercase ), jnp.cos(_lowercase )] , axis=1 )
_A = jnp.reshape(_lowercase , [jnp.shape(_lowercase )[0], embedding_dim] )
return signal
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 32
A_ = jnp.floataa
@nn.compact
def __call__( self: List[Any] , __A: Tuple ) -> Optional[Any]:
_A = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(__A )
_A = nn.silu(__A )
_A = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(__A )
return temb
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 32
A_ = False
A_ = 1
@nn.compact
def __call__( self: Union[str, Any] , __A: List[str] ) -> Any:
return get_sinusoidal_embeddings(
__A , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 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=snake_case )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = 42
A_ = 42
A_ = None
A_ = None
A_ = None
@dataclass(frozen=snake_case )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = 42
A_ = None
A_ = None
A_ = None
A_ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[int] , __A: str , __A: PreTrainedTokenizer , __A: str , __A: Optional[int] = None , __A: str=False , __A: bool = False , ) -> Dict:
_A = hans_processors[task]()
_A = os.path.join(
__A , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(__A ) , __A , ) , )
_A = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_A ,_A = label_list[2], label_list[1]
_A = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_A = cached_features_file + '''.lock'''
with FileLock(__A ):
if os.path.exists(__A ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
_A = torch.load(__A )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
_A = (
processor.get_dev_examples(__A ) if evaluate else processor.get_train_examples(__A )
)
logger.info('''Training examples: %s''' , len(__A ) )
_A = hans_convert_examples_to_features(__A , __A , __A , __A )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(self.features , __A )
def __len__( self: Dict ) -> Any:
return len(self.features )
def __getitem__( self: Any , __A: List[Any] ) -> InputFeatures:
return self.features[i]
def __A ( self: Tuple ) -> Optional[Any]:
return self.label_list
if is_tf_available():
import tensorflow as tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = 42
def __init__( self: Tuple , __A: str , __A: PreTrainedTokenizer , __A: str , __A: Optional[int] = 1_28 , __A: Dict=False , __A: bool = False , ) -> Optional[int]:
_A = hans_processors[task]()
_A = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_A ,_A = label_list[2], label_list[1]
_A = label_list
_A = processor.get_dev_examples(__A ) if evaluate else processor.get_train_examples(__A )
_A = hans_convert_examples_to_features(__A , __A , __A , __A )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(__A )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
_A = tf.data.Dataset.from_generator(
__A , (
{
'''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 __A ( self: int ) -> Dict:
return self.dataset
def __len__( self: List[str] ) -> str:
return len(self.features )
def __getitem__( self: int , __A: Optional[Any] ) -> InputFeatures:
return self.features[i]
def __A ( self: List[str] ) -> int:
return self.label_list
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: Tuple , __A: List[str] ) -> str:
return self._create_examples(self._read_tsv(os.path.join(__A , '''heuristics_train_set.txt''' ) ) , '''train''' )
def __A ( self: Any , __A: str ) -> int:
return self._create_examples(self._read_tsv(os.path.join(__A , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def __A ( self: Tuple ) -> Any:
return ["contradiction", "entailment", "neutral"]
def __A ( self: List[str] , __A: str , __A: Any ) -> Any:
_A = []
for i, line in enumerate(__A ):
if i == 0:
continue
_A = '''%s-%s''' % (set_type, line[0])
_A = line[5]
_A = line[6]
_A = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
_A = line[0]
examples.append(InputExample(guid=__A , text_a=__A , text_b=__A , label=__A , pairID=__A ) )
return examples
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
_A = {label: i for i, label in enumerate(_lowercase )}
_A = []
for ex_index, example in tqdm.tqdm(enumerate(_lowercase ) , desc='''convert examples to features''' ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d''' % (ex_index) )
_A = tokenizer(
example.text_a , example.text_b , add_special_tokens=_lowercase , max_length=_lowercase , padding='''max_length''' , truncation=_lowercase , return_overflowing_tokens=_lowercase , )
_A = label_map[example.label] if example.label in label_map else 0
_A = int(example.pairID )
features.append(InputFeatures(**_lowercase , label=_lowercase , pairID=_lowercase ) )
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,
}
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
import os
def __A ( _lowercase = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_lowercase ) , _lowercase ) ) as input_file:
_A = [
[int(_lowercase ) for element in line.split(''',''' )]
for line in input_file.readlines()
]
_A = len(_lowercase )
_A = len(matrix[0] )
_A = [[-1 for _ in range(_lowercase )] for _ in range(_lowercase )]
for i in range(_lowercase ):
_A = matrix[i][0]
for j in range(1 , _lowercase ):
for i in range(_lowercase ):
_A = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _lowercase ):
_A = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
_A = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'{solution() = }')
| 62 |
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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def __A ( _lowercase ):
'''simple docstring'''
_A = fname.split(os.path.sep )[-1]
return re.search(R'''^(.*)_\d+\.jpg$''' , _lowercase ).groups()[0]
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Tuple=None , __A: List[str]=None ) -> Tuple:
_A = file_names
_A = image_transform
_A = label_to_id
def __len__( self: Dict ) -> str:
return len(self.file_names )
def __getitem__( self: Union[str, Any] , __A: Optional[Any] ) -> int:
_A = self.file_names[idx]
_A = PIL.Image.open(__A )
_A = raw_image.convert('''RGB''' )
if self.image_transform is not None:
_A = self.image_transform(__A )
_A = extract_label(__A )
if self.label_to_id is not None:
_A = self.label_to_id[label]
return {"image": image, "label": label}
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if args.with_tracking:
_A = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_A = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_A = config['''lr''']
_A = int(config['''num_epochs'''] )
_A = int(config['''seed'''] )
_A = int(config['''batch_size'''] )
_A = config['''image_size''']
if not isinstance(_lowercase , (list, tuple) ):
_A = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , '''isdigit''' ):
if args.checkpointing_steps == "epoch":
_A = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
_A = int(args.checkpointing_steps )
else:
raise ValueError(
f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
else:
_A = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
_A = os.path.split(_lowercase )[-1].split('''.''' )[0]
accelerator.init_trackers(_lowercase , _lowercase )
# Grab all the image filenames
_A = [os.path.join(args.data_dir , _lowercase ) for fname in os.listdir(args.data_dir ) if fname.endswith('''.jpg''' )]
# Build the label correspondences
_A = [extract_label(_lowercase ) for fname in file_names]
_A = list(set(_lowercase ) )
id_to_label.sort()
_A = {lbl: i for i, lbl in enumerate(_lowercase )}
# Set the seed before splitting the data.
np.random.seed(_lowercase )
torch.manual_seed(_lowercase )
torch.cuda.manual_seed_all(_lowercase )
# Split our filenames between train and validation
_A = np.random.permutation(len(_lowercase ) )
_A = int(0.8 * len(_lowercase ) )
_A = random_perm[:cut]
_A = random_perm[cut:]
# For training we use a simple RandomResizedCrop
_A = Compose([RandomResizedCrop(_lowercase , scale=(0.5, 1.0) ), ToTensor()] )
_A = PetsDataset(
[file_names[i] for i in train_split] , image_transform=_lowercase , label_to_id=_lowercase )
# For evaluation, we use a deterministic Resize
_A = Compose([Resize(_lowercase ), ToTensor()] )
_A = PetsDataset([file_names[i] for i in eval_split] , image_transform=_lowercase , label_to_id=_lowercase )
# Instantiate dataloaders.
_A = DataLoader(_lowercase , shuffle=_lowercase , batch_size=_lowercase , num_workers=4 )
_A = DataLoader(_lowercase , shuffle=_lowercase , batch_size=_lowercase , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_A = create_model('''resnet50d''' , pretrained=_lowercase , num_classes=len(_lowercase ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_A = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
_A = False
for param in model.get_classifier().parameters():
_A = True
# We normalize the batches of images to be a bit faster.
_A = torch.tensor(model.default_cfg['''mean'''] )[None, :, None, None].to(accelerator.device )
_A = torch.tensor(model.default_cfg['''std'''] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
_A = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
_A = OneCycleLR(optimizer=_lowercase , max_lr=_lowercase , epochs=_lowercase , steps_per_epoch=len(_lowercase ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_A ,_A ,_A ,_A ,_A = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# We need to keep track of how many total steps we have iterated over
_A = 0
# We also need to keep track of the starting epoch so files are named properly
_A = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""" )
accelerator.load_state(args.resume_from_checkpoint )
_A = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
_A = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
_A = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
_A = os.path.splitext(_lowercase )[0]
if "epoch" in training_difference:
_A = int(training_difference.replace('''epoch_''' , '''''' ) ) + 1
_A = None
else:
_A = int(training_difference.replace('''step_''' , '''''' ) )
_A = resume_step // len(_lowercase )
resume_step -= starting_epoch * len(_lowercase )
# Now we train the model
for epoch in range(_lowercase , _lowercase ):
model.train()
if args.with_tracking:
_A = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
_A = accelerator.skip_first_batches(_lowercase , _lowercase )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
_A = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
_A = {k: v.to(accelerator.device ) for k, v in batch.items()}
_A = (batch['''image'''] - mean) / std
_A = model(_lowercase )
_A = torch.nn.functional.cross_entropy(_lowercase , batch['''label'''] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(_lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(_lowercase , _lowercase ):
_A = f"""step_{overall_step}"""
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
_A = os.path.join(args.output_dir , _lowercase )
accelerator.save_state(_lowercase )
model.eval()
_A = 0
_A = 0
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
_A = {k: v.to(accelerator.device ) for k, v in batch.items()}
_A = (batch['''image'''] - mean) / std
with torch.no_grad():
_A = model(_lowercase )
_A = outputs.argmax(dim=-1 )
_A ,_A = accelerator.gather_for_metrics((predictions, batch['''label''']) )
_A = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
_A = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}""" )
if args.with_tracking:
accelerator.log(
{
'''accuracy''': 1_00 * eval_metric,
'''train_loss''': total_loss.item() / len(_lowercase ),
'''epoch''': epoch,
} , step=_lowercase , )
if checkpointing_steps == "epoch":
_A = f"""epoch_{epoch}"""
if args.output_dir is not None:
_A = os.path.join(args.output_dir , _lowercase )
accelerator.save_state(_lowercase )
if args.with_tracking:
accelerator.end_training()
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument('''--data_dir''' , required=_lowercase , help='''The data folder on disk.''' )
parser.add_argument('''--fp16''' , action='''store_true''' , help='''If passed, will use FP16 training.''' )
parser.add_argument(
'''--mixed_precision''' , type=_lowercase , default=_lowercase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--checkpointing_steps''' , type=_lowercase , default=_lowercase , help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' , )
parser.add_argument(
'''--output_dir''' , type=_lowercase , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=_lowercase , default=_lowercase , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_lowercase , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_A = parser.parse_args()
_A = {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
training_function(_lowercase , _lowercase )
if __name__ == "__main__":
main()
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = int(_lowercase )
# Initialize Result
_A = []
# Traverse through all denomination
for denomination in reversed(_lowercase ):
# Find denominations
while int(_lowercase ) >= int(_lowercase ):
total_value -= int(_lowercase )
answer.append(_lowercase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
__A = []
__A = '0'
if (
input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
== "y"
):
__A = int(input('Enter the number of denominations you want to add: ').strip())
for i in range(0, n):
denominations.append(int(input(f'Denomination {i}: ').strip()))
__A = input('Enter the change you want to make in Indian Currency: ').strip()
else:
# All denominations of Indian Currency if user does not enter
__A = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
__A = input('Enter the change you want to make: ').strip()
if int(value) == 0 or int(value) < 0:
print('The total value cannot be zero or negative.')
else:
print(f'Following is minimal change for {value}: ')
__A = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=' ')
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import argparse
import struct
import unittest
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Union[str, Any] , __A: bytes ) -> None:
_A = data
# Initialize hash values
_A = [
0X6_a_0_9_e_6_6_7,
0Xb_b_6_7_a_e_8_5,
0X3_c_6_e_f_3_7_2,
0Xa_5_4_f_f_5_3_a,
0X5_1_0_e_5_2_7_f,
0X9_b_0_5_6_8_8_c,
0X1_f_8_3_d_9_a_b,
0X5_b_e_0_c_d_1_9,
]
# Initialize round constants
_A = [
0X4_2_8_a_2_f_9_8,
0X7_1_3_7_4_4_9_1,
0Xb_5_c_0_f_b_c_f,
0Xe_9_b_5_d_b_a_5,
0X3_9_5_6_c_2_5_b,
0X5_9_f_1_1_1_f_1,
0X9_2_3_f_8_2_a_4,
0Xa_b_1_c_5_e_d_5,
0Xd_8_0_7_a_a_9_8,
0X1_2_8_3_5_b_0_1,
0X2_4_3_1_8_5_b_e,
0X5_5_0_c_7_d_c_3,
0X7_2_b_e_5_d_7_4,
0X8_0_d_e_b_1_f_e,
0X9_b_d_c_0_6_a_7,
0Xc_1_9_b_f_1_7_4,
0Xe_4_9_b_6_9_c_1,
0Xe_f_b_e_4_7_8_6,
0X0_f_c_1_9_d_c_6,
0X2_4_0_c_a_1_c_c,
0X2_d_e_9_2_c_6_f,
0X4_a_7_4_8_4_a_a,
0X5_c_b_0_a_9_d_c,
0X7_6_f_9_8_8_d_a,
0X9_8_3_e_5_1_5_2,
0Xa_8_3_1_c_6_6_d,
0Xb_0_0_3_2_7_c_8,
0Xb_f_5_9_7_f_c_7,
0Xc_6_e_0_0_b_f_3,
0Xd_5_a_7_9_1_4_7,
0X0_6_c_a_6_3_5_1,
0X1_4_2_9_2_9_6_7,
0X2_7_b_7_0_a_8_5,
0X2_e_1_b_2_1_3_8,
0X4_d_2_c_6_d_f_c,
0X5_3_3_8_0_d_1_3,
0X6_5_0_a_7_3_5_4,
0X7_6_6_a_0_a_b_b,
0X8_1_c_2_c_9_2_e,
0X9_2_7_2_2_c_8_5,
0Xa_2_b_f_e_8_a_1,
0Xa_8_1_a_6_6_4_b,
0Xc_2_4_b_8_b_7_0,
0Xc_7_6_c_5_1_a_3,
0Xd_1_9_2_e_8_1_9,
0Xd_6_9_9_0_6_2_4,
0Xf_4_0_e_3_5_8_5,
0X1_0_6_a_a_0_7_0,
0X1_9_a_4_c_1_1_6,
0X1_e_3_7_6_c_0_8,
0X2_7_4_8_7_7_4_c,
0X3_4_b_0_b_c_b_5,
0X3_9_1_c_0_c_b_3,
0X4_e_d_8_a_a_4_a,
0X5_b_9_c_c_a_4_f,
0X6_8_2_e_6_f_f_3,
0X7_4_8_f_8_2_e_e,
0X7_8_a_5_6_3_6_f,
0X8_4_c_8_7_8_1_4,
0X8_c_c_7_0_2_0_8,
0X9_0_b_e_f_f_f_a,
0Xa_4_5_0_6_c_e_b,
0Xb_e_f_9_a_3_f_7,
0Xc_6_7_1_7_8_f_2,
]
_A = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def __A ( __A: bytes ) -> bytes:
_A = b'''\x80''' + (b'''\x00''' * (63 - (len(__A ) + 8) % 64))
_A = struct.pack('''>Q''' , (len(__A ) * 8) )
return data + padding + big_endian_integer
def __A ( self: Dict ) -> None:
# Convert into blocks of 64 bytes
_A = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
_A = list(struct.unpack('''>16L''' , __A ) )
# add 48 0-ed integers
words += [0] * 48
_A ,_A ,_A ,_A ,_A ,_A ,_A ,_A = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
_A = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
_A = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
_A = (
words[index - 16] + sa + words[index - 7] + sa
) % 0X1_0_0_0_0_0_0_0_0
# Compression
_A = self.ror(__A , 6 ) ^ self.ror(__A , 11 ) ^ self.ror(__A , 25 )
_A = (e & f) ^ ((~e & 0Xf_f_f_f_f_f_f_f) & g)
_A = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0_0_0_0_0_0_0_0
_A = self.ror(__A , 2 ) ^ self.ror(__A , 13 ) ^ self.ror(__A , 22 )
_A = (a & b) ^ (a & c) ^ (b & c)
_A = (sa + maj) % 0X1_0_0_0_0_0_0_0_0
_A ,_A ,_A ,_A ,_A ,_A ,_A ,_A = (
g,
f,
e,
((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
c,
b,
a,
((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
)
_A = [a, b, c, d, e, f, g, h]
# Modify final values
_A = [
((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
_A = ''''''.join([hex(__A )[2:].zfill(8 ) for value in self.hashes] )
def __A ( self: Optional[int] , __A: int , __A: int ) -> int:
return 0Xf_f_f_f_f_f_f_f & (value << (32 - rotations)) | (value >> rotations)
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Dict ) -> None:
import hashlib
_A = bytes('''Test String''' , '''utf-8''' )
self.assertEqual(SHAaaa(__A ).hash , hashlib.shaaaa(__A ).hexdigest() )
def __A ( ):
'''simple docstring'''
import doctest
doctest.testmod()
_A = argparse.ArgumentParser()
parser.add_argument(
'''-s''' , '''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument(
'''-f''' , '''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
_A = parser.parse_args()
_A = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
_A = f.read()
else:
_A = bytes(_lowercase , '''utf-8''' )
print(SHAaaa(_lowercase ).hash )
if __name__ == "__main__":
main()
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
import os
from collections.abc import Iterator
def __A ( _lowercase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_A = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_lowercase )[1] in (".py", ".ipynb"):
yield os.path.join(_lowercase , _lowercase ).lstrip('''./''' )
def __A ( _lowercase ):
'''simple docstring'''
return f"""{i * " "}*""" if i else "\n##"
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(_lowercase ) or old_parts[i] != new_part) and new_part:
print(f"""{md_prefix(_lowercase )} {new_part.replace("_" , " " ).title()}""" )
return new_path
def __A ( _lowercase = "." ):
'''simple docstring'''
_A = ''''''
for filepath in sorted(good_file_paths(_lowercase ) ):
_A ,_A = os.path.split(_lowercase )
if filepath != old_path:
_A = print_path(_lowercase , _lowercase )
_A = (filepath.count(os.sep ) + 1) if filepath else 0
_A = f"""{filepath}/{filename}""".replace(''' ''' , '''%20''' )
_A = os.path.splitext(filename.replace('''_''' , ''' ''' ).title() )[0]
print(f"""{md_prefix(_lowercase )} [{filename}]({url})""" )
if __name__ == "__main__":
print_directory_md('.')
| 62 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def __A ( _lowercase ):
'''simple docstring'''
_A = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_A = [1_44, 1_92, 2_40]
_A = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
_A = [96, 1_20, 1_44]
_A = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
_A = [64, 80, 96]
_A = [16, 16, 24, 48, 64, 80, 3_20]
_A = 0.05
_A = 2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
_A = 5_12
_A = 16
_A = 21
_A = '''pascal-voc-id2label.json'''
else:
_A = 10_00
_A = '''imagenet-1k-id2label.json'''
_A = '''huggingface/label-files'''
_A = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='''dataset''' ) , '''r''' ) )
_A = {int(_lowercase ): v for k, v in idalabel.items()}
_A = idalabel
_A = {v: k for k, v in idalabel.items()}
return config
def __A ( _lowercase , _lowercase=False ):
'''simple docstring'''
for i in range(1 , 6 ):
if f"""layer_{i}.""" in name:
_A = name.replace(f"""layer_{i}.""" , f"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
_A = name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
_A = name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
_A = name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
_A = name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
_A = name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
_A = name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
_A = name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
_A = name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
_A = name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if f""".{i}.{j}.""" in name:
_A = name.replace(f""".{i}.{j}.""" , f""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if f""".{i}.{j}.""" in name:
_A = name.replace(f""".{i}.{j}.""" , f""".{i}.""" )
if "expand_1x1" in name:
_A = name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
_A = name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
_A = name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if f""".global_rep.{i}.weight""" in name:
_A = name.replace(f""".global_rep.{i}.weight""" , '''.layernorm.weight''' )
if f""".global_rep.{i}.bias""" in name:
_A = name.replace(f""".global_rep.{i}.bias""" , '''.layernorm.bias''' )
if ".global_rep." in name:
_A = name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
_A = name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
_A = name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
_A = name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
_A = name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
_A = name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
_A = name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
_A = name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
_A = name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
_A = name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
_A = name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
_A = name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
_A = '''mobilevit.''' + name
return name
def __A ( _lowercase , _lowercase , _lowercase=False ):
'''simple docstring'''
if base_model:
_A = ''''''
else:
_A = '''mobilevit.'''
for key in orig_state_dict.copy().keys():
_A = orig_state_dict.pop(_lowercase )
if key[:8] == "encoder.":
_A = key[8:]
if "qkv" in key:
_A = key.split('''.''' )
_A = int(key_split[0][6:] ) - 1
_A = int(key_split[3] )
_A = model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}""" )
_A = layer.transformer.layer[transformer_num].attention.attention.all_head_size
_A = (
f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
_A = val[:dim, :]
_A = val[dim : dim * 2, :]
_A = val[-dim:, :]
else:
_A = val[:dim]
_A = val[dim : dim * 2]
_A = val[-dim:]
else:
_A = val
return orig_state_dict
def __A ( ):
'''simple docstring'''
_A = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_A = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def __A ( _lowercase , _lowercase , _lowercase , _lowercase=False ):
'''simple docstring'''
_A = get_mobilevit_config(_lowercase )
# load original state_dict
_A = torch.load(_lowercase , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
_A = MobileViTForSemanticSegmentation(_lowercase ).eval()
else:
_A = MobileViTForImageClassification(_lowercase ).eval()
_A = convert_state_dict(_lowercase , _lowercase )
model.load_state_dict(_lowercase )
# Check outputs on an image, prepared by MobileViTImageProcessor
_A = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_A = image_processor(images=prepare_img() , return_tensors='''pt''' )
_A = model(**_lowercase )
_A = outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_A = torch.tensor(
[
[[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
[[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
[[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_A = torch.tensor(
[
[[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
[[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
[[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_A = torch.tensor(
[
[[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
[[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
[[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
] )
else:
raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _lowercase , atol=1e-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
_A = torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
elif mobilevit_name == "mobilevit_xs":
_A = torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
elif mobilevit_name == "mobilevit_xxs":
_A = torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
else:
raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _lowercase , atol=1e-4 )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_lowercase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_lowercase )
if push_to_hub:
_A = {
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
_A = model_mapping[mobilevit_name]
image_processor.push_to_hub(_lowercase , organization='''apple''' )
model.push_to_hub(_lowercase , organization='''apple''' )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
__A = None
__A = logging.get_logger(__name__)
__A = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
__A = {
'vocab_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
},
'tokenizer_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
},
}
__A = {
'google/fnet-base': 512,
'google/fnet-large': 512,
}
__A = '▁'
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ["input_ids", "token_type_ids"]
A_ = FNetTokenizer
def __init__( self: Optional[int] , __A: str=None , __A: Optional[Any]=None , __A: Dict=False , __A: Dict=True , __A: Optional[int]=True , __A: Optional[Any]="<unk>" , __A: Any="[SEP]" , __A: Dict="<pad>" , __A: Tuple="[CLS]" , __A: List[str]="[MASK]" , **__A: Any , ) -> Optional[Any]:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_A = (
AddedToken(__A , lstrip=__A , rstrip=__A , normalized=__A )
if isinstance(__A , __A )
else mask_token
)
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , remove_space=__A , keep_accents=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , **__A , )
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = False if not self.vocab_file else True
def __A ( self: int , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [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 __A ( self: List[Any] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self: Dict , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_A = os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ):
copyfile(self.vocab_file , __A )
return (out_vocab_file,)
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: List[Any] , __A: Any ) -> Dict:
_A = parent
def __A ( self: Union[str, Any] ) -> List[Any]:
return {}
def __A ( ):
'''simple docstring'''
_A = '''<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR="FFFFFF">
<HR>
<a href="http://google.com">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style="color:#0000FF">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>'''
_A = '''
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
'''
return [html_string_a, html_string_a]
@require_bsa
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = MarkupLMFeatureExtractor if is_bsa_available() else None
def __A ( self: Optional[int] ) -> int:
_A = MarkupLMFeatureExtractionTester(self )
@property
def __A ( self: Optional[Any] ) -> int:
return self.feature_extract_tester.prepare_feat_extract_dict()
def __A ( self: List[Any] ) -> Optional[int]:
# Initialize feature_extractor
_A = self.feature_extraction_class()
# Test not batched input
_A = get_html_strings()[0]
_A = feature_extractor(__A )
# fmt: off
_A = [['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
_A = [['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
# fmt: on
self.assertEqual(encoding.nodes , __A )
self.assertEqual(encoding.xpaths , __A )
# Test batched
_A = get_html_strings()
_A = feature_extractor(__A )
# fmt: off
_A = expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
_A = expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , __A )
self.assertEqual(encoding.xpaths , __A )
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
import enum
import shutil
import sys
__A , __A = shutil.get_terminal_size()
__A = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class SCREAMING_SNAKE_CASE ( enum.Enum ):
"""simple docstring"""
A_ = 0
A_ = 1
def __A ( _lowercase , _lowercase="" ):
'''simple docstring'''
sys.stdout.write(str(_lowercase ) + end )
sys.stdout.flush()
def __A ( _lowercase , _lowercase , _lowercase="" ):
'''simple docstring'''
forceWrite(f"""\u001b[{color}m{content}\u001b[0m""" , _lowercase )
def __A ( ):
'''simple docstring'''
forceWrite('''\r''' )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def __A ( ):
'''simple docstring'''
forceWrite(''' ''' * TERMINAL_WIDTH )
reset_cursor()
def __A ( ):
'''simple docstring'''
reset_cursor()
forceWrite('''-''' * TERMINAL_WIDTH )
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from __future__ import annotations
from math import pi
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = KandinskyVaaPipeline
A_ = [
"image_embeds",
"negative_image_embeds",
]
A_ = ["image_embeds", "negative_image_embeds"]
A_ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
A_ = False
@property
def __A ( self: Any ) -> Dict:
return 32
@property
def __A ( self: Optional[Any] ) -> List[Any]:
return 32
@property
def __A ( self: List[str] ) -> List[Any]:
return self.time_input_dim
@property
def __A ( self: str ) -> List[str]:
return self.time_input_dim * 4
@property
def __A ( self: Tuple ) -> Optional[Any]:
return 1_00
@property
def __A ( self: Union[str, Any] ) -> str:
torch.manual_seed(0 )
_A = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_A = UNetaDConditionModel(**__A )
return model
@property
def __A ( self: Optional[Any] ) -> Optional[int]:
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 __A ( self: Optional[Any] ) -> str:
torch.manual_seed(0 )
_A = VQModel(**self.dummy_movq_kwargs )
return model
def __A ( self: Optional[int] ) -> List[str]:
_A = self.dummy_unet
_A = self.dummy_movq
_A = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.00_085 , beta_end=0.012 , clip_sample=__A , set_alpha_to_one=__A , steps_offset=1 , prediction_type='''epsilon''' , thresholding=__A , )
_A = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __A ( self: Optional[Any] , __A: Optional[int] , __A: Tuple=0 ) -> List[str]:
_A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__A ) ).to(__A )
_A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__A )
if str(__A ).startswith('''mps''' ):
_A = torch.manual_seed(__A )
else:
_A = torch.Generator(device=__A ).manual_seed(__A )
_A = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def __A ( self: int ) -> Any:
_A = '''cpu'''
_A = self.get_dummy_components()
_A = self.pipeline_class(**__A )
_A = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_A = pipe(**self.get_dummy_inputs(__A ) )
_A = output.images
_A = pipe(
**self.get_dummy_inputs(__A ) , return_dict=__A , )[0]
_A = image[0, -3:, -3:, -1]
_A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_A = np.array(
[0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Optional[int] ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self: List[Any] ) -> int:
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
_A = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__A )
_A = KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
_A = pipeline.to(__A )
pipeline.set_progress_bar_config(disable=__A )
_A = '''red cat, 4k photo'''
_A = torch.Generator(device='''cuda''' ).manual_seed(0 )
_A ,_A = pipe_prior(
__A , generator=__A , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_A = torch.Generator(device='''cuda''' ).manual_seed(0 )
_A = pipeline(
image_embeds=__A , negative_image_embeds=__A , generator=__A , num_inference_steps=1_00 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__A , __A )
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
__A = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__A = ['a', 'b', 'c', 'd', 'e']
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = start
# add current to visited
visited.append(_lowercase )
_A = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
_A = topological_sort(_lowercase , _lowercase , _lowercase )
# if all neighbors visited add current to sort
sort.append(_lowercase )
# if all vertices haven't been visited select a new one to visit
if len(_lowercase ) != len(_lowercase ):
for vertice in vertices:
if vertice not in visited:
_A = topological_sort(_lowercase , _lowercase , _lowercase )
# return sort
return sort
if __name__ == "__main__":
__A = topological_sort('a', [], [])
print(sort)
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self: str ) -> Any:
_A = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__A ).to(__A )
_A = AutoTokenizer.from_pretrained('''google/mt5-small''' )
_A = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
_A = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
_A = model(input_ids.to(__A ) , labels=labels.to(__A ) ).loss
_A = -(labels.shape[-1] * loss.item())
_A = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 62 |
import math
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 SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Dict , __A: Union[str, Any] , __A: Tuple=13 , __A: List[Any]=7 , __A: List[Any]=True , __A: int=True , __A: Any=True , __A: Union[str, Any]=True , __A: Dict=99 , __A: Union[str, Any]=16 , __A: Any=36 , __A: List[Any]=6 , __A: List[Any]=6 , __A: Tuple=6 , __A: List[str]=37 , __A: List[Any]="gelu" , __A: List[str]=0.1 , __A: Tuple=0.1 , __A: Union[str, Any]=5_12 , __A: int=16 , __A: int=2 , __A: Optional[Any]=0.02 , __A: List[Any]=3 , __A: Dict=4 , __A: Dict=None , ) -> Dict:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = embedding_size
_A = hidden_size
_A = num_hidden_layers
_A = num_hidden_groups
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
def __A ( self: int ) -> str:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self: Optional[int] ) -> int:
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def __A ( self: int , __A: Optional[int] , __A: str , __A: int , __A: Optional[int] , __A: Dict , __A: Optional[Any] , __A: Optional[Any] ) -> Dict:
_A = AlbertModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A )
_A = model(__A , token_type_ids=__A )
_A = model(__A )
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 __A ( self: Optional[Any] , __A: str , __A: Union[str, Any] , __A: Union[str, Any] , __A: str , __A: Union[str, Any] , __A: str , __A: Dict ) -> str:
_A = AlbertForPreTraining(config=__A )
model.to(__A )
model.eval()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , sentence_order_label=__A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def __A ( self: Optional[Any] , __A: Dict , __A: List[Any] , __A: List[str] , __A: Dict , __A: int , __A: Optional[int] , __A: Dict ) -> Union[str, Any]:
_A = AlbertForMaskedLM(config=__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: str , __A: int , __A: Any , __A: Optional[int] , __A: Union[str, Any] , __A: Dict , __A: int , __A: int ) -> Dict:
_A = AlbertForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , start_positions=__A , end_positions=__A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __A ( self: Any , __A: Any , __A: int , __A: Optional[Any] , __A: List[str] , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> Union[str, Any]:
_A = self.num_labels
_A = AlbertForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self: List[Any] , __A: List[Any] , __A: int , __A: Union[str, Any] , __A: List[str] , __A: Tuple , __A: List[str] , __A: List[Any] ) -> Union[str, Any]:
_A = self.num_labels
_A = AlbertForTokenClassification(config=__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self: int , __A: Any , __A: Optional[Any] , __A: str , __A: List[str] , __A: List[Any] , __A: Any , __A: List[Any] ) -> Tuple:
_A = self.num_choices
_A = AlbertForMultipleChoice(config=__A )
model.to(__A )
model.eval()
_A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self: List[str] ) -> str:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": AlbertModel,
"fill-mask": AlbertForMaskedLM,
"question-answering": AlbertForQuestionAnswering,
"text-classification": AlbertForSequenceClassification,
"token-classification": AlbertForTokenClassification,
"zero-shot": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
def __A ( self: str , __A: Any , __A: int , __A: int=False ) -> int:
_A = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class in get_values(__A ):
_A = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__A )
_A = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def __A ( self: Any ) -> List[Any]:
_A = AlbertModelTester(self )
_A = ConfigTester(self , config_class=__A , hidden_size=37 )
def __A ( self: Tuple ) -> int:
self.config_tester.run_common_tests()
def __A ( self: List[Any] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __A ( self: Union[str, Any] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __A ( self: Any ) -> Tuple:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__A )
def __A ( self: Dict ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__A )
def __A ( self: Optional[Any] ) -> Dict:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__A )
def __A ( self: List[Any] ) -> Tuple:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__A )
def __A ( self: Union[str, Any] ) -> str:
_A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_A = type
self.model_tester.create_and_check_model(*__A )
@slow
def __A ( self: Optional[int] ) -> Union[str, Any]:
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = AlbertModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self: Union[str, Any] ) -> Optional[Any]:
_A = AlbertModel.from_pretrained('''albert-base-v2''' )
_A = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
_A = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_A = model(__A , attention_mask=__A )[0]
_A = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , __A )
_A = torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __A , atol=1e-4 ) )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.14.0', 'To fix: pip install -r examples/pytorch/audio-classification/requirements.txt')
def __A ( _lowercase , _lowercase , _lowercase = 1_60_00 ):
'''simple docstring'''
_A = int(round(sample_rate * max_length ) )
if len(_lowercase ) <= sample_length:
return wav
_A = randint(0 , len(_lowercase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = field(default=snake_case , metadata={"help": "Name of a dataset from the datasets package"} )
A_ = field(
default=snake_case , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
A_ = field(
default=snake_case , metadata={"help": "A file containing the training audio paths and labels."} )
A_ = field(
default=snake_case , metadata={"help": "A file containing the validation audio paths and labels."} )
A_ = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
A_ = field(
default="validation" , metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
A_ = field(
default="audio" , metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"} , )
A_ = field(
default="label" , metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
A_ = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
A_ = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
A_ = field(
default=20 , metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = field(
default="facebook/wav2vec2-base" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
A_ = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
A_ = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
A_ = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
A_ = field(
default=snake_case , metadata={"help": "Name or path of preprocessor config."} )
A_ = field(
default=snake_case , metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
A_ = field(
default=snake_case , metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
A_ = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
A_ = field(
default=snake_case , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
A_ = field(
default=snake_case , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def __A ( self: Optional[Any] ) -> List[Any]:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __A , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def __A ( ):
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A ,_A ,_A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A ,_A ,_A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' , _lowercase , _lowercase )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_lowercase )
transformers.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
_A = DatasetDict()
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f"""{", ".join(raw_datasets["train"].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f"""{", ".join(raw_datasets["train"].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_A = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_A = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_A = feature_extractor.model_input_names[0]
def train_transforms(_lowercase ):
_A = []
for audio in batch[data_args.audio_column_name]:
_A = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_lowercase )
_A = feature_extractor(_lowercase , sampling_rate=feature_extractor.sampling_rate )
_A = {model_input_name: inputs.get(_lowercase )}
_A = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_lowercase ):
_A = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_A = feature_extractor(_lowercase , sampling_rate=feature_extractor.sampling_rate )
_A = {model_input_name: inputs.get(_lowercase )}
_A = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_A = raw_datasets['''train'''].features[data_args.label_column_name].names
_A ,_A = {}, {}
for i, label in enumerate(_lowercase ):
_A = str(_lowercase )
_A = label
# Load the accuracy metric from the datasets package
_A = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_lowercase ):
_A = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_lowercase , references=eval_pred.label_ids )
_A = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_lowercase ) , labelaid=_lowercase , idalabel=_lowercase , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_A = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_A = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_lowercase , output_all_columns=_lowercase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_A = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_lowercase , output_all_columns=_lowercase )
# Initialize our trainer
_A = Trainer(
model=_lowercase , args=_lowercase , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_lowercase , tokenizer=_lowercase , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_lowercase )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('''eval''' , _lowercase )
trainer.save_metrics('''eval''' , _lowercase )
# Write model card and (optionally) push to hub
_A = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowercase )
else:
trainer.create_model_card(**_lowercase )
if __name__ == "__main__":
main()
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__A = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "maskformer"
A_ = {"hidden_size": "mask_feature_size"}
A_ = ["resnet", "swin"]
A_ = ["detr"]
def __init__( self: int , __A: int = 2_56 , __A: int = 2_56 , __A: float = 0.1 , __A: bool = False , __A: Optional[Dict] = None , __A: Optional[Dict] = None , __A: float = 0.02 , __A: float = 1.0 , __A: float = 1.0 , __A: float = 1.0 , __A: float = 20.0 , __A: Optional[bool] = None , **__A: Optional[Any] , ) -> List[Any]:
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
_A = SwinConfig(
image_size=3_84 , in_channels=3 , patch_size=4 , embed_dim=1_28 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(__A , __A ):
_A = backbone_config.pop('''model_type''' )
_A = CONFIG_MAPPING[backbone_model_type]
_A = config_class.from_dict(__A )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
_A = DetrConfig()
else:
# verify that the decoder is supported
_A = (
decoder_config.pop('''model_type''' ) if isinstance(__A , __A ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(__A , __A ):
_A = CONFIG_MAPPING[decoder_type]
_A = config_class.from_dict(__A )
_A = backbone_config
_A = decoder_config
# main feature dimension for the model
_A = fpn_feature_size
_A = mask_feature_size
# initializer
_A = init_std
_A = init_xavier_std
# Hungarian matcher && loss
_A = cross_entropy_weight
_A = dice_weight
_A = mask_weight
_A = use_auxiliary_loss
_A = no_object_weight
_A = output_auxiliary_logits
_A = self.decoder_config.encoder_attention_heads
_A = self.decoder_config.num_hidden_layers
super().__init__(**__A )
@classmethod
def __A ( cls: Dict , __A: PretrainedConfig , __A: PretrainedConfig , **__A: Any ) -> int:
return cls(
backbone_config=__A , decoder_config=__A , **__A , )
def __A ( self: int ) -> Dict[str, any]:
_A = copy.deepcopy(self.__dict__ )
_A = self.backbone_config.to_dict()
_A = self.decoder_config.to_dict()
_A = self.__class__.model_type
return output
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import baseaa
def __A ( _lowercase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def __A ( _lowercase ):
'''simple docstring'''
return baseaa.aaadecode(_lowercase ).decode('''utf-8''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = AutoConfig.from_pretrained(_lowercase )
_A = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase )
_A = checkpoints.load_tax_checkpoint(_lowercase )
_A = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
_A = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
_A = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_A = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
_A = f"""layers_{str(_lowercase )}"""
# Self-Attention
_A = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
_A = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
_A = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
_A = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_A = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
_A = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
_A = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
_A = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
_A = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
_A = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
_A = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
_A = flax_model.params['''encoder''']['''block'''][str(_lowercase )]['''layer''']
_A = tax_attention_key
_A = tax_attention_out
_A = tax_attention_query
_A = tax_attention_value
_A = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_A = tax_global_layer_norm
if split_mlp_wi:
_A = tax_mlp_wi_a
_A = tax_mlp_wi_a
else:
_A = tax_mlp_wi
_A = tax_mlp_wo
_A = tax_mlp_layer_norm
_A = flax_model_encoder_layer_block
# Only for layer 0:
_A = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
_A = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_A = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
_A = tax_encoder_global_rel_embedding
# Assigning
_A = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
_A = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
_A = f"""layers_{str(_lowercase )}"""
# Self-Attention
_A = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
_A = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
_A = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
_A = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
_A = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
_A = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
_A = tax_enc_dec_attention_module['''key''']['''kernel''']
_A = tax_enc_dec_attention_module['''out''']['''kernel''']
_A = tax_enc_dec_attention_module['''query''']['''kernel''']
_A = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
_A = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
_A = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
_A = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
_A = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
_A = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
_A = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
_A = flax_model.params['''decoder''']['''block'''][str(_lowercase )]['''layer''']
_A = tax_attention_key
_A = tax_attention_out
_A = tax_attention_query
_A = tax_attention_value
_A = tax_pre_attention_layer_norm
_A = tax_enc_dec_attention_key
_A = tax_enc_dec_attention_out
_A = tax_enc_dec_attention_query
_A = tax_enc_dec_attention_value
_A = tax_cross_layer_norm
if split_mlp_wi:
_A = tax_mlp_wi_a
_A = tax_mlp_wi_a
else:
_A = tax_mlp_wi
_A = tax_mlp_wo
_A = txa_mlp_layer_norm
_A = flax_model_decoder_layer_block
# Decoder Normalization
_A = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
_A = txa_decoder_norm
# Only for layer 0:
_A = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
_A = tax_decoder_rel_embedding
# Token Embeddings
_A = tax_model['''target''']['''token_embedder''']['''embedding''']
_A = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
_A = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(_lowercase )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
)
parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
parser.add_argument(
'--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
)
__A = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_A = ParquetDatasetReader(_lowercase , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_parquet_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = features.copy() if features else default_expected_features
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = ParquetDatasetReader(_lowercase , features=_lowercase , cache_dir=_lowercase ).read()
_check_parquet_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = ParquetDatasetReader(_lowercase , cache_dir=_lowercase , split=_lowercase ).read()
_check_parquet_dataset(_lowercase , _lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if issubclass(_lowercase , _lowercase ):
_A = parquet_path
elif issubclass(_lowercase , _lowercase ):
_A = [parquet_path]
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = ParquetDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_parquet_dataset(_lowercase , _lowercase )
def __A ( _lowercase , _lowercase , _lowercase=("train",) ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase )
for split in splits:
_A = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_A = ParquetDatasetReader(
{'''train''': parquet_path} , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_parquet_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = features.copy() if features else default_expected_features
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = ParquetDatasetReader({'''train''': parquet_path} , features=_lowercase , cache_dir=_lowercase ).read()
_check_parquet_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if split:
_A = {split: parquet_path}
else:
_A = '''train'''
_A = {'''train''': parquet_path, '''test''': parquet_path}
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = ParquetDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_parquet_datasetdict(_lowercase , _lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = ParquetDatasetWriter(_lowercase , tmp_path / '''foo.parquet''' )
assert writer.write() > 0
_A = pq.ParquetFile(tmp_path / '''foo.parquet''' )
_A = pf.read()
assert dataset.data.table == output_table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = str(shared_datadir / '''test_image_rgb.jpg''' )
_A = {'''image''': [image_path]}
_A = Features({'''image''': Image()} )
_A = Dataset.from_dict(_lowercase , features=_lowercase )
_A = ParquetDatasetWriter(_lowercase , tmp_path / '''foo.parquet''' )
assert writer.write() > 0
_A = Dataset.from_parquet(str(tmp_path / '''foo.parquet''' ) )
assert dataset.features == reloaded_dataset.features
_A = ParquetDatasetReader(str(tmp_path / '''foo.parquet''' ) , streaming=_lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
'''feature, expected''' , [
(Features({'''foo''': Value('''int32''' )} ), None),
(Features({'''image''': Image(), '''foo''': Value('''int32''' )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({'''nested''': Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert get_writer_batch_size(_lowercase ) == expected
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 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 SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "marian"
A_ = ["past_key_values"]
A_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self: Union[str, Any] , __A: Any=5_81_01 , __A: Optional[Any]=None , __A: List[str]=10_24 , __A: Any=12 , __A: Tuple=40_96 , __A: Union[str, Any]=16 , __A: Tuple=12 , __A: Optional[int]=40_96 , __A: List[Any]=16 , __A: Optional[Any]=0.0 , __A: Optional[Any]=0.0 , __A: Optional[int]=True , __A: str=True , __A: Tuple="gelu" , __A: str=10_24 , __A: str=0.1 , __A: Dict=0.0 , __A: List[str]=0.0 , __A: List[Any]=0.02 , __A: int=5_81_00 , __A: Tuple=False , __A: Optional[int]=5_81_00 , __A: int=0 , __A: Optional[int]=0 , __A: Optional[int]=True , **__A: Dict , ) -> Dict:
_A = vocab_size
_A = decoder_vocab_size or vocab_size
_A = max_position_embeddings
_A = d_model
_A = encoder_ffn_dim
_A = encoder_layers
_A = encoder_attention_heads
_A = decoder_ffn_dim
_A = decoder_layers
_A = decoder_attention_heads
_A = dropout
_A = attention_dropout
_A = activation_dropout
_A = activation_function
_A = init_std
_A = encoder_layerdrop
_A = decoder_layerdrop
_A = use_cache
_A = encoder_layers
_A = scale_embedding # scale factor will be sqrt(d_model) if True
_A = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=__A , eos_token_id=__A , is_encoder_decoder=__A , decoder_start_token_id=__A , forced_eos_token_id=__A , **__A , )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def __A ( self: Any ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_A = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_A = {0: '''batch'''}
_A = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_A = {0: '''batch''', 1: '''decoder_sequence'''}
_A = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__A , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
_A = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_A ,_A = self.num_layers
for i in range(__A ):
_A = {0: '''batch''', 2: '''past_sequence + sequence'''}
_A = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
_A = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def __A ( self: int ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_A = super().outputs
else:
_A = super(__A , self ).outputs
if self.use_past:
_A ,_A = self.num_layers
for i in range(__A ):
_A = {0: '''batch''', 2: '''past_sequence + sequence'''}
_A = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __A ( self: Optional[Any] , __A: PreTrainedTokenizer , __A: int = -1 , __A: int = -1 , __A: bool = False , __A: Optional[TensorType] = None , ) -> Mapping[str, Any]:
_A = self._generate_dummy_inputs_for_encoder_and_decoder(
__A , __A , __A , __A , __A )
# Generate decoder inputs
_A = seq_length if not self.use_past else 1
_A = self._generate_dummy_inputs_for_encoder_and_decoder(
__A , __A , __A , __A , __A )
_A = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
_A = dict(**__A , **__A )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_A ,_A = common_inputs['''input_ids'''].shape
_A = common_inputs['''decoder_input_ids'''].shape[1]
_A ,_A = self.num_attention_heads
_A = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_A = decoder_seq_length + 3
_A = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_A = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__A , __A )] , dim=1 )
_A = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_A ,_A = self.num_layers
_A = min(__A , __A )
_A = max(__A , __A ) - min_num_layers
_A = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__A ):
common_inputs["past_key_values"].append(
(
torch.zeros(__A ),
torch.zeros(__A ),
torch.zeros(__A ),
torch.zeros(__A ),
) )
# TODO: test this.
_A = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__A , __A ):
common_inputs["past_key_values"].append((torch.zeros(__A ), torch.zeros(__A )) )
return common_inputs
def __A ( self: List[str] , __A: PreTrainedTokenizer , __A: int = -1 , __A: int = -1 , __A: bool = False , __A: Optional[TensorType] = None , ) -> Mapping[str, Any]:
_A = self._generate_dummy_inputs_for_encoder_and_decoder(
__A , __A , __A , __A , __A )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_A ,_A = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_A = seqlen + 2
_A ,_A = self.num_layers
_A ,_A = self.num_attention_heads
_A = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_A = common_inputs['''attention_mask'''].dtype
_A = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__A , __A , dtype=__A )] , dim=1 )
_A = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(__A )
]
return common_inputs
def __A ( self: int , __A: PreTrainedTokenizer , __A: int = -1 , __A: int = -1 , __A: bool = False , __A: Optional[TensorType] = None , ) -> Mapping[str, Any]:
# 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
_A = compute_effective_axis_dimension(
__A , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_A = tokenizer.num_special_tokens_to_add(__A )
_A = compute_effective_axis_dimension(
__A , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__A )
# Generate dummy inputs according to compute batch and sequence
_A = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
_A = dict(tokenizer(__A , return_tensors=__A ) )
return common_inputs
def __A ( self: Union[str, Any] , __A: PreTrainedTokenizer , __A: int = -1 , __A: int = -1 , __A: bool = False , __A: Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_A = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__A , batch_size=__A , seq_length=__A , is_pair=__A , framework=__A )
else:
_A = self._generate_dummy_inputs_for_causal_lm(
__A , batch_size=__A , seq_length=__A , is_pair=__A , framework=__A )
return common_inputs
def __A ( self: Dict , __A: Any , __A: Any , __A: Tuple , __A: Dict ) -> Union[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_A = super()._flatten_past_key_values_(__A , __A , __A , __A )
else:
_A = super(__A , self )._flatten_past_key_values_(
__A , __A , __A , __A )
@property
def __A ( self: Any ) -> float:
return 1e-4
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
from __future__ import annotations
__A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
_A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
] # the reference grid
_A = 1
_A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
] # the action grid
_A = init[0]
_A = init[1]
_A = 0
_A = g + heuristic[x][y] # cost from starting cell to destination cell
_A = [[f, g, x, y]]
_A = False # flag that is set when search is complete
_A = False # flag set if we can't find expand
while not found and not resign:
if len(_lowercase ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
_A = cell.pop()
_A = next_cell[2]
_A = next_cell[3]
_A = next_cell[1]
if x == goal[0] and y == goal[1]:
_A = True
else:
for i in range(len(_lowercase ) ): # to try out different valid actions
_A = x + DIRECTIONS[i][0]
_A = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_lowercase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
_A = g + cost
_A = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
_A = 1
_A = i
_A = []
_A = goal[0]
_A = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
_A = x - DIRECTIONS[action[x][y]][0]
_A = y - DIRECTIONS[action[x][y]][1]
_A = xa
_A = ya
invpath.append([x, y] )
_A = []
for i in range(len(_lowercase ) ):
path.append(invpath[len(_lowercase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__A = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__A = [0, 0]
# all coordinates are given in format [y,x]
__A = [len(grid) - 1, len(grid[0]) - 1]
__A = 1
# the cost map which pushes the path closer to the goal
__A = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__A = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__A = 99
__A , __A = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 62 |
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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = None
A_ = None
A_ = None
A_ = None
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Any , __A: int=1 , __A: Any=0 , __A: Dict=2 , __A: Dict=5_12 , __A: Union[str, Any]="cls" , __A: str=False , __A: int=True , **__A: List[str] , ) -> Optional[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = project_dim
_A = pooler_fn
_A = learn_encoder
_A = use_attention_mask
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [R"pooler", R"logit_scale"]
A_ = [R"position_ids", R"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self: Dict , __A: Optional[Any] ) -> Any:
super().__init__(__A )
_A = XLMRobertaModel(__A )
_A = nn.Linear(config.hidden_size , config.project_dim )
_A = getattr(__A , '''has_pre_transformation''' , __A )
if self.has_pre_transformation:
_A = nn.Linear(config.hidden_size , config.project_dim )
_A = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __A ( self: Any , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , ) -> Tuple:
_A = return_dict if return_dict is not None else self.config.use_return_dict
_A = self.base_model(
input_ids=__A , attention_mask=__A , token_type_ids=__A , position_ids=__A , head_mask=__A , inputs_embeds=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , output_attentions=__A , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__A , )
if self.has_pre_transformation:
_A = outputs['''hidden_states'''][-2]
_A = self.pre_LN(__A )
_A = self.transformation_pre(__A )
return TransformationModelOutput(
projection_state=__A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
_A = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if any(not isinstance(_lowercase , _lowercase ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(_lowercase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_lowercase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A = logging.getLogger(__name__)
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser(
description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
parser.add_argument(
'''--dataset_name''' , type=_lowercase , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , )
parser.add_argument(
'''--dataset_config''' , type=_lowercase , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' )
parser.add_argument(
'''--tokenizer_name_or_path''' , type=_lowercase , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , )
parser.add_argument(
'''--shard_size''' , type=_lowercase , default=10_00 , help='''Number of entries to go in a single shard.''' , )
parser.add_argument('''--split''' , type=_lowercase , default='''train''' , choices=['''train''', '''test''', '''validation'''] )
parser.add_argument(
'''--limit''' , default=_lowercase , type=_lowercase , help='''Limit the number of shards (used for debugging).''' , )
parser.add_argument(
'''--max_length''' , type=_lowercase , default=5_12 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
''' sequence length that is a multiple of 8.''' , )
parser.add_argument(
'''--output_dir''' , default='''tf-tpu''' , type=_lowercase , help='''Output directory where the TFRecord shards will be saved. If the'''
''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
''' shards will be directly saved to a Google Cloud Storage bucket.''' , )
_A = parser.parse_args()
return args
def __A ( _lowercase ):
'''simple docstring'''
def fn(_lowercase ):
return tokenizer(examples['''text'''] )
return fn
def __A ( _lowercase ):
'''simple docstring'''
_A = []
for i in range(len(tokenized_data['''input_ids'''] ) ):
_A = {
'''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
'''attention_mask''': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
}
_A = tf.train.Features(feature=_lowercase )
_A = tf.train.Example(features=_lowercase )
_A = example.SerializeToString()
records.append(_lowercase )
return records
def __A ( _lowercase ):
'''simple docstring'''
_A = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
_A = min(len(_lowercase ) , args.limit )
_A = dataset.select(range(_lowercase ) )
print(f"""Limiting the dataset to {args.limit} entries.""" )
_A = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
_A = os.path.join(args.output_dir , args.split )
if not os.path.exists(_lowercase ):
os.makedirs(_lowercase )
else:
_A = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
_A = tokenize_function(_lowercase )
_A = dataset.map(_lowercase , batched=_lowercase , num_proc=4 , remove_columns=['''text'''] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(_lowercase ):
# Concatenate all texts.
_A = {k: sum(examples[k] , [] ) for k in examples.keys()}
_A = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
_A = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
_A = {
k: [t[i : i + args.max_length] for i in range(0 , _lowercase , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
_A = dataset_tokenized.map(_lowercase , batched=_lowercase , batch_size=10_00 , num_proc=4 )
_A = 0
_A = 0
for shard in range(0 , len(_lowercase ) , args.shard_size ):
_A = grouped_dataset[shard : shard + args.shard_size]
_A = len(dataset_snapshot['''input_ids'''] )
_A = os.path.join(_lowercase , f"""dataset-{shard_count}-{records_containing}.tfrecord""" )
_A = get_serialized_examples(_lowercase )
with tf.io.TFRecordWriter(_lowercase ) as out_file:
for i in range(len(_lowercase ) ):
_A = serialized_examples[i]
out_file.write(_lowercase )
print('''Wrote file {} containing {} records'''.format(_lowercase , _lowercase ) )
shard_count += 1
total_records += records_containing
with open(f"""split-{args.split}-records-count.txt""" , '''w''' ) as f:
print(f"""Total {args.split} records: {total_records}""" , file=_lowercase )
if __name__ == "__main__":
__A = parse_args()
main(args)
| 62 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Union[str, Any] , __A: int , __A: int ) -> Optional[int]:
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
_A = img
_A = img.shape[1]
_A = img.shape[0]
_A = dst_width
_A = dst_height
_A = self.src_w / self.dst_w
_A = self.src_h / self.dst_h
_A = _A = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def __A ( self: Optional[int] ) -> List[str]:
for i in range(self.dst_h ):
for j in range(self.dst_w ):
_A = self.img[self.get_y(__A )][self.get_x(__A )]
def __A ( self: List[str] , __A: int ) -> int:
return int(self.ratio_x * x )
def __A ( self: Dict , __A: int ) -> int:
return int(self.ratio_y * y )
if __name__ == "__main__":
__A , __A = 800, 600
__A = imread('image_data/lena.jpg', 1)
__A = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__A = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def __A ( _lowercase=True ):
'''simple docstring'''
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case ) )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = None
A_ = None
def __A ( self: Union[str, Any] , __A: Dict , __A: Any ) -> Any:
with TemporaryDirectory() as tmp_dir:
_A = dataset_module_factory(__A , cache_dir=__A )
_A = import_main_class(dataset_module.module_path , dataset=__A )
_A = builder_cls(
cache_dir=__A , config_name=__A , hash=dataset_module.hash , )
_A = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=__A ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
_A = cached_path(__A , cache_dir=__A )
self.assertTrue(os.path.exists(__A ) )
@pytest.mark.integration
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
_A = dataset_module_factory('''wikipedia''' , cache_dir=_lowercase )
_A = import_main_class(dataset_module.module_path )
_A = builder_cls(
cache_dir=_lowercase , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_A = None
builder_instance.download_and_prepare()
_A = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __A ( _lowercase ):
'''simple docstring'''
_A = dataset_module_factory('''wikipedia''' , cache_dir=_lowercase )
_A = import_main_class(dataset_module.module_path , dataset=_lowercase )
_A = builder_cls(
cache_dir=_lowercase , config_name='''20220301.frr''' , hash=dataset_module.hash , )
_A = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowercase , _lowercase )
assert "train" in ds
assert isinstance(ds['''train'''] , _lowercase )
assert next(iter(ds['''train'''] ) )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
import argparse
import os
import re
import packaging.version
__A = 'examples/'
__A = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__A = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
__A = 'README.md'
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
with open(_lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.read()
_A ,_A = REPLACE_PATTERNS[pattern]
_A = replace.replace('''VERSION''' , _lowercase )
_A = re_pattern.sub(_lowercase , _lowercase )
with open(_lowercase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
for folder, directories, fnames in os.walk(_lowercase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(_lowercase , _lowercase ) , _lowercase , pattern='''examples''' )
def __A ( _lowercase , _lowercase=False ):
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_lowercase , _lowercase , _lowercase )
if not patch:
update_version_in_examples(_lowercase )
def __A ( ):
'''simple docstring'''
_A = '''🤗 Transformers currently provides the following architectures'''
_A = '''1. Want to contribute a new model?'''
with open(_lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.readlines()
# Find the start of the list.
_A = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_A = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
_A = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(_lowercase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(_lowercase )
def __A ( ):
'''simple docstring'''
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
_A = f.read()
_A = REPLACE_PATTERNS['''init'''][0].search(_lowercase ).groups()[0]
return packaging.version.parse(_lowercase )
def __A ( _lowercase=False ):
'''simple docstring'''
_A = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
_A = default_version.base_version
elif patch:
_A = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
_A = f"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
_A = input(f"""Which version are you releasing? [{default_version}]""" )
if len(_lowercase ) == 0:
_A = default_version
print(f"""Updating version to {version}.""" )
global_version_update(_lowercase , patch=_lowercase )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def __A ( ):
'''simple docstring'''
_A = get_version()
_A = f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
_A = current_version.base_version
# Check with the user we got that right.
_A = input(f"""Which version are we developing now? [{dev_version}]""" )
if len(_lowercase ) == 0:
_A = dev_version
print(f"""Updating version to {version}.""" )
global_version_update(_lowercase )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
__A = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
__A = logging.getLogger()
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
parser.add_argument('''-f''' )
_A = parser.parse_args()
return args.f
def __A ( _lowercase ):
'''simple docstring'''
_A = {}
_A = os.path.join(_lowercase , '''all_results.json''' )
if os.path.exists(_lowercase ):
with open(_lowercase , '''r''' ) as f:
_A = json.load(_lowercase )
else:
raise ValueError(f"""can't find {path}""" )
return results
def __A ( ):
'''simple docstring'''
_A = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
__A = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@classmethod
def __A ( cls: List[Any] ) -> List[Any]:
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
_A = tempfile.mkdtemp()
_A = os.path.join(cls.tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
_A = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __A ( cls: List[str] ) -> Optional[Any]:
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: Optional[int] ) -> List[Any]:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: int ) -> List[str]:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertLess(result['''perplexity'''] , 1_00 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: int ) -> Union[str, Any]:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertLess(result['''perplexity'''] , 42 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: Optional[int] ) -> List[str]:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
_A = 7 if get_gpu_count() > 1 else 2
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertLess(result['''train_loss'''] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: Tuple ) -> int:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''] , 28 )
self.assertGreaterEqual(result['''eval_exact'''] , 28 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: List[str] ) -> Tuple:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__A , '''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: List[Any] ) -> Optional[Any]:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_rouge1'''] , 10 )
self.assertGreaterEqual(result['''eval_rouge2'''] , 2 )
self.assertGreaterEqual(result['''eval_rougeL'''] , 7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''] , 7 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: Any ) -> Union[str, Any]:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_bleu'''] , 30 )
self.assertTrue(os.path.exists(os.path.join(__A , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''translation_no_trainer''' ) ) )
@slow
def __A ( self: Union[str, Any] ) -> Tuple:
_A = logging.StreamHandler(sys.stdout )
logger.addHandler(__A )
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
_A = get_results(__A )
self.assertGreaterEqual(result['''eval_overall_accuracy'''] , 0.10 )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: str ) -> Dict:
_A = self.get_auto_remove_tmp_dir()
_A = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
_A = get_results(__A )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__A , '''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__A , '''image_classification_no_trainer''' ) ) )
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from __future__ import annotations
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if len(_lowercase ) <= 1 or n <= 1:
return
insert_next(_lowercase , n - 1 )
rec_insertion_sort(_lowercase , n - 1 )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if index >= len(_lowercase ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
_A ,_A = (
collection[index],
collection[index - 1],
)
insert_next(_lowercase , index + 1 )
if __name__ == "__main__":
__A = input('Enter integers separated by spaces: ')
__A = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = len(_lowercase )
print('''The following activities are selected:''' )
# The first activity is always selected
_A = 0
print(_lowercase , end=''',''' )
# Consider rest of the activities
for j in range(_lowercase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(_lowercase , end=''',''' )
_A = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = [1, 3, 0, 5, 8, 5]
__A = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__A = False
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: str , __A: Optional[Any]=32 ) -> int:
set_seed(0 )
_A = UNetaDModel(sample_size=__A , in_channels=3 , out_channels=3 )
_A = torch.optim.SGD(model.parameters() , lr=0.0_001 )
return model, optimizer
@slow
def __A ( self: Union[str, Any] ) -> Tuple:
_A = '''cpu''' # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
_A = DDPMScheduler(
num_train_timesteps=10_00 , beta_start=0.0_001 , beta_end=0.02 , beta_schedule='''linear''' , clip_sample=__A , )
_A = DDIMScheduler(
num_train_timesteps=10_00 , beta_start=0.0_001 , beta_end=0.02 , beta_schedule='''linear''' , clip_sample=__A , )
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
_A = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(__A ) for _ in range(4 )]
_A = [torch.randn((4, 3, 32, 32) ).to(__A ) for _ in range(4 )]
_A = [torch.randint(0 , 10_00 , (4,) ).long().to(__A ) for _ in range(4 )]
# train with a DDPM scheduler
_A ,_A = self.get_model_optimizer(resolution=32 )
model.train().to(__A )
for i in range(4 ):
optimizer.zero_grad()
_A = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
_A = model(__A , timesteps[i] ).sample
_A = torch.nn.functional.mse_loss(__A , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
_A ,_A = self.get_model_optimizer(resolution=32 )
model.train().to(__A )
for i in range(4 ):
optimizer.zero_grad()
_A = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
_A = model(__A , timesteps[i] ).sample
_A = torch.nn.functional.mse_loss(__A , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(__A , __A , atol=1e-5 ) )
self.assertTrue(torch.allclose(__A , __A , atol=1e-5 ) )
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
__A = parser.parse_args()
__A = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import requests
from bsa import BeautifulSoup
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = BeautifulSoup(requests.get(_lowercase , params=_lowercase ).content , '''html.parser''' )
_A = soup.find('''div''' , attrs={'''class''': '''gs_ri'''} )
_A = div.find('''div''' , attrs={'''class''': '''gs_fl'''} ).find_all('''a''' )
return anchors[2].get_text()
if __name__ == "__main__":
__A = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 30,
'pages': '3979-3990',
'year': 2018,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 62 |
import math
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 SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
__A = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "gptj"
A_ = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self: Any , __A: List[Any]=5_04_00 , __A: Tuple=20_48 , __A: Any=40_96 , __A: List[str]=28 , __A: Optional[int]=16 , __A: Any=64 , __A: Any=None , __A: List[Any]="gelu_new" , __A: Any=0.0 , __A: int=0.0 , __A: Tuple=0.0 , __A: List[str]=1e-5 , __A: int=0.02 , __A: Any=True , __A: str=5_02_56 , __A: Dict=5_02_56 , __A: List[str]=False , **__A: Tuple , ) -> Union[str, Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = n_inner
_A = rotary_dim
_A = activation_function
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = use_cache
_A = bos_token_id
_A = eos_token_id
super().__init__(
bos_token_id=__A , eos_token_id=__A , tie_word_embeddings=__A , **__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[str] , __A: PretrainedConfig , __A: str = "default" , __A: List[PatchingSpec] = None , __A: bool = False , ) -> List[str]:
super().__init__(__A , task=__A , patching_specs=__A , use_past=__A )
if not getattr(self._config , '''pad_token_id''' , __A ):
# TODO: how to do that better?
_A = 0
@property
def __A ( self: Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
_A = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A , direction='''inputs''' )
_A = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __A ( self: Any ) -> int:
return self._config.n_layer
@property
def __A ( self: Any ) -> int:
return self._config.n_head
def __A ( self: List[Any] , __A: PreTrainedTokenizer , __A: int = -1 , __A: int = -1 , __A: bool = False , __A: Optional[TensorType] = None , ) -> Mapping[str, Any]:
_A = super(__A , self ).generate_dummy_inputs(
__A , batch_size=__A , seq_length=__A , is_pair=__A , framework=__A )
# We need to order the input in the way they appears in the forward()
_A = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_A ,_A = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_A = seqlen + 2
_A = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_A = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
_A = common_inputs['''attention_mask''']
if self.use_past:
_A = ordered_inputs['''attention_mask'''].dtype
_A = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A , __A , dtype=__A )] , dim=1 )
return ordered_inputs
@property
def __A ( self: int ) -> int:
return 13
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "transfo-xl"
A_ = ["mems"]
A_ = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self: int , __A: str=26_77_35 , __A: Tuple=[2_00_00, 4_00_00, 20_00_00] , __A: Any=10_24 , __A: int=10_24 , __A: Optional[int]=16 , __A: List[str]=64 , __A: List[Any]=40_96 , __A: Optional[int]=4 , __A: Optional[int]=False , __A: str=18 , __A: str=16_00 , __A: str=10_00 , __A: Optional[Any]=True , __A: Optional[int]=True , __A: Optional[Any]=0 , __A: Optional[int]=-1 , __A: int=True , __A: Optional[int]=0.1 , __A: Dict=0.0 , __A: Optional[Any]=True , __A: int="normal" , __A: Any=0.01 , __A: Optional[Any]=0.01 , __A: Optional[int]=0.02 , __A: Dict=1e-5 , __A: List[Any]=0 , **__A: Tuple , ) -> Dict:
_A = vocab_size
_A = []
self.cutoffs.extend(__A )
if proj_share_all_but_first:
_A = [False] + [True] * len(self.cutoffs )
else:
_A = [False] + [False] * len(self.cutoffs )
_A = d_model
_A = d_embed
_A = d_head
_A = d_inner
_A = div_val
_A = pre_lnorm
_A = n_layer
_A = n_head
_A = mem_len
_A = same_length
_A = attn_type
_A = clamp_len
_A = sample_softmax
_A = adaptive
_A = dropout
_A = dropatt
_A = untie_r
_A = init
_A = init_range
_A = proj_init_std
_A = init_std
_A = layer_norm_epsilon
super().__init__(eos_token_id=__A , **__A )
@property
def __A ( self: Any ) -> Optional[int]:
# Message copied from Transformer-XL documentation
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def __A ( self: Union[str, Any] , __A: Tuple ) -> Optional[Any]:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["image_processor", "tokenizer"]
A_ = "AutoImageProcessor"
A_ = "AutoTokenizer"
def __init__( self: Tuple , __A: List[Any]=None , __A: Union[str, Any]=None , **__A: Union[str, Any] ) -> List[Any]:
_A = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __A , )
_A = kwargs.pop('''feature_extractor''' )
_A = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__A , __A )
_A = self.image_processor
_A = False
def __call__( self: Union[str, Any] , *__A: Optional[int] , **__A: Tuple ) -> Any:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A , **__A )
_A = kwargs.pop('''images''' , __A )
_A = kwargs.pop('''text''' , __A )
if len(__A ) > 0:
_A = args[0]
_A = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
_A = self.image_processor(__A , *__A , **__A )
if text is not None:
_A = self.tokenizer(__A , **__A )
if text is None:
return inputs
elif images is None:
return encodings
else:
_A = encodings['''input_ids''']
return inputs
def __A ( self: int , *__A: Union[str, Any] , **__A: Optional[Any] ) -> int:
return self.tokenizer.batch_decode(*__A , **__A )
def __A ( self: Optional[int] , *__A: Optional[Any] , **__A: Union[str, Any] ) -> Optional[Any]:
return self.tokenizer.decode(*__A , **__A )
@contextmanager
def __A ( self: Tuple ) -> Optional[int]:
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
_A = True
_A = self.tokenizer
yield
_A = self.image_processor
_A = False
def __A ( self: Dict , __A: str , __A: List[Any]=False , __A: Any=None ) -> Tuple:
if added_vocab is None:
_A = self.tokenizer.get_added_vocab()
_A = {}
while tokens:
_A = re.search(R'''<s_(.*?)>''' , __A , re.IGNORECASE )
if start_token is None:
break
_A = start_token.group(1 )
_A = re.search(Rf"""</s_{key}>""" , __A , re.IGNORECASE )
_A = start_token.group()
if end_token is None:
_A = tokens.replace(__A , '''''' )
else:
_A = end_token.group()
_A = re.escape(__A )
_A = re.escape(__A )
_A = re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" , __A , re.IGNORECASE )
if content is not None:
_A = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_A = self.tokenajson(__A , is_inner_value=__A , added_vocab=__A )
if value:
if len(__A ) == 1:
_A = value[0]
_A = value
else: # leaf nodes
_A = []
for leaf in content.split(R'''<sep/>''' ):
_A = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_A = leaf[1:-2] # for categorical special tokens
output[key].append(__A )
if len(output[key] ) == 1:
_A = output[key][0]
_A = tokens[tokens.find(__A ) + len(__A ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=__A , added_vocab=__A )
if len(__A ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def __A ( self: List[str] ) -> str:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __A , )
return self.image_processor_class
@property
def __A ( self: Optional[Any] ) -> int:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __A , )
return self.image_processor
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __A ( _lowercase , _lowercase=10 ):
'''simple docstring'''
_A = []
for _ in range(_lowercase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __A ( _lowercase , _lowercase=10 ):
'''simple docstring'''
_A = []
for step in range(_lowercase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
_A = os.path.join(_lowercase , '''schedule.bin''' )
torch.save(scheduler.state_dict() , _lowercase )
_A = torch.load(_lowercase )
scheduler.load_state_dict(_lowercase )
return lrs
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: str , __A: Optional[int] , __A: Optional[int] , __A: Optional[Any] ) -> Optional[int]:
self.assertEqual(len(__A ) , len(__A ) )
for a, b in zip(__A , __A ):
self.assertAlmostEqual(__A , __A , delta=__A )
def __A ( self: Union[str, Any] ) -> Union[str, Any]:
_A = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__A )
_A = torch.tensor([0.4, 0.2, -0.5] )
_A = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
_A = AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(1_00 ):
_A = criterion(__A , __A )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def __A ( self: Optional[Any] ) -> List[Any]:
_A = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__A )
_A = torch.tensor([0.4, 0.2, -0.5] )
_A = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
_A = Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=__A , weight_decay=0.0 , relative_step=__A , scale_parameter=__A , warmup_init=__A , )
for _ in range(10_00 ):
_A = criterion(__A , __A )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
A_ = nn.Linear(50 , 50 ) if is_torch_available() else None
A_ = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
A_ = 10
def __A ( self: Optional[Any] , __A: List[str] , __A: Optional[Any] , __A: Optional[int] , __A: List[Any]=None ) -> List[Any]:
self.assertEqual(len(__A ) , len(__A ) )
for a, b in zip(__A , __A ):
self.assertAlmostEqual(__A , __A , delta=__A , msg=__A )
def __A ( self: Tuple ) -> List[Any]:
_A = {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
_A = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
_A ,_A = data
_A = scheduler_func(self.optimizer , **__A )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
_A = unwrap_schedule(__A , self.num_steps )
self.assertListAlmostEqual(
__A , __A , tol=1e-2 , msg=f"""failed for {scheduler_func} in normal scheduler""" , )
_A = scheduler_func(self.optimizer , **__A )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(__A ) # wrap to test picklability of the schedule
_A = unwrap_and_save_reload_schedule(__A , self.num_steps )
self.assertListEqual(__A , __A , msg=f"""failed for {scheduler_func} in save and reload""" )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Tuple ) -> Optional[Any]:
_A = fn
def __call__( self: int , *__A: Tuple , **__A: Optional[Any] ) -> Optional[int]:
return self.fn(*__A , **__A )
@classmethod
def __A ( self: Tuple , __A: Dict ) -> List[str]:
_A = list(map(self , scheduler.lr_lambdas ) )
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "summarization"
A_ = ["loss"]
A_ = ROUGE_KEYS
A_ = "rouge2"
def __init__( self: List[str] , __A: Any , **__A: Tuple ) -> Optional[Any]:
if hparams.sortish_sampler and hparams.gpus > 1:
_A = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training''' )
if hparams.sortish_sampler:
raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously''' )
super().__init__(__A , num_labels=__A , mode=self.mode , **__A )
use_task_specific_params(self.model , '''summarization''' )
save_git_info(self.hparams.output_dir )
_A = Path(self.output_dir ) / '''metrics.json'''
_A = Path(self.output_dir ) / '''hparams.pkl'''
pickle_save(self.hparams , self.hparams_save_path )
_A = 0
_A = defaultdict(__A )
_A = self.config.model_type
_A = self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size
_A = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
_A = {
'''train''': self.hparams.n_train,
'''val''': self.hparams.n_val,
'''test''': self.hparams.n_test,
}
_A = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
_A = {
'''train''': self.hparams.max_target_length,
'''val''': self.hparams.val_max_target_length,
'''test''': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
_A = get_git_info()['''repo_sha''']
_A = hparams.num_workers
_A = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , __A ):
_A = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
_A = self.decoder_start_token_id
_A = (
SeqaSeqDataset if hasattr(self.tokenizer , '''prepare_seq2seq_batch''' ) else LegacySeqaSeqDataset
)
_A = False
_A = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
_A = self.hparams.eval_max_gen_length
else:
_A = self.model.config.max_length
_A = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def __A ( self: List[Any] , __A: Dict[str, torch.Tensor] ) -> Dict[str, List[str]]:
_A = {
k: self.tokenizer.batch_decode(v.tolist() ) if '''mask''' not in k else v.shape for k, v in batch.items()
}
save_json(__A , Path(self.output_dir ) / '''text_batch.json''' )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / '''tok_batch.json''' )
_A = True
return readable_batch
def __A ( self: Optional[Any] , __A: Any , **__A: List[str] ) -> Any:
return self.model(__A , **__A )
def __A ( self: Tuple , __A: List[int] ) -> Union[str, Any]:
_A = self.tokenizer.batch_decode(
__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )
return lmap(str.strip , __A )
def __A ( self: List[str] , __A: dict ) -> Tuple:
_A = self.tokenizer.pad_token_id
_A ,_A = batch['''input_ids'''], batch['''attention_mask''']
_A = batch['''labels''']
if isinstance(self.model , __A ):
_A = self.model._shift_right(__A )
else:
_A = shift_tokens_right(__A , __A )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
_A = decoder_input_ids
self.save_readable_batch(__A )
_A = self(__A , attention_mask=__A , decoder_input_ids=__A , use_cache=__A )
_A = outputs['''logits''']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
_A = nn.CrossEntropyLoss(ignore_index=__A )
assert lm_logits.shape[-1] == self.vocab_size
_A = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
_A = nn.functional.log_softmax(__A , dim=-1 )
_A ,_A = label_smoothed_nll_loss(
__A , __A , self.hparams.label_smoothing , ignore_index=__A )
return (loss,)
@property
def __A ( self: List[Any] ) -> int:
return self.tokenizer.pad_token_id
def __A ( self: List[Any] , __A: Union[str, Any] , __A: List[str] ) -> Dict:
_A = self._step(__A )
_A = dict(zip(self.loss_names , __A ) )
# tokens per batch
_A = batch['''input_ids'''].ne(self.pad ).sum() + batch['''labels'''].ne(self.pad ).sum()
_A = batch['''input_ids'''].shape[0]
_A = batch['''input_ids'''].eq(self.pad ).sum()
_A = batch['''input_ids'''].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def __A ( self: Any , __A: List[Any] , __A: Dict ) -> Dict:
return self._generative_step(__A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Tuple="val" ) -> Dict:
self.step_count += 1
_A = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
_A = losses['''loss''']
_A = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
}
_A = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
_A = torch.tensor(__A ).type_as(__A )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(__A )
_A = {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
_A = self.step_count
self.metrics[prefix].append(__A ) # callback writes this to self.metrics_save_path
_A = flatten_list([x['''preds'''] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f"""{prefix}_loss""": loss,
f"""{prefix}_{self.val_metric}""": metric_tensor,
}
def __A ( self: List[Any] , __A: Tuple , __A: Optional[int] ) -> Dict:
return calculate_rouge(__A , __A )
def __A ( self: Optional[int] , __A: dict ) -> dict:
_A = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
_A = self.model.generate(
batch['''input_ids'''] , attention_mask=batch['''attention_mask'''] , use_cache=__A , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
_A = (time.time() - ta) / batch['''input_ids'''].shape[0]
_A = self.ids_to_clean_text(__A )
_A = self.ids_to_clean_text(batch['''labels'''] )
_A = self._step(__A )
_A = dict(zip(self.loss_names , __A ) )
_A = self.calc_generative_metrics(__A , __A )
_A = np.mean(lmap(__A , __A ) )
base_metrics.update(gen_time=__A , gen_len=__A , preds=__A , target=__A , **__A )
return base_metrics
def __A ( self: Dict , __A: int , __A: str ) -> List[Any]:
return self._generative_step(__A )
def __A ( self: int , __A: int ) -> List[str]:
return self.validation_epoch_end(__A , prefix='''test''' )
def __A ( self: Optional[Any] , __A: int ) -> SeqaSeqDataset:
_A = self.n_obs[type_path]
_A = self.target_lens[type_path]
_A = self.dataset_class(
self.tokenizer , type_path=__A , n_obs=__A , max_target_length=__A , **self.dataset_kwargs , )
return dataset
def __A ( self: Optional[int] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = self.get_dataset(__A )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
_A = dataset.make_sortish_sampler(__A , distributed=self.hparams.gpus > 1 )
return DataLoader(
__A , batch_size=__A , collate_fn=dataset.collate_fn , shuffle=__A , num_workers=self.num_workers , sampler=__A , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
_A = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
__A , batch_sampler=__A , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
__A , batch_size=__A , collate_fn=dataset.collate_fn , shuffle=__A , num_workers=self.num_workers , sampler=__A , )
def __A ( self: Tuple ) -> DataLoader:
_A = self.get_dataloader('''train''' , batch_size=self.hparams.train_batch_size , shuffle=__A )
return dataloader
def __A ( self: Optional[Any] ) -> DataLoader:
return self.get_dataloader('''val''' , batch_size=self.hparams.eval_batch_size )
def __A ( self: str ) -> DataLoader:
return self.get_dataloader('''test''' , batch_size=self.hparams.eval_batch_size )
@staticmethod
def __A ( __A: Dict , __A: Tuple ) -> List[str]:
BaseTransformer.add_model_specific_args(__A , __A )
add_generic_args(__A , __A )
parser.add_argument(
'''--max_source_length''' , default=10_24 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--max_target_length''' , default=56 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--val_max_target_length''' , default=1_42 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--test_max_target_length''' , default=1_42 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument('''--freeze_encoder''' , action='''store_true''' )
parser.add_argument('''--freeze_embeds''' , action='''store_true''' )
parser.add_argument('''--sortish_sampler''' , action='''store_true''' , default=__A )
parser.add_argument('''--overwrite_output_dir''' , action='''store_true''' , default=__A )
parser.add_argument('''--max_tokens_per_batch''' , type=__A , default=__A )
parser.add_argument('''--logger_name''' , type=__A , choices=['''default''', '''wandb''', '''wandb_shared'''] , default='''default''' )
parser.add_argument('''--n_train''' , type=__A , default=-1 , required=__A , help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_val''' , type=__A , default=5_00 , required=__A , help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_test''' , type=__A , default=-1 , required=__A , help='''# examples. -1 means use all.''' )
parser.add_argument(
'''--task''' , type=__A , default='''summarization''' , required=__A , help='''# examples. -1 means use all.''' )
parser.add_argument('''--label_smoothing''' , type=__A , default=0.0 , required=__A )
parser.add_argument('''--src_lang''' , type=__A , default='''''' , required=__A )
parser.add_argument('''--tgt_lang''' , type=__A , default='''''' , required=__A )
parser.add_argument('''--eval_beams''' , type=__A , default=__A , required=__A )
parser.add_argument(
'''--val_metric''' , type=__A , default=__A , required=__A , choices=['''bleu''', '''rouge2''', '''loss''', None] )
parser.add_argument('''--eval_max_gen_length''' , type=__A , default=__A , help='''never generate more than n tokens''' )
parser.add_argument('''--save_top_k''' , type=__A , default=1 , required=__A , help='''How many checkpoints to save''' )
parser.add_argument(
'''--early_stopping_patience''' , type=__A , default=-1 , required=__A , help=(
'''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
''' val_check_interval will effect it.'''
) , )
return parser
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "translation"
A_ = ["loss"]
A_ = ["bleu"]
A_ = "bleu"
def __init__( self: List[Any] , __A: Tuple , **__A: Optional[int] ) -> int:
super().__init__(__A , **__A )
_A = hparams.src_lang
_A = hparams.tgt_lang
def __A ( self: List[str] , __A: int , __A: str ) -> dict:
return calculate_bleu(__A , __A )
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=_lowercase )
check_output_dir(_lowercase , expected_items=3 )
if model is None:
if "summarization" in args.task:
_A = SummarizationModule(_lowercase )
else:
_A = TranslationModule(_lowercase )
_A = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('''/tmp''' )
or str(args.output_dir ).startswith('''/var''' )
):
_A = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
_A = os.environ.get('''WANDB_PROJECT''' , _lowercase )
_A = WandbLogger(name=model.output_dir.name , project=_lowercase )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
_A = WandbLogger(name=model.output_dir.name , project=f"""hf_{dataset}""" )
if args.early_stopping_patience >= 0:
_A = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
_A = False
_A = args.val_metric == '''loss'''
_A = generic_train(
_lowercase , _lowercase , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , _lowercase ) , early_stopping_callback=_lowercase , logger=_lowercase , )
pickle_save(model.hparams , model.output_dir / '''hparams.pkl''' )
if not args.do_predict:
return model
_A = ''''''
_A = sorted(glob.glob(os.path.join(args.output_dir , '''*.ckpt''' ) , recursive=_lowercase ) )
if checkpoints:
_A = checkpoints[-1]
_A = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
__A = argparse.ArgumentParser()
__A = pl.Trainer.add_argparse_args(parser)
__A = SummarizationModule.add_model_specific_args(parser, os.getcwd())
__A = parser.parse_args()
main(args)
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Dict , __A: Dict , __A: List[str]=None , __A: Optional[Any]=True , __A: str=None , **__A: List[str] ) -> Any:
_A = parent
_A = config_class
_A = has_text_modality
_A = kwargs
_A = common_properties
def __A ( self: int ) -> Optional[int]:
_A = self.config_class(**self.inputs_dict )
_A = (
['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(['''vocab_size'''] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(__A , __A ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(__A ):
try:
setattr(__A , __A , __A )
self.parent.assertEqual(
getattr(__A , __A ) , __A , msg=f"""`{name} value {idx} expected, but was {getattr(__A , __A )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(__A ):
try:
_A = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(__A , __A ) , __A , msg=f"""`{name} value {idx} expected, but was {getattr(__A , __A )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def __A ( self: Tuple ) -> List[str]:
_A = self.config_class(**self.inputs_dict )
_A = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , __A )
def __A ( self: Union[str, Any] ) -> Optional[int]:
_A = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = os.path.join(__A , '''config.json''' )
config_first.to_json_file(__A )
_A = self.config_class.from_json_file(__A )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __A ( self: str ) -> Optional[Any]:
_A = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(__A )
_A = self.config_class.from_pretrained(__A )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __A ( self: Optional[Any] ) -> Dict:
_A = self.config_class(**self.inputs_dict )
_A = '''test'''
with tempfile.TemporaryDirectory() as tmpdirname:
_A = os.path.join(__A , __A )
config_first.save_pretrained(__A )
_A = self.config_class.from_pretrained(__A , subfolder=__A )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __A ( self: int ) -> Tuple:
_A = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_A = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def __A ( self: List[str] ) -> List[Any]:
if self.config_class.is_composition:
return
_A = self.config_class()
self.parent.assertIsNotNone(__A )
def __A ( self: Dict ) -> Dict:
_A = copy.deepcopy(__A )
_A = self.config_class(**__A )
_A = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa) )
elif getattr(__A , __A ) != value:
wrong_values.append((key, getattr(__A , __A ), value) )
if len(__A ) > 0:
_A = '''\n'''.join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def __A ( self: List[Any] ) -> str:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
_A = set()
# edges = list of graph's edges
_A = get_edges(_lowercase )
# 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:
_A ,_A = edges.pop()
chosen_vertices.add(_lowercase )
chosen_vertices.add(_lowercase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_lowercase )
return chosen_vertices
def __A ( _lowercase ):
'''simple docstring'''
_A = 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)}")
| 62 |
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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "open-llama"
def __init__( self: int , __A: Optional[Any]=10_00_00 , __A: Union[str, Any]=40_96 , __A: List[str]=1_10_08 , __A: Union[str, Any]=32 , __A: Any=32 , __A: str="silu" , __A: str=20_48 , __A: Any=0.02 , __A: Optional[Any]=1e-6 , __A: str=True , __A: Optional[Any]=0 , __A: List[Any]=1 , __A: int=2 , __A: str=False , __A: List[str]=True , __A: List[str]=0.1 , __A: str=0.1 , __A: str=True , __A: List[str]=True , __A: List[str]=None , **__A: List[str] , ) -> str:
_A = vocab_size
_A = max_position_embeddings
_A = hidden_size
_A = intermediate_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = initializer_range
_A = rms_norm_eps
_A = use_cache
_A = kwargs.pop(
'''use_memorry_efficient_attention''' , __A )
_A = hidden_dropout_prob
_A = attention_dropout_prob
_A = use_stable_embedding
_A = shared_input_output_embedding
_A = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , tie_word_embeddings=__A , **__A , )
def __A ( self: List[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __A ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
f"""got {self.rope_scaling}""" )
_A = self.rope_scaling.get('''type''' , __A )
_A = self.rope_scaling.get('''factor''' , __A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(__A , __A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@slow
@require_torch
def __A ( self: Union[str, Any] ) -> Tuple:
_A = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
_A = BertTokenizer.from_pretrained('''bert-base-uncased''' )
_A = bertabert.config.encoder.vocab_size
_A = tokenizer.sep_token_id
_A = tokenizer.cls_token_id
_A = 1_28
_A = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
_A = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
_A = train_dataset.select(range(32 ) )
_A = val_dataset.select(range(16 ) )
_A = 4
def _map_to_encoder_decoder_inputs(__A: int ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_A = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__A , max_length=5_12 )
_A = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__A , max_length=1_28 )
_A = inputs.input_ids
_A = inputs.attention_mask
_A = outputs.input_ids
_A = outputs.input_ids.copy()
_A = [
[-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
_A = outputs.attention_mask
assert all(len(__A ) == 5_12 for x in inputs.input_ids )
assert all(len(__A ) == 1_28 for x in outputs.input_ids )
return batch
def _compute_metrics(__A: int ):
_A = pred.label_ids
_A = pred.predictions
# all unnecessary tokens are removed
_A = tokenizer.batch_decode(__A , skip_special_tokens=__A )
_A = tokenizer.batch_decode(__A , skip_special_tokens=__A )
_A = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__A ) )] ) / len(__A )
return {"accuracy": accuracy}
# map train dataset
_A = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__A , batch_size=__A , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
_A = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__A , batch_size=__A , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
_A = self.get_auto_remove_tmp_dir()
_A = SeqaSeqTrainingArguments(
output_dir=__A , per_device_train_batch_size=__A , per_device_eval_batch_size=__A , predict_with_generate=__A , evaluation_strategy='''steps''' , do_train=__A , do_eval=__A , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
_A = SeqaSeqTrainer(
model=__A , args=__A , compute_metrics=_compute_metrics , train_dataset=__A , eval_dataset=__A , tokenizer=__A , )
# start training
trainer.train()
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_A = gray_code_sequence_string(_lowercase )
#
# convert them to integers
for i in range(len(_lowercase ) ):
_A = int(sequence[i] , 2 )
return sequence
def __A ( _lowercase ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
_A = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
_A = gray_code_sequence_string(bit_count - 1 )
_A = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
_A = '''0''' + smaller_sequence[i]
sequence.append(_lowercase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
_A = '''1''' + smaller_sequence[i]
sequence.append(_lowercase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 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 = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
'kssteven/ibert-roberta-large-mnli': (
'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "ibert"
def __init__( self: Any , __A: Tuple=3_05_22 , __A: List[Any]=7_68 , __A: Tuple=12 , __A: Dict=12 , __A: List[str]=30_72 , __A: str="gelu" , __A: str=0.1 , __A: Tuple=0.1 , __A: Optional[Any]=5_12 , __A: List[str]=2 , __A: Any=0.02 , __A: int=1e-12 , __A: List[Any]=1 , __A: List[Any]=0 , __A: Any=2 , __A: Optional[Any]="absolute" , __A: Tuple=False , __A: List[Any]="none" , **__A: Any , ) -> List[str]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = quant_mode
_A = force_dequant
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
__A = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
__A = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __A ( self: Union[str, Any] ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def __A ( self: Optional[int] , __A: List[List[List[str]]] , __A: List[List[str]] , __A: int = 1 , __A: int = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__A , hypotheses=__A , min_len=__A , max_len=__A )
}
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = f"""{sampling_rate}"""
_A = '''1'''
_A = '''f32le'''
_A = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(_lowercase , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_A = ffmpeg_process.communicate(_lowercase )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_A = output_stream[0]
_A = np.frombuffer(_lowercase , np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __A ( _lowercase , _lowercase , _lowercase = "f32le" , ):
'''simple docstring'''
_A = f"""{sampling_rate}"""
_A = '''1'''
if format_for_conversion == "s16le":
_A = 2
elif format_for_conversion == "f32le":
_A = 4
else:
raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_A = platform.system()
if system == "Linux":
_A = '''alsa'''
_A = '''default'''
elif system == "Darwin":
_A = '''avfoundation'''
_A = ''':0'''
elif system == "Windows":
_A = '''dshow'''
_A = '''default'''
_A = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_A = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_A = _ffmpeg_stream(_lowercase , _lowercase )
for item in iterator:
yield item
def __A ( _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = "f32le" , ):
'''simple docstring'''
if stream_chunk_s is not None:
_A = stream_chunk_s
else:
_A = chunk_length_s
_A = ffmpeg_microphone(_lowercase , _lowercase , format_for_conversion=_lowercase )
if format_for_conversion == "s16le":
_A = np.intaa
_A = 2
elif format_for_conversion == "f32le":
_A = np.floataa
_A = 4
else:
raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_A = chunk_length_s / 6
_A = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(_lowercase , (int, float) ):
_A = [stride_length_s, stride_length_s]
_A = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_A = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_A = datetime.datetime.now()
_A = datetime.timedelta(seconds=_lowercase )
for item in chunk_bytes_iter(_lowercase , _lowercase , stride=(stride_left, stride_right) , stream=_lowercase ):
# Put everything back in numpy scale
_A = np.frombuffer(item['''raw'''] , dtype=_lowercase )
_A = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_A = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __A ( _lowercase , _lowercase , _lowercase , _lowercase = False ):
'''simple docstring'''
_A = b''''''
_A ,_A = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_A = 0
for raw in iterator:
acc += raw
if stream and len(_lowercase ) < chunk_len:
_A = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(_lowercase ) >= chunk_len:
# We are flushing the accumulator
_A = (_stride_left, stride_right)
_A = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_A = False
yield item
_A = stride_left
_A = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(_lowercase ) > stride_left:
_A = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_A = False
yield item
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = 2**24 # 16Mo
try:
with subprocess.Popen(_lowercase , stdout=subprocess.PIPE , bufsize=_lowercase ) as ffmpeg_process:
while True:
_A = ffmpeg_process.stdout.read(_lowercase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
__A = logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
__A = {
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
__A = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
__A = sorted(arg_to_scheduler.keys())
__A = '{' + ', '.join(arg_to_scheduler_choices) + '}'
class SCREAMING_SNAKE_CASE ( pl.LightningModule ):
"""simple docstring"""
def __init__( self: Union[str, Any] , __A: argparse.Namespace , __A: Optional[int]=None , __A: List[Any]="base" , __A: int=None , __A: str=None , __A: List[Any]=None , **__A: int , ) -> int:
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(__A )
_A = 0
_A = Path(self.hparams.output_dir )
_A = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
_A = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=__A , **__A , )
else:
_A = config
_A = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , __A , __A ):
assert hasattr(self.config , __A ), f"""model config doesn't have a `{p}` attribute"""
setattr(self.config , __A , getattr(self.hparams , __A ) )
if tokenizer is None:
_A = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__A , )
else:
_A = tokenizer
_A = MODEL_MODES[mode]
if model is None:
_A = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=__A , )
else:
_A = model
def __A ( self: int , *__A: int , **__A: Optional[Any] ) -> Optional[int]:
_A = self.model_type.from_pretrained(*__A , **__A )
def __A ( self: Optional[int] ) -> Optional[int]:
_A = arg_to_scheduler[self.hparams.lr_scheduler]
_A = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
_A = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def __A ( self: str ) -> Tuple:
_A = self.model
_A = ['''bias''', '''LayerNorm.weight''']
_A = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
_A = Adafactor(
__A , lr=self.hparams.learning_rate , scale_parameter=__A , relative_step=__A )
else:
_A = AdamW(
__A , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
_A = optimizer
_A = self.get_lr_scheduler()
return [optimizer], [scheduler]
def __A ( self: List[Any] , __A: Any , __A: Optional[Any] ) -> int:
return self.validation_step(__A , __A )
def __A ( self: int , __A: Any ) -> Optional[int]:
return self.validation_end(__A )
def __A ( self: Optional[int] ) -> int:
_A = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
_A = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def __A ( self: Union[str, Any] , __A: List[str] ) -> Optional[int]:
if stage == "test":
_A = len(self.test_dataloader().dataset )
else:
_A = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=__A )
_A = len(self.train_dataloader().dataset )
def __A ( self: int , __A: str , __A: int , __A: bool = False ) -> Tuple:
raise NotImplementedError('''You must implement this for your task''' )
def __A ( self: Union[str, Any] ) -> str:
return self.train_loader
def __A ( self: Dict ) -> int:
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=__A )
def __A ( self: Optional[Any] ) -> Optional[Any]:
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=__A )
def __A ( self: Tuple , __A: List[Any] ) -> List[str]:
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
__A , list(filter(__A , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def __A ( self: Optional[Any] , __A: Dict[str, Any] ) -> None:
_A = self.output_dir.joinpath('''best_tfmr''' )
_A = self.step_count
self.model.save_pretrained(__A )
self.tokenizer.save_pretrained(__A )
@staticmethod
def __A ( __A: Union[str, Any] , __A: Optional[Any] ) -> Dict:
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=__A , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(__A ).parent / '''test_run''' / '''cache''' ) , type=__A , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=__A , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=__A , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=__A , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=__A , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5e-5 , type=__A , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=__A , metavar=__A , type=__A , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=__A , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__A , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=__A , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=__A , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=__A )
parser.add_argument('''--train_batch_size''' , default=32 , type=__A )
parser.add_argument('''--eval_batch_size''' , default=32 , type=__A )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class SCREAMING_SNAKE_CASE ( pl.Callback ):
"""simple docstring"""
def __A ( self: Optional[Any] , __A: str , __A: List[str] ) -> Union[str, Any]:
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class SCREAMING_SNAKE_CASE ( pl.Callback ):
"""simple docstring"""
def __A ( self: str , __A: int , __A: List[str] ) -> List[Any]:
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__A )
class SCREAMING_SNAKE_CASE ( pl.Callback ):
"""simple docstring"""
def __A ( self: int , __A: Union[str, Any] , __A: Any ) -> Optional[Any]:
_A = trainer.lr_schedulers[0]['''scheduler''']
_A = {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(__A )
def __A ( self: str , __A: pl.Trainer , __A: pl.LightningModule ) -> Optional[Any]:
rank_zero_info('''***** Validation results *****''' )
_A = trainer.callback_metrics
# Log results
for key in sorted(__A ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(__A , str(metrics[key] ) ) )
def __A ( self: Tuple , __A: pl.Trainer , __A: pl.LightningModule ) -> Optional[Any]:
rank_zero_info('''***** Test results *****''' )
_A = trainer.callback_metrics
# Log and save results to file
_A = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(__A , '''w''' ) as writer:
for key in sorted(__A ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(__A , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(__A , str(metrics[key] ) ) )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
parser.add_argument(
'''--output_dir''' , default=str(Path(_lowercase ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_lowercase , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_lowercase , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_lowercase )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_lowercase , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_lowercase , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_lowercase , default=42 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_lowercase ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_lowercase , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=True , _lowercase=[] , _lowercase=None , _lowercase=None , **_lowercase , ):
'''simple docstring'''
pl.seed_everything(args.seed )
# init model
_A = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_lowercase )
# add custom checkpoints
if checkpoint_callback is None:
_A = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_lowercase )
if logging_callback is None:
_A = LoggingCallback()
_A = {}
if args.fpaa:
_A = 16
if args.gpus > 1:
_A = '''auto'''
_A = '''ddp'''
_A = args.accumulate_grad_batches
_A = None
_A = '''auto'''
_A = pl.Trainer.from_argparse_args(
_lowercase , weights_summary=_lowercase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_lowercase , val_check_interval=1 , num_sanity_val_steps=2 , **_lowercase , )
if args.do_train:
trainer.fit(_lowercase )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def __A ( ):
'''simple docstring'''
_A = ArgumentParser('''Accelerate CLI tool''' , usage='''accelerate <command> [<args>]''' , allow_abbrev=_lowercase )
_A = parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=_lowercase )
env_command_parser(subparsers=_lowercase )
launch_command_parser(subparsers=_lowercase )
tpu_command_parser(subparsers=_lowercase )
test_command_parser(subparsers=_lowercase )
# Let's go
_A = parser.parse_args()
if not hasattr(_lowercase , '''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(_lowercase )
if __name__ == "__main__":
main()
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: list[int] ) -> None:
_A = len(__A )
_A = [0] * len_array
if len_array > 0:
_A = array[0]
for i in range(1 , __A ):
_A = self.prefix_sum[i - 1] + array[i]
def __A ( self: Optional[int] , __A: int , __A: int ) -> int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __A ( self: Optional[int] , __A: int ) -> bool:
_A = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(__A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = AutoencoderKL
A_ = "sample"
A_ = 1e-2
@property
def __A ( self: int ) -> Union[str, Any]:
_A = 4
_A = 3
_A = (32, 32)
_A = floats_tensor((batch_size, num_channels) + sizes ).to(__A )
return {"sample": image}
@property
def __A ( self: Optional[Any] ) -> List[str]:
return (3, 32, 32)
@property
def __A ( self: Tuple ) -> Optional[Any]:
return (3, 32, 32)
def __A ( self: str ) -> Any:
_A = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
_A = self.dummy_input
return init_dict, inputs_dict
def __A ( self: Optional[Any] ) -> Optional[Any]:
pass
def __A ( self: List[str] ) -> str:
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def __A ( self: Any ) -> Tuple:
# enable deterministic behavior for gradient checkpointing
_A ,_A = self.prepare_init_args_and_inputs_for_common()
_A = self.model_class(**__A )
model.to(__A )
assert not model.is_gradient_checkpointing and model.training
_A = model(**__A ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
_A = torch.randn_like(__A )
_A = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
_A = self.model_class(**__A )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(__A )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
_A = model_a(**__A ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
_A = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
_A = dict(model.named_parameters() )
_A = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def __A ( self: Optional[Any] ) -> Any:
_A ,_A = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(__A )
_A = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def __A ( self: Dict ) -> Optional[Any]:
_A = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
_A = model.to(__A )
model.eval()
if torch_device == "mps":
_A = torch.manual_seed(0 )
else:
_A = torch.Generator(device=__A ).manual_seed(0 )
_A = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
_A = image.to(__A )
with torch.no_grad():
_A = model(__A , sample_posterior=__A , generator=__A ).sample
_A = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
_A = torch.tensor(
[
-4.00_78e-01,
-3.83_23e-04,
-1.26_81e-01,
-1.14_62e-01,
2.00_95e-01,
1.08_93e-01,
-8.82_47e-02,
-3.03_61e-01,
-9.86_44e-03,
] )
elif torch_device == "cpu":
_A = torch.tensor(
[-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026] )
else:
_A = torch.tensor(
[-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485] )
self.assertTrue(torch_all_close(__A , __A , rtol=1e-2 ) )
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Any , __A: List[Any] , __A: Optional[Any] ) -> Any:
return f"""gaussian_noise_s={seed}_shape={"_".join([str(__A ) for s in shape] )}.npy"""
def __A ( self: List[Any] ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self: List[Any] , __A: Tuple=0 , __A: Any=(4, 3, 5_12, 5_12) , __A: Dict=False ) -> Union[str, Any]:
_A = torch.floataa if fpaa else torch.floataa
_A = torch.from_numpy(load_hf_numpy(self.get_file_format(__A , __A ) ) ).to(__A ).to(__A )
return image
def __A ( self: str , __A: List[str]="CompVis/stable-diffusion-v1-4" , __A: int=False ) -> str:
_A = '''fp16''' if fpaa else None
_A = torch.floataa if fpaa else torch.floataa
_A = AutoencoderKL.from_pretrained(
__A , subfolder='''vae''' , torch_dtype=__A , revision=__A , )
model.to(__A ).eval()
return model
def __A ( self: Any , __A: str=0 ) -> Tuple:
if torch_device == "mps":
return torch.manual_seed(__A )
return torch.Generator(device=__A ).manual_seed(__A )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
[47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
# fmt: on
] )
def __A ( self: Union[str, Any] , __A: Any , __A: str , __A: Any ) -> Tuple:
_A = self.get_sd_vae_model()
_A = self.get_sd_image(__A )
_A = self.get_generator(__A )
with torch.no_grad():
_A = model(__A , generator=__A , sample_posterior=__A ).sample
assert sample.shape == image.shape
_A = sample[-1, -2:, -2:, :2].flatten().float().cpu()
_A = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(__A , __A , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
[47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
# fmt: on
] )
@require_torch_gpu
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: int ) -> Tuple:
_A = self.get_sd_vae_model(fpaa=__A )
_A = self.get_sd_image(__A , fpaa=__A )
_A = self.get_generator(__A )
with torch.no_grad():
_A = model(__A , generator=__A , sample_posterior=__A ).sample
assert sample.shape == image.shape
_A = sample[-1, -2:, :2, -2:].flatten().float().cpu()
_A = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
[47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
# fmt: on
] )
def __A ( self: List[str] , __A: str , __A: Tuple , __A: Optional[Any] ) -> Union[str, Any]:
_A = self.get_sd_vae_model()
_A = self.get_sd_image(__A )
with torch.no_grad():
_A = model(__A ).sample
assert sample.shape == image.shape
_A = sample[-1, -2:, -2:, :2].flatten().float().cpu()
_A = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(__A , __A , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
[37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
# fmt: on
] )
@require_torch_gpu
def __A ( self: str , __A: Tuple , __A: str ) -> str:
_A = self.get_sd_vae_model()
_A = self.get_sd_image(__A , shape=(3, 4, 64, 64) )
with torch.no_grad():
_A = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 5_12, 5_12]
_A = sample[-1, -2:, :2, -2:].flatten().cpu()
_A = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
[16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
# fmt: on
] )
@require_torch_gpu
def __A ( self: str , __A: int , __A: Optional[int] ) -> int:
_A = self.get_sd_vae_model(fpaa=__A )
_A = self.get_sd_image(__A , shape=(3, 4, 64, 64) , fpaa=__A )
with torch.no_grad():
_A = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 5_12, 5_12]
_A = sample[-1, -2:, :2, -2:].flatten().float().cpu()
_A = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def __A ( self: Any , __A: Dict ) -> Union[str, Any]:
_A = self.get_sd_vae_model(fpaa=__A )
_A = self.get_sd_image(__A , shape=(3, 4, 64, 64) , fpaa=__A )
with torch.no_grad():
_A = model.decode(__A ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
_A = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 5_12, 5_12]
assert torch_all_close(__A , __A , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def __A ( self: Any , __A: Tuple ) -> Optional[Any]:
_A = self.get_sd_vae_model()
_A = self.get_sd_image(__A , shape=(3, 4, 64, 64) )
with torch.no_grad():
_A = model.decode(__A ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
_A = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 5_12, 5_12]
assert torch_all_close(__A , __A , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
[47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
# fmt: on
] )
def __A ( self: str , __A: Dict , __A: Any ) -> Tuple:
_A = self.get_sd_vae_model()
_A = self.get_sd_image(__A )
_A = self.get_generator(__A )
with torch.no_grad():
_A = model.encode(__A ).latent_dist
_A = dist.sample(generator=__A )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
_A = sample[0, -1, -3:, -3:].flatten().cpu()
_A = torch.tensor(__A )
_A = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(__A , __A , atol=__A )
| 62 |
import math
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 SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = tempfile.mkdtemp()
# fmt: off
_A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
_A = os.path.join(self.tmpdirname , __A )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__A , __A )
def __A ( self: List[Any] , **__A: Any ) -> List[str]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: int , **__A: List[str] ) -> Optional[Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__A )
def __A ( self: Union[str, Any] ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def __A ( self: Optional[int] ) -> Dict:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self: Any ) -> Dict:
_A = self.get_tokenizer()
_A = self.get_image_processor()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
processor.save_pretrained(self.tmpdirname )
_A = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __A )
def __A ( self: Union[str, Any] ) -> int:
_A = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__A , padding_value=1.0 )
_A = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
_A = self.prepare_image_inputs()
_A = image_processor(__A , return_tensors='''np''' )
_A = processor(images=__A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self: int ) -> List[str]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
_A = '''lower newer'''
_A = processor(text=__A )
_A = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self: Optional[int] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__A , images=__A )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(__A ):
processor()
def __A ( self: Union[str, Any] ) -> Union[str, Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__A )
_A = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def __A ( self: Optional[int] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=__A , image_processor=__A )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__A , images=__A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "dpr"
def __init__( self: Union[str, Any] , __A: Dict=3_05_22 , __A: Optional[Any]=7_68 , __A: Union[str, Any]=12 , __A: Optional[int]=12 , __A: Union[str, Any]=30_72 , __A: Dict="gelu" , __A: Optional[Any]=0.1 , __A: Tuple=0.1 , __A: Optional[int]=5_12 , __A: int=2 , __A: List[str]=0.02 , __A: Dict=1e-12 , __A: List[str]=0 , __A: Union[str, Any]="absolute" , __A: int = 0 , **__A: Optional[int] , ) -> Any:
super().__init__(pad_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = projection_dim
_A = position_embedding_type
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __A ( _lowercase , _lowercase=0 ):
'''simple docstring'''
return sorted(_lowercase , key=lambda _lowercase : x[column] )
def __A ( _lowercase , _lowercase , _lowercase=float('''inf''' ) ):
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , _lowercase ):
_A = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
_A = current_dis
return min_dis
def __A ( _lowercase , _lowercase , _lowercase=float('''inf''' ) ):
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , _lowercase ):
for j in range(max(0 , i - 6 ) , _lowercase ):
_A = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
_A = current_dis
return min_dis
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(_lowercase , _lowercase )
# recursion
_A = points_counts // 2
_A = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[:mid] , _lowercase )
_A = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[mid:] , points_counts - mid )
_A = min(_lowercase , _lowercase )
_A = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(_lowercase )
_A = dis_between_closest_in_strip(
_lowercase , len(_lowercase ) , _lowercase )
return min(_lowercase , _lowercase )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = column_based_sort(_lowercase , column=0 )
_A = column_based_sort(_lowercase , column=1 )
return (
closest_pair_of_points_sqr(
_lowercase , _lowercase , _lowercase )
) ** 0.5
if __name__ == "__main__":
__A = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print('Distance:', closest_pair_of_points(points, len(points)))
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
__A = parser.parse_args()
if args.model_type == "roberta":
__A = RobertaForMaskedLM.from_pretrained(args.model_name)
__A = 'roberta'
elif args.model_type == "gpt2":
__A = GPTaLMHeadModel.from_pretrained(args.model_name)
__A = 'transformer'
__A = model.state_dict()
__A = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__A = state_dict[f'{prefix}.{param_name}']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__A = f'{prefix}.embeddings.{w}.weight'
__A = state_dict[param_name]
for w in ["weight", "bias"]:
__A = f'{prefix}.embeddings.LayerNorm.{w}'
__A = state_dict[param_name]
# Transformer Blocks #
__A = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__A = state_dict[
f'{prefix}.h.{teacher_idx}.{layer}.{w}'
]
__A = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__A = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__A = state_dict[f'{layer}']
if args.vocab_transform:
for w in ["weight", "bias"]:
__A = state_dict[f'lm_head.dense.{w}']
__A = state_dict[f'lm_head.layer_norm.{w}']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__A = state_dict[f'{prefix}.ln_f.{w}']
__A = state_dict['lm_head.weight']
print(f'N layers selected for distillation: {std_idx}')
print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
__A = 'sshleifer/mar_enro_6_3_student'
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: Tuple ) -> Dict:
super().setUp()
_A = cached_path(
'''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz''' , extract_compressed_file=__A , )
_A = f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""
@slow
@require_torch_gpu
def __A ( self: Any ) -> List[Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def __A ( self: Tuple ) -> Optional[Any]:
_A = {
'''$MAX_LEN''': 64,
'''$BS''': 64,
'''$GAS''': 1,
'''$ENRO_DIR''': self.data_dir,
'''facebook/mbart-large-cc25''': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
'''--num_train_epochs 6''': '''--num_train_epochs 1''',
}
# Clean up bash script
_A = (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip()
_A = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' )
for k, v in env_vars_to_replace.items():
_A = bash_script.replace(__A , str(__A ) )
_A = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_A = f"""
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
""".split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_A = ['''finetune.py'''] + bash_script.split() + args
with patch.object(__A , '''argv''' , __A ):
_A = argparse.ArgumentParser()
_A = pl.Trainer.add_argparse_args(__A )
_A = SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_A = parser.parse_args()
_A = main(__A )
# Check metrics
_A = load_json(model.metrics_save_path )
_A = metrics['''val'''][0]
_A = metrics['''val'''][-1]
self.assertEqual(len(metrics['''val'''] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , __A )
self.assertGreater(last_step_stats['''val_avg_gen_time'''] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['''val_avg_gen_time'''] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu'''] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['''val_avg_bleu'''] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu'''] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_A = os.listdir(__A )
_A = [x for x in contents if x.endswith('''.ckpt''' )][0]
_A = os.path.join(args.output_dir , __A )
_A = torch.load(__A , map_location='''cpu''' )
_A = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_A = {os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test'''] ) == 1
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@timeout_decorator.timeout(6_00 )
@slow
@require_torch_gpu
def __A ( self: List[str] ) -> Union[str, Any]:
_A = f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
_A = {
'''--fp16_opt_level=O1''': '''''',
'''$MAX_LEN''': 1_28,
'''$BS''': 16,
'''$GAS''': 1,
'''$ENRO_DIR''': data_dir,
'''$m''': '''sshleifer/student_marian_en_ro_6_1''',
'''val_check_interval=0.25''': '''val_check_interval=1.0''',
}
# Clean up bash script
_A = (
(self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip()
)
_A = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' )
_A = bash_script.replace('''--fp16 ''' , ''' ''' )
for k, v in env_vars_to_replace.items():
_A = bash_script.replace(__A , str(__A ) )
_A = self.get_auto_remove_tmp_dir()
_A = bash_script.replace('''--fp16''' , '''''' )
_A = 6
_A = (
['''distillation.py''']
+ bash_script.split()
+ [
f"""--output_dir={output_dir}""",
'''--gpus=1''',
'''--learning_rate=1e-3''',
f"""--num_train_epochs={epochs}""",
'''--warmup_steps=10''',
'''--val_check_interval=1.0''',
'''--do_predict''',
]
)
with patch.object(__A , '''argv''' , __A ):
_A = argparse.ArgumentParser()
_A = pl.Trainer.add_argparse_args(__A )
_A = SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_A = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_A = distill_main(__A )
# Check metrics
_A = load_json(model.metrics_save_path )
_A = metrics['''val'''][0]
_A = metrics['''val'''][-1]
assert len(metrics['''val'''] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_A = os.listdir(__A )
_A = [x for x in contents if x.endswith('''.ckpt''' )][0]
_A = os.path.join(args.output_dir , __A )
_A = torch.load(__A , map_location='''cpu''' )
_A = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_A = {os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test'''] ) == 1
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 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': 1536,
'junnyu/roformer_chinese_base': 1536,
'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 SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = PRETRAINED_INIT_CONFIGURATION
A_ = RoFormerTokenizer
def __init__( self: Any , __A: Union[str, Any]=None , __A: Dict=None , __A: Dict=True , __A: Union[str, Any]="[UNK]" , __A: List[Any]="[SEP]" , __A: List[str]="[PAD]" , __A: List[str]="[CLS]" , __A: List[str]="[MASK]" , __A: int=True , __A: List[Any]=None , **__A: int , ) -> int:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
pre_tok_state.get('''lowercase''' , __A ) != do_lower_case
or pre_tok_state.get('''strip_accents''' , __A ) != strip_accents
):
_A = getattr(__A , pre_tok_state.pop('''type''' ) )
_A = do_lower_case
_A = strip_accents
_A = pre_tok_class(**__A )
_A = do_lower_case
def __getstate__( self: Union[str, Any] ) -> str:
_A = self.__dict__.copy()
_A = BertPreTokenizer()
return state
def __setstate__( self: Union[str, Any] , __A: Union[str, Any] ) -> Optional[int]:
_A = d
_A = self.__dict__['''_tokenizer'''].get_vocab()
_A = PreTokenizer.custom(JiebaPreTokenizer(__A ) )
def __A ( self: str , __A: str , __A: str=None ) -> int:
_A = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self: Tuple , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self: List[Any] , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
_A = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
def __A ( self: List[str] , __A: int , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[str]=False , **__A: Optional[Any] , ) -> Dict:
_A = BertPreTokenizer()
return super().save_pretrained(__A , __A , __A , __A , **__A )
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: list[list[int]] ) -> Dict:
_A = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(__A ) != 0:
_A = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(__A ) != cols:
raise error
for value in row:
if not isinstance(__A , (int, float) ):
raise error
_A = rows
else:
_A = []
def __A ( self: Any ) -> list[list[int]]:
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def __A ( self: int ) -> int:
return len(self.rows )
@property
def __A ( self: int ) -> int:
return len(self.rows[0] )
@property
def __A ( self: List[str] ) -> tuple[int, int]:
return (self.num_rows, self.num_columns)
@property
def __A ( self: Any ) -> bool:
return self.order[0] == self.order[1]
def __A ( self: int ) -> Matrix:
_A = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(__A )
def __A ( self: Tuple ) -> int:
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def __A ( self: str ) -> bool:
return bool(self.determinant() )
def __A ( self: List[Any] , __A: int , __A: int ) -> int:
_A = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(__A ).determinant()
def __A ( self: Optional[Any] , __A: int , __A: int ) -> int:
if (row + column) % 2 == 0:
return self.get_minor(__A , __A )
return -1 * self.get_minor(__A , __A )
def __A ( self: List[Any] ) -> Matrix:
return Matrix(
[
[self.get_minor(__A , __A ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def __A ( self: List[Any] ) -> Matrix:
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def __A ( self: Tuple ) -> Matrix:
_A = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(__A )
def __A ( self: List[str] ) -> Matrix:
_A = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self: List[str] ) -> str:
return str(self.rows )
def __str__( self: Any ) -> str:
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(__A ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def __A ( self: Optional[Any] , __A: list[int] , __A: int | None = None ) -> None:
_A = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(__A , __A ):
raise type_error
for value in row:
if not isinstance(__A , (int, float) ):
raise type_error
if len(__A ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(__A )
else:
_A = self.rows[0:position] + [row] + self.rows[position:]
def __A ( self: Any , __A: list[int] , __A: int | None = None ) -> None:
_A = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(__A , __A ):
raise type_error
for value in column:
if not isinstance(__A , (int, float) ):
raise type_error
if len(__A ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
_A = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
_A = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self: Union[str, Any] , __A: object ) -> bool:
if not isinstance(__A , __A ):
return NotImplemented
return self.rows == other.rows
def __ne__( self: Optional[int] , __A: object ) -> bool:
return not self == other
def __neg__( self: Dict ) -> Matrix:
return self * -1
def __add__( self: Union[str, Any] , __A: Matrix ) -> Matrix:
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self: Union[str, Any] , __A: Matrix ) -> Matrix:
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self: Optional[Any] , __A: Matrix | int | float ) -> Matrix:
if isinstance(__A , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(__A , __A ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(__A , __A ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self: Optional[Any] , __A: int ) -> Matrix:
if not isinstance(__A , __A ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
_A = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def __A ( cls: Dict , __A: list[int] , __A: list[int] ) -> int:
return sum(row[i] * column[i] for i in range(len(__A ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
__A = 8.314_462 # Unit - J mol-1 K-1
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 |
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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
def __A ( _lowercase = 50 ):
'''simple docstring'''
_A = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'{solution() = }')
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__A = 'bart'
__A = True
@st.cache(allow_output_mutation=_lowercase )
def __A ( ):
'''simple docstring'''
if LOAD_DENSE_INDEX:
_A = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_A = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_A = qar_model.eval()
else:
_A ,_A = (None, None)
if MODEL_TYPE == "bart":
_A = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_A = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_A = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_A = sas_model.eval()
else:
_A ,_A = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=_lowercase )
def __A ( ):
'''simple docstring'''
if LOAD_DENSE_INDEX:
_A = faiss.StandardGpuResources()
_A = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
_A = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 1_28) , )
_A = faiss.IndexFlatIP(1_28 )
_A = faiss.index_cpu_to_gpu(_lowercase , 1 , _lowercase )
wikiaab_gpu_index_flat.add(_lowercase ) # TODO fix for larger GPU
else:
_A ,_A = (None, None)
_A = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_lowercase )
def __A ( ):
'''simple docstring'''
_A = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
_A = elia['''train_eli5''']
_A = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 1_28) )
_A = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(_lowercase )
return (elia_train, eli5_train_q_index)
__A , __A , __A = load_indexes()
__A , __A , __A , __A = load_models()
__A , __A = load_train_data()
def __A ( _lowercase , _lowercase=10 ):
'''simple docstring'''
_A = embed_questions_for_retrieval([question] , _lowercase , _lowercase )
_A ,_A = eli5_train_q_index.search(_lowercase , _lowercase )
_A = [elia_train[int(_lowercase )] for i in I[0]]
return nn_examples
def __A ( _lowercase , _lowercase="wiki40b" , _lowercase="dense" , _lowercase=10 ):
'''simple docstring'''
if source == "none":
_A ,_A = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_A ,_A = query_qa_dense_index(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
else:
_A ,_A = query_es_index(
_lowercase , _lowercase , index_name='''english_wiki40b_snippets_100w''' , n_results=_lowercase , )
_A = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_A = '''question: {} context: {}'''.format(_lowercase , _lowercase )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda _lowercase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowercase : None),
} )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase=64 , _lowercase=2_56 , _lowercase=False , _lowercase=2 , _lowercase=0.95 , _lowercase=0.8 ):
'''simple docstring'''
with torch.no_grad():
_A = qa_sas_generate(
_lowercase , _lowercase , _lowercase , num_answers=1 , num_beams=_lowercase , min_len=_lowercase , max_len=_lowercase , do_sample=_lowercase , temp=_lowercase , top_p=_lowercase , top_k=_lowercase , max_input_length=10_24 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title('Long Form Question Answering with ELI5')
# Start sidebar
__A = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
__A = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__A = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)
__A = [
'Answer the question',
'View the retrieved document only',
'View the most similar ELI5 question and answer',
'Show me everything, please!',
]
__A = st.sidebar.checkbox('Demo options')
if demo_options:
__A = st.sidebar.selectbox(
'',
action_list,
index=3,
)
__A = action_list.index(action_st)
__A = st.sidebar.selectbox(
'',
['Show full text of passages', 'Show passage section titles'],
index=0,
)
__A = show_type == 'Show full text of passages'
else:
__A = 3
__A = True
__A = st.sidebar.checkbox('Retrieval options')
if retrieval_options:
__A = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n '
st.sidebar.markdown(retriever_info)
__A = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
__A = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
__A = 'wiki40b'
__A = 'dense'
__A = 'beam'
__A = 2
__A = 64
__A = 256
__A = None
__A = None
__A = st.sidebar.checkbox('Generation options')
if generate_options:
__A = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n '
st.sidebar.markdown(generate_info)
__A = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
__A = st.sidebar.slider(
'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
__A = st.sidebar.slider(
'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
__A = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__A = st.sidebar.slider(
'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
__A = st.sidebar.slider(
'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
__A = None
# start main text
__A = [
'<MY QUESTION>',
'How do people make chocolate?',
'Why do we get a fever when we are sick?',
'How can different animals perceive different colors?',
'What is natural language processing?',
'What\'s the best way to treat a sunburn?',
'What exactly are vitamins ?',
'How does nuclear energy provide electricity?',
'What\'s the difference between viruses and bacteria?',
'Why are flutes classified as woodwinds when most of them are made out of metal ?',
'Why do people like drinking coffee even though it tastes so bad?',
'What happens when wine ages? How does it make the wine taste better?',
'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
'How does New Zealand have so many large bird predators?',
]
__A = st.selectbox(
'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__A = st.text_input('Enter your question here:', '')
else:
__A = question_s
if st.button('Show me!'):
if action in [0, 1, 3]:
if index_type == "mixed":
__A , __A = make_support(question, source=wiki_source, method='dense', n_results=10)
__A , __A = make_support(question, source=wiki_source, method='sparse', n_results=10)
__A = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__A = support_list[:10]
__A = '<P> ' + ' <P> '.join([res[-1] for res in support_list])
else:
__A , __A = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__A , __A = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == 'sampled'),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('### The model generated answer is:')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
for i, res in enumerate(support_list):
__A = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
__A = res[1].strip()
if sec_titles == "":
__A = '[{}]({})'.format(res[0], wiki_url)
else:
__A = sec_titles.split(' & ')
__A = ' & '.join(
['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
)
st.markdown(
'{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
)
if action in [2, 3]:
__A = find_nearest_training(question)
__A = nn_train_list[0]
st.markdown(
'--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
)
__A = [
'{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
if i == 0 or sc > 2
]
st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))
__A = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A = logging.get_logger(__name__)
def __A ( ):
'''simple docstring'''
_A = os.getenv('''SM_HP_MP_PARAMETERS''' , '''{}''' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
_A = json.loads(_lowercase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
_A = os.getenv('''SM_FRAMEWORK_PARAMS''' , '''{}''' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
_A = json.loads(_lowercase )
if not mpi_options.get('''sagemaker_mpi_enabled''' , _lowercase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('''smdistributed''' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = field(
default="" , metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"} , )
def __A ( self: Dict ) -> Tuple:
super().__post_init__()
warnings.warn(
'''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
'''`TrainingArguments` instead.''' , __A , )
@cached_property
def __A ( self: Any ) -> "torch.device":
logger.info('''PyTorch: setting up devices''' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'''torch.distributed process group is initialized, but local_rank == -1. '''
'''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch''' )
if self.no_cuda:
_A = torch.device('''cpu''' )
_A = 0
elif is_sagemaker_model_parallel_available():
_A = smp.local_rank()
_A = torch.device('''cuda''' , __A )
_A = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='''smddp''' , timeout=self.ddp_timeout_delta )
_A = int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK''' ) )
_A = torch.device('''cuda''' , self.local_rank )
_A = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
_A = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
_A = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='''nccl''' , timeout=self.ddp_timeout_delta )
_A = torch.device('''cuda''' , self.local_rank )
_A = 1
if device.type == "cuda":
torch.cuda.set_device(__A )
return device
@property
def __A ( self: List[Any] ) -> Tuple:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def __A ( self: Dict ) -> Any:
return not is_sagemaker_model_parallel_available()
@property
def __A ( self: int ) -> Optional[Any]:
return False
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = 0
if start < end:
_A = randint(_lowercase , _lowercase )
_A = a[end]
_A = a[pivot]
_A = temp
_A ,_A = _in_place_partition(_lowercase , _lowercase , _lowercase )
count += _in_place_quick_sort(_lowercase , _lowercase , p - 1 )
count += _in_place_quick_sort(_lowercase , p + 1 , _lowercase )
return count
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = 0
_A = randint(_lowercase , _lowercase )
_A = a[end]
_A = a[pivot]
_A = temp
_A = start - 1
for index in range(_lowercase , _lowercase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_A = new_pivot_index + 1
_A = a[new_pivot_index]
_A = a[index]
_A = temp
_A = a[new_pivot_index + 1]
_A = a[end]
_A = temp
return new_pivot_index + 1, count
__A = TemporaryFile()
__A = 100 # 1000 elements are to be sorted
__A , __A = 0, 1 # mean and standard deviation
__A = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)
outfile.seek(0) # using the same array
__A = np.load(outfile)
__A = len(M) - 1
__A = _in_place_quick_sort(M, 0, r)
print(
'No of Comparisons for 100 elements selected from a standard normal distribution'
'is :'
)
print(z)
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import warnings
from .generation import TFGenerationMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead." , snake_case , )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "WhisperFeatureExtractor"
A_ = "WhisperTokenizer"
def __init__( self: Tuple , __A: int , __A: int ) -> List[str]:
super().__init__(__A , __A )
_A = self.feature_extractor
_A = False
def __A ( self: int , __A: Optional[int]=None , __A: Optional[Any]=None , __A: Optional[int]=True ) -> List[Any]:
return self.tokenizer.get_decoder_prompt_ids(task=__A , language=__A , no_timestamps=__A )
def __call__( self: Optional[Any] , *__A: Optional[int] , **__A: Optional[Any] ) -> Optional[int]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A , **__A )
_A = kwargs.pop('''audio''' , __A )
_A = kwargs.pop('''sampling_rate''' , __A )
_A = kwargs.pop('''text''' , __A )
if len(__A ) > 0:
_A = args[0]
_A = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
_A = self.feature_extractor(__A , *__A , sampling_rate=__A , **__A )
if text is not None:
_A = self.tokenizer(__A , **__A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_A = encodings['''input_ids''']
return inputs
def __A ( self: Any , *__A: Optional[Any] , **__A: Any ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Dict , **__A: str ) -> Any:
return self.tokenizer.decode(*__A , **__A )
def __A ( self: int , __A: str , __A: Tuple="np" ) -> int:
return self.tokenizer.get_prompt_ids(__A , return_tensors=__A )
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
from math import sqrt
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
_A = True
# 0 and 1 are none primes.
if number <= 1:
_A = False
for divisor in range(2 , int(round(sqrt(_lowercase ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
_A = False
break
# precondition
assert isinstance(_lowercase , _lowercase ), "'status' must been from type bool"
return status
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
_A = list(range(2 , n + 1 ) )
_A = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_lowercase ) ):
for j in range(i + 1 , len(_lowercase ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
_A = 0
# filters actual prime numbers.
_A = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_lowercase , _lowercase ), "'ans' must been from type list"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n > 2), "'N' must been an int and > 2"
_A = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(_lowercase ):
ans.append(_lowercase )
# precondition
assert isinstance(_lowercase , _lowercase ), "'ans' must been from type list"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and number >= 0, "'number' must been an int and >= 0"
_A = [] # this list will be returns of the function.
# potential prime number factors.
_A = 2
_A = number
if number == 0 or number == 1:
ans.append(_lowercase )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_lowercase ):
while quotient != 1:
if is_prime(_lowercase ) and (quotient % factor == 0):
ans.append(_lowercase )
quotient /= factor
else:
factor += 1
else:
ans.append(_lowercase )
# precondition
assert isinstance(_lowercase , _lowercase ), "'ans' must been from type list"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' bust been an int and >= 0"
_A = 0
# prime factorization of 'number'
_A = prime_factorization(_lowercase )
_A = max(_lowercase )
# precondition
assert isinstance(_lowercase , _lowercase ), "'ans' must been from type int"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' bust been an int and >= 0"
_A = 0
# prime factorization of 'number'
_A = prime_factorization(_lowercase )
_A = min(_lowercase )
# precondition
assert isinstance(_lowercase , _lowercase ), "'ans' must been from type int"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ), "'number' must been an int"
assert isinstance(number % 2 == 0 , _lowercase ), "compare bust been from type bool"
return number % 2 == 0
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ), "'number' must been an int"
assert isinstance(number % 2 != 0 , _lowercase ), "compare bust been from type bool"
return number % 2 != 0
def __A ( _lowercase ):
'''simple docstring'''
assert (
isinstance(_lowercase , _lowercase ) and (number > 2) and is_even(_lowercase )
), "'number' must been an int, even and > 2"
_A = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
_A = get_prime_numbers(_lowercase )
_A = len(_lowercase )
# run variable for while-loops.
_A = 0
_A = None
# exit variable. for break up the loops
_A = True
while i < len_pn and loop:
_A = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
_A = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_lowercase , _lowercase )
and (len(_lowercase ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert (
isinstance(_lowercase , _lowercase )
and isinstance(_lowercase , _lowercase )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
_A = 0
while numbera != 0:
_A = numbera % numbera
_A = numbera
_A = rest
# precondition
assert isinstance(_lowercase , _lowercase ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert (
isinstance(_lowercase , _lowercase )
and isinstance(_lowercase , _lowercase )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
_A = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
_A = prime_factorization(_lowercase )
_A = prime_factorization(_lowercase )
elif numbera == 1 or numbera == 1:
_A = []
_A = []
_A = max(_lowercase , _lowercase )
_A = 0
_A = 0
_A = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
_A = prime_fac_a.count(_lowercase )
_A = prime_fac_a.count(_lowercase )
for _ in range(max(_lowercase , _lowercase ) ):
ans *= n
else:
_A = prime_fac_a.count(_lowercase )
for _ in range(_lowercase ):
ans *= n
done.append(_lowercase )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
_A = prime_fac_a.count(_lowercase )
for _ in range(_lowercase ):
ans *= n
done.append(_lowercase )
# precondition
assert isinstance(_lowercase , _lowercase ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n >= 0), "'number' must been a positive int"
_A = 0
_A = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_lowercase ):
ans += 1
# precondition
assert isinstance(_lowercase , _lowercase ) and is_prime(
_lowercase ), "'ans' must been a prime number and from type int"
return ans
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert (
is_prime(_lowercase ) and is_prime(_lowercase ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
_A = p_number_a + 1 # jump to the next number
_A = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_lowercase ):
number += 1
while number < p_number_a:
ans.append(_lowercase )
number += 1
# fetch the next prime number.
while not is_prime(_lowercase ):
number += 1
# precondition
assert (
isinstance(_lowercase , _lowercase )
and ans[0] != p_number_a
and ans[len(_lowercase ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n >= 1), "'n' must been int and >= 1"
_A = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(_lowercase )
# precondition
assert ans[0] == 1 and ans[len(_lowercase ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (
number > 1
), "'number' must been an int and >= 1"
_A = get_divisors(_lowercase )
# precondition
assert (
isinstance(_lowercase , _lowercase )
and (divisors[0] == 1)
and (divisors[len(_lowercase ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert (
isinstance(_lowercase , _lowercase )
and isinstance(_lowercase , _lowercase )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
_A = gcd(abs(_lowercase ) , abs(_lowercase ) )
# precondition
assert (
isinstance(_lowercase , _lowercase )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n >= 0), "'n' must been a int and >= 0"
_A = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def __A ( _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (n >= 0), "'n' must been an int and >= 0"
_A = 0
_A = 1
_A = 1 # this will be return
for _ in range(n - 1 ):
_A = ans
ans += fiba
_A = tmp
return ans
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
__A = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def __A ( _lowercase , _lowercase , _lowercase=8 ):
'''simple docstring'''
_A = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_A = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , __A: UNetaDConditionModel , __A: DDPMScheduler , __A: VQModel , ) -> List[Any]:
super().__init__()
self.register_modules(
unet=__A , scheduler=__A , movq=__A , )
_A = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __A ( self: Union[str, Any] , __A: Optional[Any] , __A: int , __A: int , __A: Tuple , __A: List[str] , __A: Optional[int] ) -> Union[str, Any]:
if latents is None:
_A = randn_tensor(__A , generator=__A , device=__A , dtype=__A )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
_A = latents.to(__A )
_A = latents * scheduler.init_noise_sigma
return latents
def __A ( self: Optional[Any] , __A: Any=0 ) -> Dict:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_A = torch.device(f"""cuda:{gpu_id}""" )
_A = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__A , __A )
def __A ( self: List[Any] , __A: Union[str, Any]=0 ) -> List[Any]:
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
_A = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_A = None
for cpu_offloaded_model in [self.unet, self.movq]:
_A ,_A = cpu_offload_with_hook(__A , __A , prev_module_hook=__A )
# We'll offload the last model manually.
_A = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __A ( self: Union[str, Any] ) -> List[str]:
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__A , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__A )
def __call__( self: Union[str, Any] , __A: Union[torch.FloatTensor, List[torch.FloatTensor]] , __A: Union[torch.FloatTensor, List[torch.FloatTensor]] , __A: int = 5_12 , __A: int = 5_12 , __A: int = 1_00 , __A: float = 4.0 , __A: int = 1 , __A: Optional[Union[torch.Generator, List[torch.Generator]]] = None , __A: Optional[torch.FloatTensor] = None , __A: Optional[str] = "pil" , __A: bool = True , ) -> Any:
_A = self._execution_device
_A = guidance_scale > 1.0
if isinstance(__A , __A ):
_A = torch.cat(__A , dim=0 )
_A = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__A , __A ):
_A = torch.cat(__A , dim=0 )
if do_classifier_free_guidance:
_A = image_embeds.repeat_interleave(__A , dim=0 )
_A = negative_image_embeds.repeat_interleave(__A , dim=0 )
_A = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__A )
self.scheduler.set_timesteps(__A , device=__A )
_A = self.scheduler.timesteps
_A = self.unet.config.in_channels
_A ,_A = downscale_height_and_width(__A , __A , self.movq_scale_factor )
# create initial latent
_A = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __A , __A , __A , self.scheduler , )
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
_A = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A = {'''image_embeds''': image_embeds}
_A = self.unet(
sample=__A , timestep=__A , encoder_hidden_states=__A , added_cond_kwargs=__A , return_dict=__A , )[0]
if do_classifier_free_guidance:
_A ,_A = noise_pred.split(latents.shape[1] , dim=1 )
_A ,_A = noise_pred.chunk(2 )
_A ,_A = variance_pred.chunk(2 )
_A = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_A = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_A ,_A = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(
__A , __A , __A , generator=__A , )[0]
# post-processing
_A = self.movq.decode(__A , force_not_quantize=__A )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
_A = image * 0.5 + 0.5
_A = image.clamp(0 , 1 )
_A = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_A = self.numpy_to_pil(__A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__A )
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def __A ( self: Dict ) -> int:
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=__A , )
def __A ( self: Optional[int] , __A: Any , __A: List[str] ) -> str:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def __A ( self: List[str] , __A: str , __A: List[str] ) -> Dict:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__A )
class SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def __A ( self: Optional[Any] ) -> Optional[int]:
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=__A , )
def __A ( self: int , __A: str , __A: str ) -> Tuple:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def __A ( self: Optional[Any] , __A: Dict , __A: int ) -> Tuple:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__A )
def __A ( ):
'''simple docstring'''
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def __A ( ):
'''simple docstring'''
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@require_beam
def __A ( self: Union[str, Any] ) -> str:
_A = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_A = DummyBeamDataset(cache_dir=__A , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__A , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_A = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __A )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def __A ( self: Tuple ) -> Dict:
import apache_beam as beam
_A = beam.io.parquetio.WriteToParquet
_A = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_A = DummyBeamDataset(cache_dir=__A , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
_A = partial(__A , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
__A , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
__A , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_A = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __A )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def __A ( self: List[str] ) -> int:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_A = DummyBeamDataset(cache_dir=__A )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def __A ( self: List[str] ) -> List[Any]:
_A = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_A = NestedBeamDataset(cache_dir=__A , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__A , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
_A = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __A )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[str] , __A: VQModel , __A: UNetaDModel , __A: DDIMScheduler ) -> Tuple:
super().__init__()
self.register_modules(vqvae=__A , unet=__A , scheduler=__A )
@torch.no_grad()
def __call__( self: Optional[Any] , __A: int = 1 , __A: Optional[Union[torch.Generator, List[torch.Generator]]] = None , __A: float = 0.0 , __A: int = 50 , __A: Optional[str] = "pil" , __A: bool = True , **__A: int , ) -> Union[Tuple, ImagePipelineOutput]:
_A = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=__A , )
_A = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_A = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(__A )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
_A = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_A = {}
if accepts_eta:
_A = eta
for t in self.progress_bar(self.scheduler.timesteps ):
_A = self.scheduler.scale_model_input(__A , __A )
# predict the noise residual
_A = self.unet(__A , __A ).sample
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(__A , __A , __A , **__A ).prev_sample
# decode the image latents with the VAE
_A = self.vqvae.decode(__A ).sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
_A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_A = self.numpy_to_pil(__A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__A )
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "unispeech"
def __init__( self: str , __A: Any=32 , __A: str=7_68 , __A: str=12 , __A: int=12 , __A: Optional[Any]=30_72 , __A: List[str]="gelu" , __A: List[str]=0.1 , __A: Dict=0.1 , __A: int=0.1 , __A: Any=0.0 , __A: str=0.0 , __A: str=0.1 , __A: Any=0.1 , __A: Optional[int]=0.02 , __A: int=1e-5 , __A: Dict="group" , __A: Union[str, Any]="gelu" , __A: str=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __A: List[str]=(5, 2, 2, 2, 2, 2, 2) , __A: Any=(10, 3, 3, 3, 3, 2, 2) , __A: List[str]=False , __A: Dict=1_28 , __A: Tuple=16 , __A: int=False , __A: List[str]=True , __A: Any=0.05 , __A: str=10 , __A: Any=2 , __A: str=0.0 , __A: Tuple=10 , __A: Any=0 , __A: Optional[Any]=3_20 , __A: str=2 , __A: Optional[int]=0.1 , __A: int=1_00 , __A: List[str]=2_56 , __A: int=2_56 , __A: Optional[Any]=0.1 , __A: List[str]="mean" , __A: Optional[int]=False , __A: Any=False , __A: str=2_56 , __A: List[str]=80 , __A: List[str]=0 , __A: Union[str, Any]=1 , __A: int=2 , __A: Union[str, Any]=0.5 , **__A: Union[str, Any] , ) -> List[str]:
super().__init__(**__A , pad_token_id=__A , bos_token_id=__A , eos_token_id=__A )
_A = hidden_size
_A = feat_extract_norm
_A = feat_extract_activation
_A = list(__A )
_A = list(__A )
_A = list(__A )
_A = conv_bias
_A = num_conv_pos_embeddings
_A = num_conv_pos_embedding_groups
_A = len(self.conv_dim )
_A = num_hidden_layers
_A = intermediate_size
_A = hidden_act
_A = num_attention_heads
_A = hidden_dropout
_A = attention_dropout
_A = activation_dropout
_A = feat_proj_dropout
_A = final_dropout
_A = layerdrop
_A = layer_norm_eps
_A = initializer_range
_A = num_ctc_classes
_A = vocab_size
_A = do_stable_layer_norm
_A = use_weighted_layer_sum
_A = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_A = apply_spec_augment
_A = mask_time_prob
_A = mask_time_length
_A = mask_time_min_masks
_A = mask_feature_prob
_A = mask_feature_length
_A = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_A = num_codevectors_per_group
_A = num_codevector_groups
_A = contrastive_logits_temperature
_A = feat_quantizer_dropout
_A = num_negatives
_A = codevector_dim
_A = proj_codevector_dim
_A = diversity_loss_weight
# ctc loss
_A = ctc_loss_reduction
_A = ctc_zero_infinity
# pretraining loss
_A = replace_prob
@property
def __A ( self: List[str] ) -> int:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
__A = logging.get_logger(__name__)
__A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__A = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
__A = {
'allenai/led-base-16384': 16384,
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = LEDTokenizer
A_ = ["input_ids", "attention_mask"]
def __init__( self: List[str] , __A: List[str]=None , __A: Dict=None , __A: List[Any]=None , __A: List[Any]="replace" , __A: List[Any]="<s>" , __A: Dict="</s>" , __A: Any="</s>" , __A: Optional[int]="<s>" , __A: Dict="<unk>" , __A: Tuple="<pad>" , __A: str="<mask>" , __A: Optional[int]=False , __A: Dict=True , **__A: Union[str, Any] , ) -> List[str]:
super().__init__(
__A , __A , tokenizer_file=__A , errors=__A , bos_token=__A , eos_token=__A , sep_token=__A , cls_token=__A , unk_token=__A , pad_token=__A , mask_token=__A , add_prefix_space=__A , trim_offsets=__A , **__A , )
_A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __A ) != add_prefix_space:
_A = getattr(__A , pre_tok_state.pop('''type''' ) )
_A = add_prefix_space
_A = pre_tok_class(**__A )
_A = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_A = '''post_processor'''
_A = getattr(self.backend_tokenizer , __A , __A )
if tokenizer_component_instance:
_A = 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:
_A = tuple(state['''sep'''] )
if "cls" in state:
_A = tuple(state['''cls'''] )
_A = False
if state.get('''add_prefix_space''' , __A ) != add_prefix_space:
_A = add_prefix_space
_A = True
if state.get('''trim_offsets''' , __A ) != trim_offsets:
_A = trim_offsets
_A = True
if changes_to_apply:
_A = getattr(__A , state.pop('''type''' ) )
_A = component_class(**__A )
setattr(self.backend_tokenizer , __A , __A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def __A ( self: List[str] ) -> str:
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 __A ( self: List[Any] , __A: Optional[int] ) -> str:
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else value
_A = value
def __A ( self: List[str] , *__A: str , **__A: str ) -> BatchEncoding:
_A = kwargs.get('''is_split_into_words''' , __A )
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(*__A , **__A )
def __A ( self: List[str] , *__A: int , **__A: int ) -> BatchEncoding:
_A = kwargs.get('''is_split_into_words''' , __A )
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(*__A , **__A )
def __A ( self: List[str] , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
_A = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
def __A ( self: Optional[int] , __A: int , __A: Union[str, Any]=None ) -> int:
_A = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __A ( self: int , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __A ( self: List[str] , __A: Union[Dict[str, EncodedInput], BatchEncoding] , __A: Optional[int] = None , __A: PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __A: Optional[int] = None , __A: Optional[bool] = None , ) -> dict:
_A = super()._pad(
encoded_inputs=__A , max_length=__A , padding_strategy=__A , pad_to_multiple_of=__A , return_attention_mask=__A , )
# Load from model defaults
if return_attention_mask is None:
_A = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_A = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_A = len(encoded_inputs['''global_attention_mask'''] ) != len(__A )
if needs_to_be_padded:
_A = len(__A ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_A = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
_A = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 62 |
import math
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 SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Union[str, Any] , __A: Dict , __A: Union[str, Any]=sys.maxsize ) -> Tuple:
_A = '''bilinear'''
_A = max_size
_A = short_edge_length
def __call__( self: Dict , __A: Optional[int] ) -> Any:
_A = []
for img in imgs:
_A ,_A = img.shape[:2]
# later: provide list and randomly choose index for resize
_A = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_A = size * 1.0 / min(__A , __A )
if h < w:
_A ,_A = size, scale * w
else:
_A ,_A = scale * h, size
if max(__A , __A ) > self.max_size:
_A = self.max_size * 1.0 / max(__A , __A )
_A = newh * scale
_A = neww * scale
_A = int(neww + 0.5 )
_A = int(newh + 0.5 )
if img.dtype == np.uinta:
_A = Image.fromarray(__A )
_A = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_A = np.asarray(__A )
else:
_A = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_A = nn.functional.interpolate(
__A , (newh, neww) , mode=self.interp_method , align_corners=__A ).squeeze(0 )
img_augs.append(__A )
return img_augs
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: int ) -> List[str]:
_A = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_A = cfg.INPUT.FORMAT
_A = cfg.SIZE_DIVISIBILITY
_A = cfg.PAD_VALUE
_A = cfg.INPUT.MAX_SIZE_TEST
_A = cfg.MODEL.DEVICE
_A = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_A = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_A = lambda __A : (x - self.pixel_mean) / self.pixel_std
def __A ( self: List[Any] , __A: str ) -> Union[str, Any]:
_A = tuple(max(__A ) for s in zip(*[img.shape for img in images] ) )
_A = [im.shape[-2:] for im in images]
_A = [
nn.functional.pad(
__A , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(__A , __A )
]
return torch.stack(__A ), torch.tensor(__A )
def __call__( self: int , __A: Dict , __A: List[str]=False ) -> List[str]:
with torch.no_grad():
if not isinstance(__A , __A ):
_A = [images]
if single_image:
assert len(__A ) == 1
for i in range(len(__A ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(__A , images.pop(__A ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
__A , torch.as_tensor(img_tensorize(images.pop(__A ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_A = torch.tensor([im.shape[:2] for im in images] )
_A = self.aug(__A )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_A = [self.normalizer(__A ) for x in images]
# now pad them to do the following operations
_A ,_A = self.pad(__A )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_A = torch.true_divide(__A , __A )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert torch.isfinite(_lowercase ).all(), "Box tensor contains infinite or NaN!"
_A ,_A = box_size
tensor[:, 0].clamp_(min=0 , max=_lowercase )
tensor[:, 1].clamp_(min=0 , max=_lowercase )
tensor[:, 2].clamp_(min=0 , max=_lowercase )
tensor[:, 3].clamp_(min=0 , max=_lowercase )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
__A = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Dict , __A: str , __A: bool , __A: str = None , __A: list = None ) -> List[str]:
_A = None
_A = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
_A = os.path.abspath('''examples''' )
for item in os.listdir(__A ):
if item not in EXCLUDE_EXAMPLES:
_A = os.path.join(__A , __A )
if os.path.isfile(__A ) and ".py" in item_path:
with self.subTest(
tested_script=__A , feature_script=__A , tested_section='''main()''' if parser_only else '''training_function()''' , ):
_A = compare_against_test(
os.path.join(__A , __A ) , __A , __A , __A )
_A = '''\n'''.join(__A )
if special_strings is not None:
for string in special_strings:
_A = diff.replace(__A , '''''' )
self.assertEqual(__A , '''''' )
def __A ( self: Optional[int] ) -> Union[str, Any]:
self.one_complete_example('''complete_nlp_example.py''' , __A )
self.one_complete_example('''complete_nlp_example.py''' , __A )
def __A ( self: Any ) -> Tuple:
_A = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
_A = [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , __A , __A , __A )
self.one_complete_example('''complete_cv_example.py''' , __A , __A , __A )
@mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "1"} )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = False
@classmethod
def __A ( cls: List[Any] ) -> List[str]:
super().setUpClass()
_A = tempfile.mkdtemp()
_A = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
_A = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __A ( cls: str ) -> int:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __A ( self: str ) -> Dict:
_A = f"""
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def __A ( self: Any ) -> Optional[Any]:
_A = f"""
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
""".split()
_A = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def __A ( self: Tuple ) -> Tuple:
_A = f"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}
""".split()
_A = run_command(self._launch_args + testargs , return_stdout=__A )
self.assertNotIn('''epoch 0:''' , __A )
self.assertIn('''epoch 1:''' , __A )
def __A ( self: Union[str, Any] ) -> Dict:
_A = f"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}
""".split()
_A = run_command(self._launch_args + testargs , return_stdout=__A )
if torch.cuda.is_available():
_A = torch.cuda.device_count()
else:
_A = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , __A )
self.assertIn('''epoch 1:''' , __A )
else:
self.assertIn('''epoch 0:''' , __A )
self.assertIn('''epoch 1:''' , __A )
@slow
def __A ( self: List[str] ) -> List[Any]:
_A = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
_A = run_command(self._launch_args + testargs , return_stdout=__A )
_A = re.findall('''({.+})''' , __A )
_A = [r for r in results if '''accuracy''' in r][-1]
_A = ast.literal_eval(__A )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def __A ( self: int ) -> Optional[Any]:
_A = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __A ( self: str ) -> Tuple:
with tempfile.TemporaryDirectory() as tmpdir:
_A = f"""
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__A , '''tracking''' ) ) )
def __A ( self: List[Any] ) -> Optional[int]:
_A = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def __A ( self: Dict ) -> List[Any]:
_A = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
import operator as op
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = lambda _lowercase , _lowercase : int(x / y ) # noqa: E731 integer division operation
_A = {
'''^''': op.pow,
'''*''': op.mul,
'''/''': div,
'''+''': op.add,
'''-''': op.sub,
} # operators & their respective operation
# print table header
print('''Symbol'''.center(8 ) , '''Action'''.center(12 ) , '''Stack''' , sep=''' | ''' )
print('''-''' * (30 + len(_lowercase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(_lowercase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('''push(''' + x + ''')''').ljust(12 ) , ''','''.join(_lowercase ) , sep=''' | ''' )
else:
_A = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + b + ''')''').ljust(12 ) , ''','''.join(_lowercase ) , sep=''' | ''' )
_A = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + a + ''')''').ljust(12 ) , ''','''.join(_lowercase ) , sep=''' | ''' )
stack.append(
str(opr[x](int(_lowercase ) , int(_lowercase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('''push(''' + a + x + b + ''')''').ljust(12 ) , ''','''.join(_lowercase ) , sep=''' | ''' , )
return int(stack[0] )
if __name__ == "__main__":
__A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__A = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
__A = '\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'
__A = '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'
__A = '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results["meteor"], 4))\n 0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __A ( self: List[str] ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def __A ( self: int , __A: Optional[Any] ) -> Optional[Any]:
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def __A ( self: str , __A: Any , __A: Dict , __A: Optional[Any]=0.9 , __A: Dict=3 , __A: Union[str, Any]=0.5 ) -> Optional[Any]:
if NLTK_VERSION >= version.Version('''3.6.5''' ):
_A = [
meteor_score.single_meteor_score(
word_tokenize(__A ) , word_tokenize(__A ) , alpha=__A , beta=__A , gamma=__A )
for ref, pred in zip(__A , __A )
]
else:
_A = [
meteor_score.single_meteor_score(__A , __A , alpha=__A , beta=__A , gamma=__A )
for ref, pred in zip(__A , __A )
]
return {"meteor": np.mean(__A )}
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = TextToVideoSDPipeline
A_ = TEXT_TO_IMAGE_PARAMS
A_ = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
A_ = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def __A ( self: Optional[Any] ) -> List[Any]:
torch.manual_seed(0 )
_A = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , )
_A = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
_A = 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=1_28 , )
torch.manual_seed(0 )
_A = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='''gelu''' , projection_dim=5_12 , )
_A = CLIPTextModel(__A )
_A = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_A = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def __A ( self: Union[str, Any] , __A: List[Any] , __A: Tuple=0 ) -> int:
if str(__A ).startswith('''mps''' ):
_A = torch.manual_seed(__A )
else:
_A = torch.Generator(device=__A ).manual_seed(__A )
_A = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def __A ( self: List[Any] ) -> Dict:
_A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = TextToVideoSDPipeline(**__A )
_A = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs(__A )
_A = '''np'''
_A = sd_pipe(**__A ).frames
_A = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_A = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self: Dict ) -> str:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__A , expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __A ( self: Union[str, Any] ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__A , expected_max_diff=1e-2 )
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __A ( self: Any ) -> Optional[Any]:
pass
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __A ( self: Optional[Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
def __A ( self: Optional[int] ) -> List[str]:
pass
def __A ( self: List[Any] ) -> Dict:
return super().test_progress_bar()
@slow
@skip_mps
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: List[Any] ) -> str:
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' )
_A = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
_A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_A = pipe.to('''cuda''' )
_A = '''Spiderman is surfing'''
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe(__A , generator=__A , num_inference_steps=25 , output_type='''pt''' ).frames
_A = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def __A ( self: List[Any] ) -> Union[str, Any]:
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' )
_A = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
_A = pipe.to('''cuda''' )
_A = '''Spiderman is surfing'''
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe(__A , generator=__A , num_inference_steps=2 , output_type='''pt''' ).frames
_A = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: List[Any] ) -> Any:
_A = val
_A = None
_A = None
def __A ( self: Optional[Any] , __A: List[Any] ) -> str:
if self.val:
if val < self.val:
if self.left is None:
_A = Node(__A )
else:
self.left.insert(__A )
elif val > self.val:
if self.right is None:
_A = Node(__A )
else:
self.right.insert(__A )
else:
_A = val
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if root:
inorder(root.left , _lowercase )
res.append(root.val )
inorder(root.right , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return arr
_A = Node(arr[0] )
for i in range(1 , len(_lowercase ) ):
root.insert(arr[i] )
# Traverse BST in order.
_A = []
inorder(_lowercase , _lowercase )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self: List[Any] , __A: Dict[str, int] , __A: List[str] , __A: int = None , __A: int = None ) -> Any:
super().__init__()
_A = pad_token_id
_A = max_length
_A = vocab
_A = merges
_A = BytePairTokenizer(__A , __A , sequence_length=__A )
@classmethod
def __A ( cls: int , __A: GPTaTokenizer , *__A: Any , **__A: Optional[int] ) -> int:
_A = [''' '''.join(__A ) for m in tokenizer.bpe_ranks.keys()]
_A = tokenizer.get_vocab()
return cls(__A , __A , *__A , **__A )
@classmethod
def __A ( cls: Optional[int] , __A: Union[str, os.PathLike] , *__A: str , **__A: Tuple ) -> Optional[Any]:
_A = GPTaTokenizer.from_pretrained(__A , *__A , **__A )
return cls.from_tokenizer(__A , *__A , **__A )
@classmethod
def __A ( cls: int , __A: Tuple ) -> Tuple:
return cls(**__A )
def __A ( self: str ) -> List[Any]:
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: int = None ) -> Dict:
_A = self.tf_tokenizer(__A )
_A = tf.ones_like(__A )
if self.pad_token_id is not None:
# pad the tokens up to max length
_A = max_length if max_length is not None else self.max_length
if max_length is not None:
_A ,_A = pad_model_inputs(
__A , max_seq_length=__A , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
__A = logging.getLogger(__name__)
def __A ( _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
_A = bnb_quantization_config.load_in_abit
_A = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
'''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
''' make sure you have the latest version of `bitsandbytes` installed.''' )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
'''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
'''make sure you have the latest version of `bitsandbytes` installed.''' )
_A = []
# custom device map
if isinstance(_lowercase , _lowercase ) and len(device_map.keys() ) > 1:
_A = [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
_A = get_keys_to_not_convert(_lowercase )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(_lowercase )
_A = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
_A = []
_A = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(_lowercase )
# compatibility with peft
_A = load_in_abit
_A = load_in_abit
_A = get_parameter_device(_lowercase )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
'''It is not recommended to quantize a loaded model. '''
'''The model should be instantiated under the `init_empty_weights` context manager.''' )
_A = replace_with_bnb_layers(_lowercase , _lowercase , modules_to_not_convert=_lowercase )
# convert param to the right dtype
_A = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
_A = name.replace('''.weight''' , '''''' ).replace('''.bias''' , '''''' )
_A = getattr(_lowercase , _lowercase , _lowercase )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(_lowercase ):
param.to(_lowercase )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info(
f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
'''We move the model to cuda.''' )
return model
elif weights_location is None:
raise RuntimeError(
f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
_A = replace_with_bnb_layers(
_lowercase , _lowercase , modules_to_not_convert=_lowercase )
_A = get_quantized_model_device_map(
_lowercase , _lowercase , _lowercase , max_memory=_lowercase , no_split_module_classes=_lowercase , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
_A = True
_A = any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )
load_checkpoint_in_model(
_lowercase , _lowercase , _lowercase , dtype=bnb_quantization_config.torch_dtype , offload_folder=_lowercase , offload_state_dict=_lowercase , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(_lowercase , device_map=_lowercase , offload_dir=_lowercase )
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None ):
'''simple docstring'''
if device_map is None:
if torch.cuda.is_available():
_A = {'''''': torch.cuda.current_device()}
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )
if isinstance(_lowercase , _lowercase ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
'''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
'''\'sequential\'.''' )
_A = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
_A = {}
_A = special_dtypes
_A = no_split_module_classes
_A = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
_A = get_balanced_memory(
_lowercase , low_zero=(device_map == '''balanced_low_0''') , max_memory=_lowercase , **_lowercase , )
_A = max_memory
_A = infer_auto_device_map(_lowercase , **_lowercase )
if isinstance(_lowercase , _lowercase ):
# check if don't have any quantized module on the cpu
_A = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
_A = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
'''
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
''' )
else:
logger.info(
'''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
del device_map_without_some_modules
return device_map
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None ):
'''simple docstring'''
if modules_to_not_convert is None:
_A = []
_A ,_A = _replace_with_bnb_layers(
_lowercase , _lowercase , _lowercase , _lowercase )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , ):
'''simple docstring'''
_A = False
for name, module in model.named_children():
if current_key_name is None:
_A = []
current_key_name.append(_lowercase )
if isinstance(_lowercase , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
_A = '''.'''.join(_lowercase )
_A = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
_A = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
_A = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=_lowercase , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
_A = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
_A = module.weight.data
if module.bias is not None:
_A = module.bias.data
bnb_module.requires_grad_(_lowercase )
setattr(_lowercase , _lowercase , _lowercase )
_A = True
if len(list(module.children() ) ) > 0:
_A ,_A = _replace_with_bnb_layers(
_lowercase , _lowercase , _lowercase , _lowercase )
_A = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def __A ( _lowercase ):
'''simple docstring'''
with init_empty_weights():
_A = deepcopy(_lowercase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
_A = find_tied_parameters(_lowercase )
# For compatibility with Accelerate < 0.18
if isinstance(_lowercase , _lowercase ):
_A = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
_A = sum(_lowercase , [] )
_A = len(_lowercase ) > 0
# Check if it is a base model
_A = False
if hasattr(_lowercase , '''base_model_prefix''' ):
_A = not hasattr(_lowercase , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_A = list(model.named_children() )
_A = [list_modules[-1][0]]
# add last module together with tied weights
_A = set(_lowercase ) - set(_lowercase )
_A = list(set(_lowercase ) ) + list(_lowercase )
# remove ".weight" from the keys
_A = ['''.weight''', '''.bias''']
_A = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_A = name.replace(_lowercase , '''''' )
filtered_module_names.append(_lowercase )
return filtered_module_names
def __A ( _lowercase ):
'''simple docstring'''
for m in model.modules():
if isinstance(_lowercase , bnb.nn.Linearabit ):
return True
return False
def __A ( _lowercase ):
'''simple docstring'''
return next(parameter.parameters() ).device
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if fpaa_statistics is None:
set_module_tensor_to_device(_lowercase , _lowercase , 0 , dtype=_lowercase , value=_lowercase )
_A = param_name
_A = model
if "." in tensor_name:
_A = tensor_name.split('''.''' )
for split in splits[:-1]:
_A = getattr(_lowercase , _lowercase )
if new_module is None:
raise ValueError(f"""{module} has no attribute {split}.""" )
_A = new_module
_A = splits[-1]
# offload weights
_A = False
offload_weight(module._parameters[tensor_name] , _lowercase , _lowercase , index=_lowercase )
if hasattr(module._parameters[tensor_name] , '''SCB''' ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace('''weight''' , '''SCB''' ) , _lowercase , index=_lowercase , )
else:
offload_weight(_lowercase , _lowercase , _lowercase , index=_lowercase )
offload_weight(_lowercase , param_name.replace('''weight''' , '''SCB''' ) , _lowercase , index=_lowercase )
set_module_tensor_to_device(_lowercase , _lowercase , '''meta''' , dtype=_lowercase , value=torch.empty(*param.size() ) )
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@staticmethod
def __A ( *__A: int , **__A: List[Any] ) -> str:
pass
def __A ( _lowercase ):
'''simple docstring'''
_A = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
A_ = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __A ( self: Optional[Any] , __A: str , __A: Dict , __A: Tuple ) -> Tuple:
_A = DepthEstimationPipeline(model=__A , image_processor=__A )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __A ( self: Tuple , __A: Tuple , __A: Union[str, Any] ) -> str:
_A = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , __A )
import datasets
_A = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
_A = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , __A , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def __A ( self: Any ) -> Optional[Any]:
pass
@slow
@require_torch
def __A ( self: List[Any] ) -> Tuple:
_A = '''Intel/dpt-large'''
_A = pipeline('''depth-estimation''' , model=__A )
_A = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
_A = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.662 )
@require_torch
def __A ( self: List[Any] ) -> Optional[Any]:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 62 |
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 = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__A = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
__A = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def __A ( _lowercase ):
'''simple docstring'''
_A = (images / 2 + 0.5).clamp(0 , 1 )
_A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_A = numpy_to_pil(_lowercase )
return images
def __A ( _lowercase ):
'''simple docstring'''
if images.ndim == 3:
_A = images[None, ...]
_A = (images * 2_55).round().astype('''uint8''' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
_A = [Image.fromarray(image.squeeze() , mode='''L''' ) for image in images]
else:
_A = [Image.fromarray(_lowercase ) for image in images]
return pil_images
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def __A ( _lowercase ):
'''simple docstring'''
if hor == 1_28:
_A = ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
_A = (32, 1_28, 2_56)
_A = ('''UpResnetBlock1D''', '''UpResnetBlock1D''')
elif hor == 32:
_A = ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
_A = (32, 64, 1_28, 2_56)
_A = ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
_A = torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
_A = model.state_dict()
_A = {
'''down_block_types''': down_block_types,
'''block_out_channels''': block_out_channels,
'''up_block_types''': up_block_types,
'''layers_per_block''': 1,
'''use_timestep_embedding''': True,
'''out_block_type''': '''OutConv1DBlock''',
'''norm_num_groups''': 8,
'''downsample_each_block''': False,
'''in_channels''': 14,
'''out_channels''': 14,
'''extra_in_channels''': 0,
'''time_embedding_type''': '''positional''',
'''flip_sin_to_cos''': False,
'''freq_shift''': 1,
'''sample_size''': 6_55_36,
'''mid_block_type''': '''MidResTemporalBlock1D''',
'''act_fn''': '''mish''',
}
_A = UNetaDModel(**_lowercase )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
_A = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
_A = state_dict.pop(_lowercase )
hf_value_function.load_state_dict(_lowercase )
torch.save(hf_value_function.state_dict() , f"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(f"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , '''w''' ) as f:
json.dump(_lowercase , _lowercase )
def __A ( ):
'''simple docstring'''
_A = {
'''in_channels''': 14,
'''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
'''up_block_types''': (),
'''out_block_type''': '''ValueFunction''',
'''mid_block_type''': '''ValueFunctionMidBlock1D''',
'''block_out_channels''': (32, 64, 1_28, 2_56),
'''layers_per_block''': 1,
'''downsample_each_block''': True,
'''sample_size''': 6_55_36,
'''out_channels''': 14,
'''extra_in_channels''': 0,
'''time_embedding_type''': '''positional''',
'''use_timestep_embedding''': True,
'''flip_sin_to_cos''': False,
'''freq_shift''': 1,
'''norm_num_groups''': 8,
'''act_fn''': '''mish''',
}
_A = torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch''' )
_A = model
_A = UNetaDModel(**_lowercase )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
_A = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
_A = state_dict.pop(_lowercase )
hf_value_function.load_state_dict(_lowercase )
torch.save(hf_value_function.state_dict() , '''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin''' )
with open('''hub/hopper-medium-v2/value_function/config.json''' , '''w''' ) as f:
json.dump(_lowercase , _lowercase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 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 = {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "xlm-roberta"
def __init__( self: List[Any] , __A: List[str]=3_05_22 , __A: Optional[Any]=7_68 , __A: str=12 , __A: Dict=12 , __A: int=30_72 , __A: Union[str, Any]="gelu" , __A: Tuple=0.1 , __A: Dict=0.1 , __A: List[Any]=5_12 , __A: int=2 , __A: Any=0.02 , __A: Optional[int]=1e-12 , __A: Union[str, Any]=1 , __A: Dict=0 , __A: Optional[Any]=2 , __A: Dict="absolute" , __A: List[str]=True , __A: List[str]=None , **__A: Any , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __A ( _lowercase ):
'''simple docstring'''
_A = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(_lowercase , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A ,_A = emb.weight.shape
_A = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
_A = emb.weight.data
return lin_layer
def __A ( _lowercase ):
'''simple docstring'''
_A = torch.load(_lowercase , map_location='''cpu''' )
_A = Namespace(**checkpoint['''cfg''']['''model'''] )
_A = checkpoint['''model''']
remove_ignore_keys_(_lowercase )
_A = state_dict['''decoder.embed_tokens.weight'''].shape[0]
_A = {key.replace('''decoder''' , '''model''' ): val for key, val in state_dict.items()}
_A = XGLMConfig(
vocab_size=_lowercase , max_position_embeddings=args.max_target_positions , num_layers=args.decoder_layers , attention_heads=args.decoder_attention_heads , ffn_dim=args.decoder_ffn_embed_dim , d_model=args.decoder_embed_dim , layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''gelu''' , scale_embedding=not args.no_scale_embedding , tie_word_embeddings=args.share_decoder_input_output_embed , )
_A = XGLMForCausalLM(_lowercase )
_A = model.load_state_dict(_lowercase , strict=_lowercase )
print(_lowercase )
_A = make_linear_from_emb(model.model.embed_tokens )
return model
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
__A = parser.parse_args()
__A = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
model.save_pretrained(args.pytorch_dump_folder_path)
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
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