code
stringlengths 86
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| code_codestyle
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| style_context
stringlengths 87
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| style_context_codestyle
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|---|---|---|---|---|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
if digit_amount > 0:
return round(number - int(UpperCAmelCase_ ) , UpperCAmelCase_ )
return number - int(UpperCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal_isolate(35.3_45, 2))
print(decimal_isolate(35.3_45, 3))
print(decimal_isolate(-14.7_89, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.1_23, 1))
print(decimal_isolate(-14.1_23, 2))
print(decimal_isolate(-14.1_23, 3))
| 353
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 313
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : List[Any] = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Optional[int] = [
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 354
|
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=6_4 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=3_7 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Dict=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = MegatronBertModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MegatronBertForMaskedLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = MegatronBertForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForNextSentencePrediction(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForPreTraining(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , next_sentence_label=lowerCAmelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = MegatronBertForQuestionAnswering(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForTokenClassification(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MegatronBertForMultipleChoice(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
# test_resize_embeddings = False
lowercase__ = False
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
if return_labels:
if model_class in get_values(lowerCAmelCase_):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_)
return inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = MegatronBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return torch.tensor(
__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase , )
UpperCAmelCase : Any = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
@unittest.skip("""Model is not available.""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ = os.path.join(os.environ["""MYDIR"""] , lowerCAmelCase_)
lowercase_ = MegatronBertModel.from_pretrained(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.half()
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = torch.Size((1, 9, 1_0_2_4))
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3):
for jj in range(3):
lowercase_ = output[0, ii, jj]
lowercase_ = expected[3 * ii + jj]
lowercase_ = """ii={} jj={} a={} b={}""".format(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
self.assertTrue(math.isclose(lowerCAmelCase_ , lowerCAmelCase_ , rel_tol=lowerCAmelCase_ , abs_tol=lowerCAmelCase_) , msg=lowerCAmelCase_)
| 313
| 0
|
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self : List[Any] , lowerCAmelCase_ : Any = 1_2_8 , lowerCAmelCase_ : Union[str, Any] = 2_5_6 , lowerCAmelCase_ : Optional[Any] = 2_000.0 , lowerCAmelCase_ : Tuple = 7_6_8 , lowerCAmelCase_ : Dict = 1_2 , lowerCAmelCase_ : int = 1_2 , lowerCAmelCase_ : str = 6_4 , lowerCAmelCase_ : Any = 2_0_4_8 , lowerCAmelCase_ : List[Any] = 0.1 , ):
"""simple docstring"""
super().__init__()
lowercase_ = nn.Sequential(
nn.Linear(lowerCamelCase_ , d_model * 4 , bias=lowerCamelCase_) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=lowerCamelCase_) , nn.SiLU() , )
lowercase_ = nn.Embedding(lowerCamelCase_ , lowerCamelCase_)
lowercase_ = False
lowercase_ = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_)
lowercase_ = nn.Dropout(p=lowerCamelCase_)
lowercase_ = nn.ModuleList()
for lyr_num in range(lowerCamelCase_):
# FiLM conditional T5 decoder
lowercase_ = DecoderLayer(d_model=lowerCamelCase_ , d_kv=lowerCamelCase_ , num_heads=lowerCamelCase_ , d_ff=lowerCamelCase_ , dropout_rate=lowerCamelCase_)
self.decoders.append(lowerCamelCase_)
lowercase_ = TaLayerNorm(lowerCamelCase_)
lowercase_ = nn.Dropout(p=lowerCamelCase_)
lowercase_ = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = torch.mul(query_input.unsqueeze(-1) , key_input.unsqueeze(-2))
return mask.unsqueeze(-3)
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
lowercase_ = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype)
lowercase_ = self.conditioning_emb(lowerCamelCase_).unsqueeze(1)
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
lowercase_ = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
lowercase_ = torch.broadcast_to(
torch.arange(lowerCamelCase_ , device=decoder_input_tokens.device) , (batch, seq_length) , )
lowercase_ = self.position_encoding(lowerCamelCase_)
lowercase_ = self.continuous_inputs_projection(lowerCamelCase_)
inputs += position_encodings
lowercase_ = self.dropout(lowerCamelCase_)
# decoder: No padding present.
lowercase_ = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype)
# Translate encoding masks to encoder-decoder masks.
lowercase_ = [(x, self.encoder_decoder_mask(lowerCamelCase_ , lowerCamelCase_)) for x, y in encodings_and_masks]
# cross attend style: concat encodings
lowercase_ = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1)
lowercase_ = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1)
for lyr in self.decoders:
lowercase_ = lyr(
lowerCamelCase_ , conditioning_emb=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , )[0]
lowercase_ = self.decoder_norm(lowerCamelCase_)
lowercase_ = self.post_dropout(lowerCamelCase_)
lowercase_ = self.spec_out(lowerCamelCase_)
return spec_out
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : int=1E-6):
"""simple docstring"""
super().__init__()
lowercase_ = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=lowerCamelCase_ , d_kv=lowerCamelCase_ , num_heads=lowerCamelCase_ , dropout_rate=lowerCamelCase_))
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=lowerCamelCase_ , d_kv=lowerCamelCase_ , num_heads=lowerCamelCase_ , dropout_rate=lowerCamelCase_ , layer_norm_epsilon=lowerCamelCase_ , ))
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=lowerCamelCase_ , d_ff=lowerCamelCase_ , dropout_rate=lowerCamelCase_ , layer_norm_epsilon=lowerCamelCase_))
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Any=None , ):
"""simple docstring"""
lowercase_ = self.layer[0](
lowerCamelCase_ , conditioning_emb=lowerCamelCase_ , attention_mask=lowerCamelCase_ , )
if encoder_hidden_states is not None:
lowercase_ = torch.where(encoder_attention_mask > 0 , 0 , -1E10).to(
encoder_hidden_states.dtype)
lowercase_ = self.layer[1](
lowerCamelCase_ , key_value_states=lowerCamelCase_ , attention_mask=lowerCamelCase_ , )
# Apply Film Conditional Feed Forward layer
lowercase_ = self.layer[-1](lowerCamelCase_ , lowerCamelCase_)
return (hidden_states,)
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple):
"""simple docstring"""
super().__init__()
lowercase_ = TaLayerNorm(lowerCamelCase_)
lowercase_ = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCamelCase_)
lowercase_ = Attention(query_dim=lowerCamelCase_ , heads=lowerCamelCase_ , dim_head=lowerCamelCase_ , out_bias=lowerCamelCase_ , scale_qk=lowerCamelCase_)
lowercase_ = nn.Dropout(lowerCamelCase_)
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Any=None , ):
"""simple docstring"""
lowercase_ = self.layer_norm(lowerCamelCase_)
if conditioning_emb is not None:
lowercase_ = self.FiLMLayer(lowerCamelCase_ , lowerCamelCase_)
# Self-attention block
lowercase_ = self.attention(lowerCamelCase_)
lowercase_ = hidden_states + self.dropout(lowerCamelCase_)
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int):
"""simple docstring"""
super().__init__()
lowercase_ = Attention(query_dim=lowerCamelCase_ , heads=lowerCamelCase_ , dim_head=lowerCamelCase_ , out_bias=lowerCamelCase_ , scale_qk=lowerCamelCase_)
lowercase_ = TaLayerNorm(lowerCamelCase_ , eps=lowerCamelCase_)
lowercase_ = nn.Dropout(lowerCamelCase_)
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Optional[int]=None , ):
"""simple docstring"""
lowercase_ = self.layer_norm(lowerCamelCase_)
lowercase_ = self.attention(
lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , attention_mask=attention_mask.squeeze(1) , )
lowercase_ = hidden_states + self.dropout(lowerCamelCase_)
return layer_output
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__()
lowercase_ = TaDenseGatedActDense(d_model=lowerCamelCase_ , d_ff=lowerCamelCase_ , dropout_rate=lowerCamelCase_)
lowercase_ = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCamelCase_)
lowercase_ = TaLayerNorm(lowerCamelCase_ , eps=lowerCamelCase_)
lowercase_ = nn.Dropout(lowerCamelCase_)
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = self.layer_norm(lowerCamelCase_)
if conditioning_emb is not None:
lowercase_ = self.film(lowerCamelCase_ , lowerCamelCase_)
lowercase_ = self.DenseReluDense(lowerCamelCase_)
lowercase_ = hidden_states + self.dropout(lowerCamelCase_)
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
super().__init__()
lowercase_ = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_)
lowercase_ = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_)
lowercase_ = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_)
lowercase_ = nn.Dropout(lowerCamelCase_)
lowercase_ = NewGELUActivation()
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.act(self.wi_a(lowerCamelCase_))
lowercase_ = self.wi_a(lowerCamelCase_)
lowercase_ = hidden_gelu * hidden_linear
lowercase_ = self.dropout(lowerCamelCase_)
lowercase_ = self.wo(lowerCamelCase_)
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any]=1E-6):
"""simple docstring"""
super().__init__()
lowercase_ = nn.Parameter(torch.ones(lowerCamelCase_))
lowercase_ = eps
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = hidden_states.to(torch.floataa).pow(2).mean(-1 , keepdim=lowerCamelCase_)
lowercase_ = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
lowercase_ = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044_715 * torch.pow(lowerCamelCase_ , 3.0))))
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
super().__init__()
lowercase_ = nn.Linear(lowerCamelCase_ , out_features * 2 , bias=lowerCamelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = self.scale_bias(lowerCamelCase_)
lowercase_ , lowercase_ = torch.chunk(lowerCamelCase_ , 2 , -1)
lowercase_ = x * (1 + scale) + shift
return x
| 355
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows != columns:
lowercase_ = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__lowerCAmelCase )
lowercase_ = np.zeros((rows, columns) )
lowercase_ = np.zeros((rows, columns) )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase_ = (table[i][j] - total) / upper[j][j]
lowercase_ = 1
for j in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
lowercase_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _SCREAMING_SNAKE_CASE () -> Dict:
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
lowercase_ = os.path.join(__UpperCAmelCase , """words.txt""" )
lowercase_ = """"""
with open(__UpperCAmelCase ) as f:
lowercase_ = f.readline()
lowercase_ = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
lowercase_ = [
word
for word in [sum(ord(__UpperCAmelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__UpperCAmelCase )
if __name__ == "__main__":
print(solution())
| 356
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [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(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Any = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip2VisionConfig''',
],
'''processing_blip_2''': ['''Blip2Processor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = [
'''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Blip2Model''',
'''Blip2QFormerModel''',
'''Blip2PreTrainedModel''',
'''Blip2ForConditionalGeneration''',
'''Blip2VisionModel''',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 357
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , 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]
| 313
| 0
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]: # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def _SCREAMING_SNAKE_CASE () -> Dict:
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
lowercase_ = [1, 2, 3]
with pytest.raises(A_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(A_ , A_ , num_proc=2 )
with pytest.raises(A_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(A_ , A_ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" , [2, -1] )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = [1, 2]
lowercase_ = {'''a''': 1, '''b''': 2}
lowercase_ = {'''a''': [1, 2], '''b''': [3, 4]}
lowercase_ = {'''a''': {'''1''': 1}, '''b''': 2}
lowercase_ = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
lowercase_ = [2, 3]
lowercase_ = {'''a''': 2, '''b''': 3}
lowercase_ = {'''a''': [2, 3], '''b''': [4, 5]}
lowercase_ = {'''a''': {'''1''': 2}, '''b''': 3}
lowercase_ = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
with parallel_backend("""spark""" ):
assert map_nested(A_ , A_ , num_proc=A_ ) == expected_map_nested_sa
assert map_nested(A_ , A_ , num_proc=A_ ) == expected_map_nested_sa
assert map_nested(A_ , A_ , num_proc=A_ ) == expected_map_nested_sa
assert map_nested(A_ , A_ , num_proc=A_ ) == expected_map_nested_sa
assert map_nested(A_ , A_ , num_proc=A_ ) == expected_map_nested_sa
| 358
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Dict=9_9 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : str=4 , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : List[Any]=0.02 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
lowercase_ = initializer_range
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowercase_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase_ , )
lowercase_ = prepare_blenderbot_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = 99
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
lowercase_ = input_ids.shape[0]
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._get_config_and_data()
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = lm_model(input_ids=lowerCAmelCase_)
lowercase_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa)
lowercase_ = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa)
lowercase_ = lm_model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_)
lowercase_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(lowerCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowercase__ = True
lowercase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = FlaxBlenderbotModelTester(self)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : str):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase_ = np.ones((1, 1)) * model.config.eos_token_id
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""")
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
lowercase_ = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
lowercase_ = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCAmelCase_)
lowercase_ = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")
lowercase_ = ["""Sam"""]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""jax""")
lowercase_ = model.generate(**lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = """Sam is a great name. It means \"sun\" in Gaelic."""
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , **lowerCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 313
| 0
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a \'int\' type""" )
return bin(_lowercase ).count("""1""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313
| 0
|
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
UpperCAmelCase : Optional[int] = (
"https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
UpperCAmelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
'''simple docstring'''
lowercase_ = """https://pypi.org/pypi/diffusers/json"""
lowercase_ = json.loads(request.urlopen(__lowerCAmelCase ).read() )["""releases"""].keys()
return sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : version.Version(__lowerCAmelCase ) )
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
lowercase_ = Path(__lowerCAmelCase ) / """__init__.py"""
if not init_path.exists():
init_path.touch()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
init_hf_modules()
lowercase_ = Path(__lowerCAmelCase ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
lowercase_ = dynamic_module_path / """__init__.py"""
if not init_path.exists():
init_path.touch()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Dict:
'''simple docstring'''
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.read()
# Imports of the form `import .xxx`
lowercase_ = re.findall("""^\s*import\s+\.(\S+)\s*$""" , __lowerCAmelCase , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import""" , __lowerCAmelCase , flags=re.MULTILINE )
# Unique-ify
return list(set(__lowerCAmelCase ) )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = False
lowercase_ = [module_file]
lowercase_ = []
# Let's recurse through all relative imports
while not no_change:
lowercase_ = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__lowerCAmelCase ) )
lowercase_ = Path(__lowerCAmelCase ).parent
lowercase_ = [str(module_path / m ) for m in new_imports]
lowercase_ = [f for f in new_import_files if f not in all_relative_imports]
lowercase_ = [F'''{f}.py''' for f in new_import_files]
lowercase_ = len(__lowerCAmelCase ) == 0
all_relative_imports.extend(__lowerCAmelCase )
return all_relative_imports
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.read()
# Imports of the form `import xxx`
lowercase_ = re.findall("""^\s*import\s+(\S+)\s*$""" , __lowerCAmelCase , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("""^\s*from\s+(\S+)\s+import""" , __lowerCAmelCase , flags=re.MULTILINE )
# Only keep the top-level module
lowercase_ = [imp.split(""".""" )[0] for imp in imports if not imp.startswith(""".""" )]
# Unique-ify and test we got them all
lowercase_ = list(set(__lowerCAmelCase ) )
lowercase_ = []
for imp in imports:
try:
importlib.import_module(__lowerCAmelCase )
except ImportError:
missing_packages.append(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
raise ImportError(
"""This modeling file requires the following packages that were not found in your environment: """
F'''{", ".join(__lowerCAmelCase )}. Run `pip install {" ".join(__lowerCAmelCase )}`''' )
return get_relative_imports(__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = module_path.replace(os.path.sep , """.""" )
lowercase_ = importlib.import_module(__lowerCAmelCase )
if class_name is None:
return find_pipeline_class(__lowerCAmelCase )
return getattr(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
from ..pipelines import DiffusionPipeline
lowercase_ = dict(inspect.getmembers(__lowerCAmelCase , inspect.isclass ) )
lowercase_ = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __lowerCAmelCase )
and cls.__module__.split(""".""" )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowercase_ = cls
return pipeline_class
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , ) -> Dict:
'''simple docstring'''
lowercase_ = str(__lowerCAmelCase )
lowercase_ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.isfile(__lowerCAmelCase ):
lowercase_ = module_file_or_url
lowercase_ = """local"""
elif pretrained_model_name_or_path.count("""/""" ) == 0:
lowercase_ = get_diffusers_versions()
# cut ".dev0"
lowercase_ = """v""" + """.""".join(__version__.split(""".""" )[:3] )
# retrieve github version that matches
if revision is None:
lowercase_ = latest_version if latest_version[1:] in available_versions else """main"""
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowercase_ = F'''v{revision}'''
elif revision == "main":
lowercase_ = revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {", ".join(available_versions + ["main"] )}.''' )
# community pipeline on GitHub
lowercase_ = COMMUNITY_PIPELINES_URL.format(revision=__lowerCAmelCase , pipeline=__lowerCAmelCase )
try:
lowercase_ = cached_download(
__lowerCAmelCase , cache_dir=__lowerCAmelCase , force_download=__lowerCAmelCase , proxies=__lowerCAmelCase , resume_download=__lowerCAmelCase , local_files_only=__lowerCAmelCase , use_auth_token=__lowerCAmelCase , )
lowercase_ = """git"""
lowercase_ = pretrained_model_name_or_path + """.py"""
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowercase_ = hf_hub_download(
__lowerCAmelCase , __lowerCAmelCase , cache_dir=__lowerCAmelCase , force_download=__lowerCAmelCase , proxies=__lowerCAmelCase , resume_download=__lowerCAmelCase , local_files_only=__lowerCAmelCase , use_auth_token=__lowerCAmelCase , )
lowercase_ = os.path.join("""local""" , """--""".join(pretrained_model_name_or_path.split("""/""" ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowercase_ = check_imports(__lowerCAmelCase )
# Now we move the module inside our cached dynamic modules.
lowercase_ = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__lowerCAmelCase )
lowercase_ = Path(__lowerCAmelCase ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__lowerCAmelCase , submodule_path / module_file )
for module_needed in modules_needed:
lowercase_ = F'''{module_needed}.py'''
shutil.copy(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = use_auth_token
elif use_auth_token is True:
lowercase_ = HfFolder.get_token()
else:
lowercase_ = None
lowercase_ = model_info(__lowerCAmelCase , revision=__lowerCAmelCase , token=__lowerCAmelCase ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowercase_ = submodule_path / commit_hash
lowercase_ = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__lowerCAmelCase )
if not (submodule_path / module_file).exists():
shutil.copy(__lowerCAmelCase , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__lowerCAmelCase , F'''{module_needed}.py''' , cache_dir=__lowerCAmelCase , force_download=__lowerCAmelCase , resume_download=__lowerCAmelCase , proxies=__lowerCAmelCase , use_auth_token=__lowerCAmelCase , revision=__lowerCAmelCase , local_files_only=__lowerCAmelCase , )
return os.path.join(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , **__lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
lowercase_ = get_cached_module_file(
__lowerCAmelCase , __lowerCAmelCase , cache_dir=__lowerCAmelCase , force_download=__lowerCAmelCase , resume_download=__lowerCAmelCase , proxies=__lowerCAmelCase , use_auth_token=__lowerCAmelCase , revision=__lowerCAmelCase , local_files_only=__lowerCAmelCase , )
return get_class_in_module(__lowerCAmelCase , final_module.replace(""".py""" , """""" ) )
| 360
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 313
| 0
|
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Union[str, Any] , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : Tuple):
"""simple docstring"""
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_)
| 361
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 313
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE__ :
def __init__( self : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : Optional[Any]=1_0 , lowerCAmelCase_ : Any=[8, 1_6, 3_2, 6_4] , lowerCAmelCase_ : Tuple=[1, 1, 2, 1] , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Tuple="relu" , lowerCAmelCase_ : Dict=3 , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Dict=["stage2", "stage3", "stage4"] , lowerCAmelCase_ : Any=[2, 3, 4] , lowerCAmelCase_ : int=1 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = embeddings_size
lowercase_ = hidden_sizes
lowercase_ = depths
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = hidden_act
lowercase_ = num_labels
lowercase_ = scope
lowercase_ = len(snake_case__)
lowercase_ = out_features
lowercase_ = out_indices
lowercase_ = num_groups
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.num_labels)
lowercase_ = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = BitModel(config=snake_case__)
model.to(snake_case__)
model.eval()
lowercase_ = model(snake_case__)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = BitForImageClassification(snake_case__)
model.to(snake_case__)
model.eval()
lowercase_ = model(snake_case__ , labels=snake_case__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = BitBackbone(config=snake_case__)
model.to(snake_case__)
model.eval()
lowercase_ = model(snake_case__)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])
# verify backbone works with out_features=None
lowercase_ = None
lowercase_ = BitBackbone(config=snake_case__)
model.to(snake_case__)
model.eval()
lowercase_ = model(snake_case__)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ = config_and_inputs
lowercase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase__ = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ = (
{"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = BitModelTester(self)
lowercase_ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return
@unittest.skip(reason="""Bit does not output attentions""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass
@unittest.skip(reason="""Bit does not use inputs_embeds""")
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
pass
@unittest.skip(reason="""Bit does not support input and output embeddings""")
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(snake_case__)
lowercase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case__)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case__)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(config=snake_case__)
for name, module in model.named_modules():
if isinstance(snake_case__ , (nn.BatchNormad, nn.GroupNorm)):
self.assertTrue(
torch.all(module.weight == 1) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any]):
lowercase_ = model_class(snake_case__)
model.to(snake_case__)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(snake_case__ , snake_case__))
lowercase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ = self.model_tester.num_stages
self.assertEqual(len(snake_case__) , expected_num_stages + 1)
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ = layer_type
lowercase_ = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__)
@unittest.skip(reason="""Bit does not use feedforward chunking""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = BitModel.from_pretrained(snake_case__)
self.assertIsNotNone(snake_case__)
def _SCREAMING_SNAKE_CASE () -> Tuple:
'''simple docstring'''
lowercase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(snake_case__)
lowercase_ = self.default_image_processor
lowercase_ = prepare_img()
lowercase_ = image_processor(images=snake_case__ , return_tensors="""pt""").to(snake_case__)
# forward pass
with torch.no_grad():
lowercase_ = model(**snake_case__)
# verify the logits
lowercase_ = torch.Size((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , snake_case__)
lowercase_ = torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]).to(snake_case__)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4))
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = (BitBackbone,) if is_torch_available() else ()
lowercase__ = BitConfig
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = BitModelTester(self)
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
"""simple docstring"""
from math import ceil, sqrt
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1_00_00_00 ) -> int:
'''simple docstring'''
lowercase_ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase_ = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
lowercase_ = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F"{solution() = }")
| 363
|
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase_ = 0 if allow_empty_subarrays else float("""-inf""" )
lowercase_ = 0.0
for num in arr:
lowercase_ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowercase_ = max(__lowerCAmelCase , __lowerCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : Union[str, Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 313
| 0
|
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
def merge(__lowerCAmelCase , __lowerCAmelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(__lowerCAmelCase ) <= 1:
return collection
lowercase_ = len(__lowerCAmelCase ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : int = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase : Dict = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 364
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 313
| 0
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
lowercase__ = field(default="summarization" , metadata={"include_in_asdict_even_if_is_default": True} )
lowercase__ = Features({"text": Value("string" )} )
lowercase__ = Features({"summary": Value("string" )} )
lowercase__ = "text"
lowercase__ = "summary"
@property
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
return {self.text_column: "text", self.summary_column: "summary"}
| 365
|
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x20000 and cp <= 0x2a6df) #
or (cp >= 0x2a700 and cp <= 0x2b73f) #
or (cp >= 0x2b740 and cp <= 0x2b81f) #
or (cp >= 0x2b820 and cp <= 0x2ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2f800 and cp <= 0x2fa1f) #
): #
return True
return False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
for char in word:
lowercase_ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase_ = set()
for token in tokens:
lowercase_ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowercase_ = list(__lowerCAmelCase )
return word_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
lowercase_ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowercase_ = bert_tokens
lowercase_ , lowercase_ = 0, len(__lowerCAmelCase )
while start < end:
lowercase_ = True
if is_chinese(bert_word[start] ):
lowercase_ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowercase_ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowercase_ = """##""" + bert_word[j]
lowercase_ = start + i
lowercase_ = False
break
if single_word:
start += 1
return bert_word
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
lowercase_ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=5_12 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = []
for id in input_ids:
lowercase_ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowercase_ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowercase_ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.readlines()
lowercase_ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowercase_ = LTP(args.ltp ) # faster in GPU device
lowercase_ = BertTokenizer.from_pretrained(args.bert )
lowercase_ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowercase_ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
UpperCAmelCase : int = parser.parse_args()
main(args)
| 313
| 0
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
# General docstring
UpperCAmelCase : Optional[int] = 'MobileNetV1Config'
# Base docstring
UpperCAmelCase : Optional[Any] = 'google/mobilenet_v1_1.0_224'
UpperCAmelCase : Any = [1, 1024, 7, 7]
# Image classification docstring
UpperCAmelCase : Dict = 'google/mobilenet_v1_1.0_224'
UpperCAmelCase : List[str] = 'tabby, tabby cat'
UpperCAmelCase : str = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Dict:
'''simple docstring'''
lowercase_ = {}
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowercase_ = model.mobilenet_va
else:
lowercase_ = model
lowercase_ = """MobilenetV1/Conv2d_0/"""
lowercase_ = backbone.conv_stem.convolution.weight
lowercase_ = backbone.conv_stem.normalization.bias
lowercase_ = backbone.conv_stem.normalization.weight
lowercase_ = backbone.conv_stem.normalization.running_mean
lowercase_ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
lowercase_ = i + 1
lowercase_ = i * 2
lowercase_ = backbone.layer[pt_index]
lowercase_ = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ = pointer.convolution.weight
lowercase_ = pointer.normalization.bias
lowercase_ = pointer.normalization.weight
lowercase_ = pointer.normalization.running_mean
lowercase_ = pointer.normalization.running_var
lowercase_ = backbone.layer[pt_index + 1]
lowercase_ = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ = pointer.convolution.weight
lowercase_ = pointer.normalization.bias
lowercase_ = pointer.normalization.weight
lowercase_ = pointer.normalization.running_mean
lowercase_ = pointer.normalization.running_var
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowercase_ = """MobilenetV1/Logits/Conv2d_1c_1x1/"""
lowercase_ = model.classifier.weight
lowercase_ = model.classifier.bias
return tf_to_pt_map
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"""Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
"""https://www.tensorflow.org/install/ for installation instructions.""" )
raise
# Load weights from TF model
lowercase_ = tf.train.list_variables(lowerCAmelCase__ )
lowercase_ = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ = tf.train.load_variable(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase_ = array
# Build TF to PyTorch weights loading map
lowercase_ = _build_tf_to_pytorch_map(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ = tf_weights[name]
if "depthwise_weights" in name:
logger.info("""Transposing depthwise""" )
lowercase_ = np.transpose(lowerCAmelCase__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("""Transposing""" )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ = array.squeeze().transpose()
else:
lowercase_ = np.transpose(lowerCAmelCase__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ = torch.from_numpy(lowerCAmelCase__ )
tf_weights.pop(lowerCAmelCase__ , lowerCAmelCase__ )
tf_weights.pop(name + """/RMSProp""" , lowerCAmelCase__ )
tf_weights.pop(name + """/RMSProp_1""" , lowerCAmelCase__ )
tf_weights.pop(name + """/ExponentialMovingAverage""" , lowerCAmelCase__ )
logger.info(F'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}''' )
return model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = features.shape[-2:]
lowercase_ = conv_layer.stride
lowercase_ = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ = max(kernel_height - stride_height , 0 )
else:
lowercase_ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ = max(kernel_width - stride_width , 0 )
else:
lowercase_ = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ = pad_along_width // 2
lowercase_ = pad_along_width - pad_left
lowercase_ = pad_along_height // 2
lowercase_ = pad_along_height - pad_top
lowercase_ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(lowerCAmelCase__ , lowerCAmelCase__ , """constant""" , 0.0 )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[Any] = 1 , lowerCAmelCase_ : Optional[int] = 1 , lowerCAmelCase_ : Any = False , lowerCAmelCase_ : List[Any] = True , lowerCAmelCase_ : Union[str, Any] = True , ):
"""simple docstring"""
super().__init__()
lowercase_ = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''')
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''')
lowercase_ = 0 if config.tf_padding else int((kernel_size - 1) / 2)
lowercase_ = nn.Convad(
in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , kernel_size=SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ , padding_mode="""zeros""" , )
if use_normalization:
lowercase_ = nn.BatchNormad(
num_features=SCREAMING_SNAKE_CASE_ , eps=config.layer_norm_eps , momentum=0.9_997 , affine=SCREAMING_SNAKE_CASE_ , track_running_stats=SCREAMING_SNAKE_CASE_ , )
else:
lowercase_ = None
if use_activation:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
lowercase_ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , SCREAMING_SNAKE_CASE_):
lowercase_ = ACTaFN[config.hidden_act]
else:
lowercase_ = config.hidden_act
else:
lowercase_ = None
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : str):
"""simple docstring"""
if self.config.tf_padding:
lowercase_ = apply_tf_padding(SCREAMING_SNAKE_CASE_ , self.convolution)
lowercase_ = self.convolution(SCREAMING_SNAKE_CASE_)
if self.normalization is not None:
lowercase_ = self.normalization(SCREAMING_SNAKE_CASE_)
if self.activation is not None:
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_)
return features
class SCREAMING_SNAKE_CASE__ ( _lowerCAmelCase ):
lowercase__ = MobileNetVaConfig
lowercase__ = load_tf_weights_in_mobilenet_va
lowercase__ = "mobilenet_v1"
lowercase__ = "pixel_values"
lowercase__ = False
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : int):
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , (nn.Linear, nn.Convad)):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE_ , nn.BatchNormad):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
UpperCAmelCase : Union[str, Any] = r'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
UpperCAmelCase : int = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _lowerCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( _lowerCAmelCase ):
def __init__( self : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple = True):
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE_)
lowercase_ = config
lowercase_ = 3_2
lowercase_ = max(int(depth * config.depth_multiplier) , config.min_depth)
lowercase_ = MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE_ , in_channels=config.num_channels , out_channels=SCREAMING_SNAKE_CASE_ , kernel_size=3 , stride=2 , )
lowercase_ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ = nn.ModuleList()
for i in range(1_3):
lowercase_ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ = max(int(depth * config.depth_multiplier) , config.min_depth)
self.layer.append(
MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , kernel_size=3 , stride=strides[i] , groups=SCREAMING_SNAKE_CASE_ , ))
self.layer.append(
MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , kernel_size=1 , ))
lowercase_ = nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[Any] = None , lowerCAmelCase_ : Tuple = None , lowerCAmelCase_ : Tuple = None , ):
"""simple docstring"""
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""")
lowercase_ = self.conv_stem(SCREAMING_SNAKE_CASE_)
lowercase_ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_)
if output_hidden_states:
lowercase_ = all_hidden_states + (hidden_states,)
lowercase_ = hidden_states
if self.pooler is not None:
lowercase_ = torch.flatten(self.pooler(SCREAMING_SNAKE_CASE_) , start_dim=1)
else:
lowercase_ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_ , pooler_output=SCREAMING_SNAKE_CASE_ , hidden_states=SCREAMING_SNAKE_CASE_ , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _lowerCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( _lowerCAmelCase ):
def __init__( self : Dict , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE_)
lowercase_ = config.num_labels
lowercase_ = MobileNetVaModel(SCREAMING_SNAKE_CASE_)
lowercase_ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ = nn.Dropout(config.classifier_dropout_prob , inplace=SCREAMING_SNAKE_CASE_)
lowercase_ = nn.Linear(SCREAMING_SNAKE_CASE_ , config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] = None , lowerCAmelCase_ : Union[str, Any] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Union[str, Any] = None , ):
"""simple docstring"""
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.mobilenet_va(SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_)
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier(self.dropout(SCREAMING_SNAKE_CASE_))
lowercase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ = """single_label_classification"""
else:
lowercase_ = """multi_label_classification"""
if self.config.problem_type == "regression":
lowercase_ = MSELoss()
if self.num_labels == 1:
lowercase_ = loss_fct(logits.squeeze() , labels.squeeze())
else:
lowercase_ = loss_fct(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
elif self.config.problem_type == "single_label_classification":
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
lowercase_ = BCEWithLogitsLoss()
lowercase_ = loss_fct(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ , hidden_states=outputs.hidden_states , )
| 366
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
lowercase_ = int(__lowerCAmelCase )
lowercase_ = int(__lowerCAmelCase )
lowercase_ = []
for temp in range(int(__lowerCAmelCase ) ):
series.append(F'''1 / {pow(temp + 1 , int(__lowerCAmelCase ) )}''' if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[str] = int(input("Enter the last number (nth term) of the P-Series"))
UpperCAmelCase : Tuple = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
UpperCAmelCase : Union[str, Any] = TypeVar("KT")
UpperCAmelCase : Union[str, Any] = TypeVar("VT")
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase_ : KT | str = "root" , lowerCAmelCase_ : VT | None = None):
"""simple docstring"""
lowercase_ = key
lowercase_ = value
lowercase_ = []
def __repr__( self : int):
"""simple docstring"""
return F'''Node({self.key}: {self.value})'''
@property
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return len(self.forward)
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : float = 0.5 , lowerCAmelCase_ : int = 1_6):
"""simple docstring"""
lowercase_ = Node[KT, VT]()
lowercase_ = 0
lowercase_ = p
lowercase_ = max_level
def __str__( self : Optional[Any]):
"""simple docstring"""
lowercase_ = list(self)
if len(UpperCamelCase__) == 0:
return F'''SkipList(level={self.level})'''
lowercase_ = max((len(str(UpperCamelCase__)) for item in items) , default=4)
lowercase_ = max(UpperCamelCase__ , 4) + 4
lowercase_ = self.head
lowercase_ = []
lowercase_ = node.forward.copy()
lines.append(F'''[{node.key}]'''.ljust(UpperCamelCase__ , """-""") + """* """ * len(UpperCamelCase__))
lines.append(""" """ * label_size + """| """ * len(UpperCamelCase__))
while len(node.forward) != 0:
lowercase_ = node.forward[0]
lines.append(
F'''[{node.key}]'''.ljust(UpperCamelCase__ , """-""")
+ """ """.join(str(n.key) if n.key == node.key else """|""" for n in forwards))
lines.append(""" """ * label_size + """| """ * len(UpperCamelCase__))
lowercase_ = node.forward
lines.append("""None""".ljust(UpperCamelCase__) + """* """ * len(UpperCamelCase__))
return F'''SkipList(level={self.level})\n''' + "\n".join(UpperCamelCase__)
def __iter__( self : List[Any]):
"""simple docstring"""
lowercase_ = self.head
while len(node.forward) != 0:
yield node.forward[0].key
lowercase_ = node.forward[0]
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = []
lowercase_ = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowercase_ = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(UpperCamelCase__)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : KT):
"""simple docstring"""
lowercase_ = self._locate_node(UpperCamelCase__)
if node is not None:
for i, update_node in enumerate(UpperCamelCase__):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowercase_ = node.forward[i]
else:
lowercase_ = update_node.forward[:i]
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : KT , lowerCAmelCase_ : VT):
"""simple docstring"""
lowercase_ = self._locate_node(UpperCamelCase__)
if node is not None:
lowercase_ = value
else:
lowercase_ = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , UpperCamelCase__):
update_vector.append(self.head)
lowercase_ = level
lowercase_ = Node(UpperCamelCase__ , UpperCamelCase__)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(UpperCamelCase__)
else:
lowercase_ = new_node
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : VT):
"""simple docstring"""
lowercase_ = self._locate_node(UpperCamelCase__)
if node is not None:
return node.value
return None
def _SCREAMING_SNAKE_CASE () -> Any:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
lowercase_ = skip_list.head
lowercase_ = {}
while node.level != 0:
lowercase_ = node.forward[0]
lowercase_ = node.value
assert len(SCREAMING_SNAKE_CASE__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
lowercase_ = skip_list.head
lowercase_ = {}
while node.level != 0:
lowercase_ = node.forward[0]
lowercase_ = node.value
if len(SCREAMING_SNAKE_CASE__ ) != 4:
print()
assert len(SCREAMING_SNAKE_CASE__ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
'''simple docstring'''
lowercase_ = SkipList()
assert skip_list.find("""Some key""" ) is None
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def _SCREAMING_SNAKE_CASE () -> Dict:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def _SCREAMING_SNAKE_CASE () -> Dict:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def _SCREAMING_SNAKE_CASE () -> Tuple:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 1_42 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(__lowerCAmelCase ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(SCREAMING_SNAKE_CASE__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
'''simple docstring'''
def is_sorted(__lowerCAmelCase ):
return all(next_item >= item for item, next_item in zip(SCREAMING_SNAKE_CASE__ , lst[1:] ) )
lowercase_ = SkipList()
for i in range(10 ):
skip_list.insert(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert is_sorted(list(SCREAMING_SNAKE_CASE__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(SCREAMING_SNAKE_CASE__ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(SCREAMING_SNAKE_CASE__ ) )
def _SCREAMING_SNAKE_CASE () -> str:
'''simple docstring'''
for _ in range(1_00 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _SCREAMING_SNAKE_CASE () -> Any:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 367
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
self.assertEqual(x.component(0) , 1)
self.assertEqual(x.component(2) , 3)
lowercase_ = Vector()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(lowerCAmelCase_) , """(0,0,0,0,0,1)""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3, 4])
self.assertEqual(len(lowerCAmelCase_) , 4)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = Vector([1, 2])
lowercase_ = Vector([1, 2, 3, 4, 5])
lowercase_ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
lowercase_ = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3)
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3)
self.assertEqual(z.euclidean_length() , 0)
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x + y).component(0) , 2)
self.assertEqual((x + y).component(1) , 3)
self.assertEqual((x + y).component(2) , 4)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x - y).component(0) , 0)
self.assertEqual((x - y).component(1) , 1)
self.assertEqual((x - y).component(2) , 2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([2, -1, 4]) # for test of dot product
lowercase_ = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0) , """(3.0,6.0,9.0)""")
self.assertEqual((a * b) , 0)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.assertEqual(str(zero_vector(1_0)).count("""0""") , 1_0)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1)) , """(0,1,0)""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 0, 1])
self.assertEqual(str(axpy(2 , lowerCAmelCase_ , lowerCAmelCase_)) , """(3,4,7)""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0, 0, 0, 0])
lowercase_ = x.copy()
self.assertEqual(str(lowerCAmelCase_) , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0])
x.change_component(0 , 0)
x.change_component(1 , 1)
self.assertEqual(str(lowerCAmelCase_) , """(0,1,0)""")
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y] , a.minor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y] , a.cofactor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(-5 , a.determinant())
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3)
lowercase_ = Vector([1, 2, 3])
self.assertEqual("""(14,32,50)""" , str(a * x))
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
a.change_component(0 , 2 , 5)
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(7 , a.component(2 , 1) , 0.01)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5)) , )
if __name__ == "__main__":
unittest.main()
| 313
| 0
|
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : int = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
lowercase__ = PegasusTokenizer
lowercase__ = PegasusTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = PegasusTokenizer(lowerCAmelCase_)
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/pegasus-large""")
def _UpperCAmelCase ( self : str , **lowerCAmelCase_ : int):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_)
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
return ("This is a test", "This is a test")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = '</s>'
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_) , lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<pad>""")
self.assertEqual(vocab_keys[1] , """</s>""")
self.assertEqual(vocab_keys[-1] , """v""")
self.assertEqual(len(lowerCAmelCase_) , 1_1_0_3)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
lowercase_ = self.tokenizer_class.from_pretrained(self.tmpdirname)
lowercase_ = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
lowercase_ = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_).input_ids[0]
lowercase_ = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_).input_ids[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowercase_ = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
lowercase_ = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
lowercase_ = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_).input_ids[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
lowercase_ = 'To ensure a smooth flow of bank resolutions.'
lowercase_ = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
lowercase_ = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_).input_ids[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ['This is going to be way too long.' * 1_5_0, 'short example']
lowercase_ = ['not super long but more than 5 tokens', 'tiny']
lowercase_ = self._large_tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
lowercase_ = self._large_tokenizer(
text_target=lowerCAmelCase_ , max_length=5 , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase_) == 2 # input_ids, attention_mask.
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = {'input_ids': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
lowercase__ = PegasusTokenizer
lowercase__ = PegasusTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = PegasusTokenizer(lowerCAmelCase_ , offset=0 , mask_token_sent=lowerCAmelCase_ , mask_token="""[MASK]""")
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""")
def _UpperCAmelCase ( self : List[Any] , **lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
return ("This is a test", "This is a test")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
lowercase_ = self.tokenizer_class.from_pretrained(self.tmpdirname)
lowercase_ = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
lowercase_ = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_).input_ids[0]
lowercase_ = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_).input_ids[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@require_torch
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = ['This is going to be way too long.' * 1_0_0_0, 'short example']
lowercase_ = ['not super long but more than 5 tokens', 'tiny']
lowercase_ = self._large_tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
lowercase_ = self._large_tokenizer(
text_target=lowerCAmelCase_ , max_length=5 , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase_) == 2 # input_ids, attention_mask.
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
lowercase_ = self._large_tokenizer(lowerCAmelCase_).input_ids
self.assertListEqual(
lowerCAmelCase_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 368
|
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < end:
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ , lowercase_ = _in_place_partition(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
count += _in_place_quick_sort(__lowerCAmelCase , __lowerCAmelCase , p - 1 )
count += _in_place_quick_sort(__lowerCAmelCase , p + 1 , __lowerCAmelCase )
return count
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ = start - 1
for index in range(__lowerCAmelCase , __lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowercase_ = new_pivot_index + 1
lowercase_ = a[new_pivot_index]
lowercase_ = a[index]
lowercase_ = temp
lowercase_ = a[new_pivot_index + 1]
lowercase_ = a[end]
lowercase_ = temp
return new_pivot_index + 1, count
UpperCAmelCase : Union[str, Any] = TemporaryFile()
UpperCAmelCase : Optional[int] = 100 # 1000 elements are to be sorted
UpperCAmelCase , UpperCAmelCase : List[str] = 0, 1 # mean and standard deviation
UpperCAmelCase : Optional[Any] = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase : List[str] = np.load(outfile)
UpperCAmelCase : List[Any] = len(M) - 1
UpperCAmelCase : Optional[int] = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 313
| 0
|
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( __a ):
lowercase__ = ["image_processor", "tokenizer"]
lowercase__ = "BridgeTowerImageProcessor"
lowercase__ = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(UpperCamelCase__ , UpperCamelCase__)
def __call__( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase_ : Union[bool, str, TruncationStrategy] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , lowerCAmelCase_ : Optional[bool] = None , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Optional[Union[str, TensorType]] = None , **lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
lowercase_ = self.tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
# add pixel_values + pixel_mask
lowercase_ = self.image_processor(
UpperCamelCase__ , return_tensors=UpperCamelCase__ , do_normalize=UpperCamelCase__ , do_center_crop=UpperCamelCase__ , **UpperCamelCase__)
encoding.update(UpperCamelCase__)
return encoding
def _UpperCAmelCase ( self : List[str] , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[str]):
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__)
def _UpperCAmelCase ( self : int , *lowerCAmelCase_ : int , **lowerCAmelCase_ : Tuple):
"""simple docstring"""
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__)
@property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.tokenizer.model_input_names
lowercase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ = """"""
else:
lowercase_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''' )
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ = in_proj_weight[
: config.hidden_size, :
]
lowercase_ = in_proj_bias[: config.hidden_size]
lowercase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ = in_proj_bias[-config.hidden_size :]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = dct.pop(__lowerCAmelCase )
lowercase_ = val
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = ViTMSNConfig()
lowercase_ = 10_00
lowercase_ = """datasets/huggingface/label-files"""
lowercase_ = """imagenet-1k-id2label.json"""
lowercase_ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase ) , """r""" ) )
lowercase_ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowercase_ = idalabel
lowercase_ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase_ = 3_84
lowercase_ = 15_36
lowercase_ = 6
elif "l16" in checkpoint_url:
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
elif "b4" in checkpoint_url:
lowercase_ = 4
elif "l7" in checkpoint_url:
lowercase_ = 7
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
lowercase_ = ViTMSNModel(__lowerCAmelCase )
lowercase_ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""target_encoder"""]
lowercase_ = ViTImageProcessor(size=config.image_size )
remove_projection_head(__lowerCAmelCase )
lowercase_ = create_rename_keys(__lowerCAmelCase , base_model=__lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase , base_model=__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
lowercase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
lowercase_ = ViTImageProcessor(
size=config.image_size , image_mean=__lowerCAmelCase , image_std=__lowerCAmelCase )
lowercase_ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowercase_ = model(**__lowerCAmelCase )
lowercase_ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowercase_ = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
lowercase_ = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
lowercase_ = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
lowercase_ = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
lowercase_ = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __lowerCAmelCase , atol=1E-4 )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313
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|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = 2
lowercase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(snake_case_ )
if n > 1:
factors.append(snake_case_ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "perceiver"
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str]=2_5_6 , lowerCAmelCase_ : Dict=1_2_8_0 , lowerCAmelCase_ : List[Any]=7_6_8 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[Any]=2_6 , lowerCAmelCase_ : Optional[Any]=8 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Optional[Any]="kv" , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Union[str, Any]=0.02 , lowerCAmelCase_ : List[Any]=1E-12 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Union[str, Any]=2_6_2 , lowerCAmelCase_ : Union[str, Any]=2_0_4_8 , lowerCAmelCase_ : Any=5_6 , lowerCAmelCase_ : int=[3_6_8, 4_9_6] , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : Dict=1_9_2_0 , lowerCAmelCase_ : Optional[Any]=1_6 , lowerCAmelCase_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **lowerCAmelCase_ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_)
lowercase_ = num_latents
lowercase_ = d_latents
lowercase_ = d_model
lowercase_ = num_blocks
lowercase_ = num_self_attends_per_block
lowercase_ = num_self_attention_heads
lowercase_ = num_cross_attention_heads
lowercase_ = qk_channels
lowercase_ = v_channels
lowercase_ = cross_attention_shape_for_attention
lowercase_ = self_attention_widening_factor
lowercase_ = cross_attention_widening_factor
lowercase_ = hidden_act
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = use_query_residual
# masked language modeling attributes
lowercase_ = vocab_size
lowercase_ = max_position_embeddings
# image classification attributes
lowercase_ = image_size
# flow attributes
lowercase_ = train_size
# multimodal autoencoding attributes
lowercase_ = num_frames
lowercase_ = audio_samples_per_frame
lowercase_ = samples_per_patch
lowercase_ = output_shape
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
if self.task == "multiple-choice":
lowercase_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""inputs""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
@property
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return 1E-4
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[TensorType] = None , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 4_0 , lowerCAmelCase_ : int = 4_0 , ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , 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
lowercase_ = preprocessor.num_special_tokens_to_add(lowerCAmelCase_)
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase_)
# Generate dummy inputs according to compute batch and sequence
lowercase_ = [""" """.join(["""a"""]) * seq_length] * batch_size
lowercase_ = dict(preprocessor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""input_ids""")
return inputs
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase_ = self._generate_dummy_images(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = dict(preprocessor(images=lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""pixel_values""")
return inputs
else:
raise ValueError(
"""Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""")
| 313
| 0
|
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCamelCase : List[Any] = ""
__lowerCamelCase : str = ""
__lowerCamelCase : Optional[int] = ""
__lowerCamelCase : List[Any] = 1 # (0 is vertical, 1 is horizontal)
def _SCREAMING_SNAKE_CASE () -> List[Any]:
'''simple docstring'''
lowercase_ , lowercase_ = get_dataset(a__ , a__ )
print("""Processing...""" )
lowercase_ , lowercase_ , lowercase_ = update_image_and_anno(a__ , a__ , a__ )
for index, image in enumerate(a__ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowercase_ = random_chars(32 )
lowercase_ = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
lowercase_ = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , a__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(a__ )} with {file_name}''' )
lowercase_ = []
for anno in new_annos[index]:
lowercase_ = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(a__ )
with open(F'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase_ = []
lowercase_ = []
for label_file in glob.glob(os.path.join(a__ , """*.txt""" ) ):
lowercase_ = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(a__ ) as in_file:
lowercase_ = in_file.readlines()
lowercase_ = os.path.join(a__ , F'''{label_name}.jpg''' )
lowercase_ = []
for obj_list in obj_lists:
lowercase_ = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(a__ )
labels.append(a__ )
return img_paths, labels
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 ) -> int:
'''simple docstring'''
lowercase_ = []
lowercase_ = []
lowercase_ = []
for idx in range(len(a__ ) ):
lowercase_ = []
lowercase_ = img_list[idx]
path_list.append(a__ )
lowercase_ = anno_list[idx]
lowercase_ = cva.imread(a__ )
if flip_type == 1:
lowercase_ = cva.flip(a__ , a__ )
for bbox in img_annos:
lowercase_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
lowercase_ = cva.flip(a__ , a__ )
for bbox in img_annos:
lowercase_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(a__ )
new_imgs_list.append(a__ )
return new_imgs_list, new_annos_lists, path_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 32 ) -> Dict:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
lowercase_ = ascii_lowercase + digits
return "".join(random.choice(a__ ) for _ in range(a__ ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 371
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BarthezTokenizer
lowercase__ = BarthezTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
super().setUp()
lowercase_ = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
tokenizer.save_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase_)
lowercase_ = tokenizer
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_) , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<s>""")
self.assertEqual(vocab_keys[1] , """<pad>""")
self.assertEqual(vocab_keys[-1] , """<mask>""")
self.assertEqual(len(lowerCAmelCase_) , 1_0_1_1_2_2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2)
@require_torch
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowercase_ = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
lowercase_ = self.tokenizer(
lowerCAmelCase_ , max_length=len(lowerCAmelCase_) , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_)
self.assertEqual((2, 6) , batch.input_ids.shape)
self.assertEqual((2, 6) , batch.attention_mask.shape)
lowercase_ = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
lowercase_ = rust_tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase_ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""moussaKam/mbarthez""" , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" , sequences=lowerCAmelCase_ , )
| 313
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|
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str=1_3 , lowerCAmelCase_ : str=7 , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : int=9_9 , lowerCAmelCase_ : int=6_4 , lowerCAmelCase_ : Any=3_2 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Optional[Any]=3_7 , lowerCAmelCase_ : Dict="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Union[str, Any]=5_1_2 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : int=2 , lowerCAmelCase_ : Union[str, Any]=0.02 , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : List[str]=4 , lowerCAmelCase_ : Any=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_A , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MobileBertModel(config=_A)
model.to(_A)
model.eval()
lowercase_ = model(_A , attention_mask=_A , token_type_ids=_A)
lowercase_ = model(_A , token_type_ids=_A)
lowercase_ = 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 _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = MobileBertForMaskedLM(config=_A)
model.to(_A)
model.eval()
lowercase_ = 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 _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = MobileBertForNextSentencePrediction(config=_A)
model.to(_A)
model.eval()
lowercase_ = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = MobileBertForPreTraining(config=_A)
model.to(_A)
model.eval()
lowercase_ = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , next_sentence_label=_A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MobileBertForQuestionAnswering(config=_A)
model.to(_A)
model.eval()
lowercase_ = 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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MobileBertForSequenceClassification(_A)
model.to(_A)
model.eval()
lowercase_ = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MobileBertForTokenClassification(config=_A)
model.to(_A)
model.eval()
lowercase_ = 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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MobileBertForMultipleChoice(config=_A)
model.to(_A)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
lowercase_
) = config_and_inputs
lowercase_ = {'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 ):
lowercase__ = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MobileBertModel,
"fill-mask": MobileBertForMaskedLM,
"question-answering": MobileBertForQuestionAnswering,
"text-classification": MobileBertForSequenceClassification,
"token-classification": MobileBertForTokenClassification,
"zero-shot": MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(_A , _A , return_labels=_A)
if return_labels:
if model_class in get_values(_A):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_A)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A)
return inputs_dict
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = MobileBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=_A , hidden_size=3_7)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*_A)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*_A)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_A)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_A)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*_A)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*_A)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_A)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*_A)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return torch.tensor(
__a , dtype=torch.long , device=__a , )
UpperCAmelCase : int = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = MobileBertModel.from_pretrained("""google/mobilebert-uncased""").to(_A)
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(_A)[0]
lowercase_ = torch.Size((1, 9, 5_1_2))
self.assertEqual(output.shape , _A)
lowercase_ = torch.tensor(
[
[
[-2.4736526E07, 8.2691656E04, 1.6521838E05],
[-5.7541704E-01, 3.9056022E00, 4.4011507E00],
[2.6047359E00, 1.5677652E00, -1.7324188E-01],
]
] , device=_A , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
lowercase_ = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE)
lowercase_ = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)
self.assertTrue(lower_bound and upper_bound)
| 350
|
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Optional[Any] = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class SCREAMING_SNAKE_CASE__ :
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = "gelu"
def __init__( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Any=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : str=9_9 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Dict=5 , lowerCAmelCase_ : Union[str, Any]=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Optional[int]=2_0 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
lowercase_ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
lowercase_ = np.concatenate([input_ids, eos_tensor] , axis=1)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase_ = prepare_pegasus_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.not_equal(__lowerCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowercase_ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxPegasusModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=None , **lowerCAmelCase_ : Optional[int]):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=lowerCAmelCase_)
lowercase_ = np.ones((1, 1))
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
lowercase_ = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")
lowercase_ = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
lowercase_ = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""np""" , truncation=lowerCAmelCase_ , max_length=5_1_2 , padding=lowerCAmelCase_)
lowercase_ = model.generate(**lowerCAmelCase_ , num_beams=2).sequences
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_)
assert tgt_text == decoded
| 313
| 0
|
"""simple docstring"""
UpperCAmelCase : Any = tuple[float, float, float]
UpperCAmelCase : Optional[Any] = tuple[float, float, float]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
lowercase_ = end_pointa[0] - end_pointa[0]
lowercase_ = end_pointa[1] - end_pointa[1]
lowercase_ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
lowercase_ = ab[1] * ac[2] - ab[2] * ac[1] # *i
lowercase_ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
lowercase_ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
return tuple(round(A__ , A__ ) for x in vector ) == (0, 0, 0)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 10 ) -> Dict:
lowercase_ = create_vector(A__ , A__ )
lowercase_ = create_vector(A__ , A__ )
return is_zero_vector(get_ad_vectors_cross(A__ , A__ ) , A__ )
| 351
|
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = None
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = True
lowercase__ = None
lowercase__ = 1
lowercase__ = None
lowercase__ = False
lowercase__ = None
lowercase__ = None
def _UpperCAmelCase ( self : int):
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(lowerCAmelCase_) for k, v in self.__dict__.items()})
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
UpperCAmelCase : Tuple = TypeVar("KT")
UpperCAmelCase : Any = TypeVar("VT")
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
def __init__( self : Dict , lowerCAmelCase_ : KT | str = "root" , lowerCAmelCase_ : VT | None = None):
"""simple docstring"""
lowercase_ = key
lowercase_ = value
lowercase_ = []
def __repr__( self : str):
"""simple docstring"""
return F'''Node({self.key}: {self.value})'''
@property
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
return len(self.forward)
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
def __init__( self : Union[str, Any] , lowerCAmelCase_ : float = 0.5 , lowerCAmelCase_ : int = 1_6):
"""simple docstring"""
lowercase_ = Node[KT, VT]()
lowercase_ = 0
lowercase_ = p
lowercase_ = max_level
def __str__( self : List[Any]):
"""simple docstring"""
lowercase_ = list(self)
if len(snake_case_) == 0:
return F'''SkipList(level={self.level})'''
lowercase_ = max((len(str(snake_case_)) for item in items) , default=4)
lowercase_ = max(snake_case_ , 4) + 4
lowercase_ = self.head
lowercase_ = []
lowercase_ = node.forward.copy()
lines.append(F'''[{node.key}]'''.ljust(snake_case_ , """-""") + """* """ * len(snake_case_))
lines.append(""" """ * label_size + """| """ * len(snake_case_))
while len(node.forward) != 0:
lowercase_ = node.forward[0]
lines.append(
F'''[{node.key}]'''.ljust(snake_case_ , """-""")
+ """ """.join(str(n.key) if n.key == node.key else """|""" for n in forwards))
lines.append(""" """ * label_size + """| """ * len(snake_case_))
lowercase_ = node.forward
lines.append("""None""".ljust(snake_case_) + """* """ * len(snake_case_))
return F'''SkipList(level={self.level})\n''' + "\n".join(snake_case_)
def __iter__( self : int):
"""simple docstring"""
lowercase_ = self.head
while len(node.forward) != 0:
yield node.forward[0].key
lowercase_ = node.forward[0]
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = []
lowercase_ = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowercase_ = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(snake_case_)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : KT):
"""simple docstring"""
lowercase_ = self._locate_node(snake_case_)
if node is not None:
for i, update_node in enumerate(snake_case_):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowercase_ = node.forward[i]
else:
lowercase_ = update_node.forward[:i]
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : KT , lowerCAmelCase_ : VT):
"""simple docstring"""
lowercase_ = self._locate_node(snake_case_)
if node is not None:
lowercase_ = value
else:
lowercase_ = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , snake_case_):
update_vector.append(self.head)
lowercase_ = level
lowercase_ = Node(snake_case_ , snake_case_)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(snake_case_)
else:
lowercase_ = new_node
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : VT):
"""simple docstring"""
lowercase_ = self._locate_node(snake_case_)
if node is not None:
return node.value
return None
def _SCREAMING_SNAKE_CASE () -> str:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
lowercase_ = skip_list.head
lowercase_ = {}
while node.level != 0:
lowercase_ = node.forward[0]
lowercase_ = node.value
assert len(lowerCAmelCase__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
lowercase_ = skip_list.head
lowercase_ = {}
while node.level != 0:
lowercase_ = node.forward[0]
lowercase_ = node.value
if len(lowerCAmelCase__ ) != 4:
print()
assert len(lowerCAmelCase__ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
assert skip_list.find("""Some key""" ) is None
def _SCREAMING_SNAKE_CASE () -> Tuple:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def _SCREAMING_SNAKE_CASE () -> str:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 1_42 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(__lowerCAmelCase ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowerCAmelCase__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _SCREAMING_SNAKE_CASE () -> int:
'''simple docstring'''
def is_sorted(__lowerCAmelCase ):
return all(next_item >= item for item, next_item in zip(lowerCAmelCase__ , lst[1:] ) )
lowercase_ = SkipList()
for i in range(10 ):
skip_list.insert(lowerCAmelCase__ , lowerCAmelCase__ )
assert is_sorted(list(lowerCAmelCase__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowerCAmelCase__ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(lowerCAmelCase__ ) )
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
for _ in range(1_00 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
'''simple docstring'''
lowercase_ = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 352
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : List[Any] = {
"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",
},
}
UpperCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 1_6384,
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = LEDTokenizer
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]="replace" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : List[Any]="</s>" , lowerCAmelCase_ : Optional[Any]="<s>" , lowerCAmelCase_ : Union[str, Any]="<unk>" , lowerCAmelCase_ : List[str]="<pad>" , lowerCAmelCase_ : Dict="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop("""type"""))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**lowerCAmelCase_)
lowercase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ = """post_processor"""
lowercase_ = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
if tokenizer_component_instance:
lowercase_ = 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:
lowercase_ = tuple(state["""sep"""])
if "cls" in state:
lowercase_ = tuple(state["""cls"""])
lowercase_ = False
if state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = add_prefix_space
lowercase_ = True
if state.get("""trim_offsets""" , lowerCAmelCase_) != trim_offsets:
lowercase_ = trim_offsets
lowercase_ = True
if changes_to_apply:
lowercase_ = getattr(lowerCAmelCase_ , state.pop("""type"""))
lowercase_ = component_class(**lowerCAmelCase_)
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""")
return None
return str(self._mask_token)
@mask_token.setter
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else value
lowercase_ = value
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
lowercase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_)
return tuple(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = [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 _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
lowercase_ = [self.sep_token_id]
lowercase_ = [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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = super()._pad(
encoded_inputs=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding_strategy=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ = len(encoded_inputs["""global_attention_mask"""]) != len(lowerCAmelCase_)
if needs_to_be_padded:
lowercase_ = len(lowerCAmelCase_) - 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`
lowercase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))
return encoded_inputs
| 313
| 0
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : int = TypeVar("DatasetType", Dataset, IterableDataset)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = "first_exhausted" , ) -> int:
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("""Unable to interleave an empty list of datasets.""" )
for i, dataset in enumerate(__lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(__lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"""is an empty dataset dictionary.""" )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(__lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase ).__name__}.''' )
if i == 0:
lowercase_ , lowercase_ = (
(Dataset, IterableDataset) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , stopping_strategy=__lowerCAmelCase )
else:
return _interleave_iterable_datasets(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , stopping_strategy=__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = 0 , ) -> str:
'''simple docstring'''
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(__lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(__lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"""is an empty dataset dictionary.""" )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(__lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase ).__name__}.''' )
if i == 0:
lowercase_ , lowercase_ = (
(Dataset, IterableDataset) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(__lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , axis=__lowerCAmelCase )
else:
return _concatenate_iterable_datasets(__lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , axis=__lowerCAmelCase )
| 353
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 313
| 0
|
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase : Optional[int] = TypeVar("T")
UpperCAmelCase : Tuple = Union[List[T], Tuple[T, ...]]
UpperCAmelCase : List[Any] = Union[T, List[T], Dict[str, T]]
UpperCAmelCase : Any = Union[str, bytes, os.PathLike]
| 354
|
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=6_4 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=3_7 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Dict=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = MegatronBertModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MegatronBertForMaskedLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = MegatronBertForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForNextSentencePrediction(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForPreTraining(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , next_sentence_label=lowerCAmelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = MegatronBertForQuestionAnswering(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForTokenClassification(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MegatronBertForMultipleChoice(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
# test_resize_embeddings = False
lowercase__ = False
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
if return_labels:
if model_class in get_values(lowerCAmelCase_):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_)
return inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = MegatronBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return torch.tensor(
__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase , )
UpperCAmelCase : Any = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
@unittest.skip("""Model is not available.""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ = os.path.join(os.environ["""MYDIR"""] , lowerCAmelCase_)
lowercase_ = MegatronBertModel.from_pretrained(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.half()
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = torch.Size((1, 9, 1_0_2_4))
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3):
for jj in range(3):
lowercase_ = output[0, ii, jj]
lowercase_ = expected[3 * ii + jj]
lowercase_ = """ii={} jj={} a={} b={}""".format(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
self.assertTrue(math.isclose(lowerCAmelCase_ , lowerCAmelCase_ , rel_tol=lowerCAmelCase_ , abs_tol=lowerCAmelCase_) , msg=lowerCAmelCase_)
| 313
| 0
|
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''BlipImageProcessor'''
lowercase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = False
super().__init__(UpperCamelCase_ , UpperCamelCase_)
lowercase_ = self.image_processor
def __call__( self : List[Any] , lowerCAmelCase_ : ImageInput = None , lowerCAmelCase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase_ : Union[bool, str, TruncationStrategy] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Optional[Union[str, TensorType]] = None , **lowerCAmelCase_ : Tuple , ):
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""")
# Get only text
if images is None:
lowercase_ = self.tokenizer
lowercase_ = self.tokenizer(
text=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=UpperCamelCase_ , stride=UpperCamelCase_ , pad_to_multiple_of=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , return_overflowing_tokens=UpperCamelCase_ , return_special_tokens_mask=UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ , return_length=UpperCamelCase_ , verbose=UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ , )
return text_encoding
# add pixel_values
lowercase_ = self.image_processor(UpperCamelCase_ , return_tensors=UpperCamelCase_)
if text is not None:
lowercase_ = self.tokenizer(
text=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=UpperCamelCase_ , stride=UpperCamelCase_ , pad_to_multiple_of=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , return_overflowing_tokens=UpperCamelCase_ , return_special_tokens_mask=UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ , return_length=UpperCamelCase_ , verbose=UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ , )
else:
lowercase_ = None
if text_encoding is not None:
encoding_image_processor.update(UpperCamelCase_)
return encoding_image_processor
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Dict , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCamelCase_ , **UpperCamelCase_)
def _UpperCAmelCase ( self : int , *lowerCAmelCase_ : Tuple , **lowerCAmelCase_ : List[str]):
"""simple docstring"""
return self.tokenizer.decode(*UpperCamelCase_ , **UpperCamelCase_)
@property
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.tokenizer.model_input_names
lowercase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 355
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows != columns:
lowercase_ = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__lowerCAmelCase )
lowercase_ = np.zeros((rows, columns) )
lowercase_ = np.zeros((rows, columns) )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase_ = (table[i][j] - total) / upper[j][j]
lowercase_ = 1
for j in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
lowercase_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
from __future__ import annotations
from random import random
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , lowerCAmelCase_ : Any = None):
"""simple docstring"""
lowercase_ = value
lowercase_ = random()
lowercase_ = None
lowercase_ = None
def __repr__( self : Dict):
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return F'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{F'''{self.value}: {self.prior:.5}''': (self.left, self.right)} , indent=1)
def __str__( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = str(self.value) + ''' '''
lowercase_ = str(self.left or """""")
lowercase_ = str(self.right or """""")
return value + left + right
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowercase_ = split(root.left , lowerCamelCase_ )
return left, root
else:
lowercase_ = split(root.right , lowerCamelCase_ )
return root, right
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowercase_ = merge(left.right , lowerCamelCase_ )
return left
else:
lowercase_ = merge(lowerCamelCase_ , right.left )
return right
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = Node(lowerCamelCase_ )
lowercase_ = split(lowerCamelCase_ , lowerCamelCase_ )
return merge(merge(lowerCamelCase_ , lowerCamelCase_ ) , lowerCamelCase_ )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = split(lowerCamelCase_ , value - 1 )
lowercase_ = split(lowerCamelCase_ , lowerCamelCase_ )
return merge(lowerCamelCase_ , lowerCamelCase_ )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=""",""" )
inorder(root.right )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
lowercase_ = insert(lowerCamelCase_ , int(arg[1:] ) )
elif arg[0] == "-":
lowercase_ = erase(lowerCamelCase_ , int(arg[1:] ) )
else:
print("""Unknown command""" )
return root
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = None
print(
"""enter numbers to create a tree, + value to add value into treap, """
"""- value to erase all nodes with value. \'q\' to quit. """ )
lowercase_ = input()
while args != "q":
lowercase_ = interact_treap(lowerCamelCase_ , lowerCamelCase_ )
print(lowerCamelCase_ )
lowercase_ = input()
print("""good by!""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 356
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [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(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
| 0
|
import functools
from typing import Any
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> bool:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or len(UpperCAmelCase_ ) == 0:
raise ValueError("""the string should be not empty string""" )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not all(
isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and len(UpperCAmelCase_ ) > 0 for item in words ):
raise ValueError("""the words should be a list of non-empty strings""" )
# Build trie
lowercase_ = {}
lowercase_ = 'WORD_KEEPER'
for word in words:
lowercase_ = trie
for c in word:
if c not in trie_node:
lowercase_ = {}
lowercase_ = trie_node[c]
lowercase_ = True
lowercase_ = len(UpperCAmelCase_ )
# Dynamic programming method
@functools.cache
def is_breakable(__lowerCAmelCase ) -> bool:
if index == len_string:
return True
lowercase_ = trie
for i in range(UpperCAmelCase_ , UpperCAmelCase_ ):
lowercase_ = trie_node.get(string[i] , UpperCAmelCase_ )
if trie_node is None:
return False
if trie_node.get(UpperCAmelCase_ , UpperCAmelCase_ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , 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]
| 313
| 0
|
"""simple docstring"""
import numpy as np
UpperCAmelCase : Optional[Any] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any):
"""simple docstring"""
lowercase_ = np.array(__snake_case)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = np.where(letter == self.SQUARE)
lowercase_ = np.concatenate([indexa + 1, indexa + 1])
return indexes
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = message.lower()
lowercase_ = message.replace(""" """ , """""")
lowercase_ = message.replace("""j""" , """i""")
lowercase_ = np.empty((2, len(__snake_case)))
for letter_index in range(len(__snake_case)):
lowercase_ = self.letter_to_numbers(message[letter_index])
lowercase_ = numbers[0]
lowercase_ = numbers[1]
lowercase_ = first_step.reshape(2 * len(__snake_case))
lowercase_ = ''
for numbers_index in range(len(__snake_case)):
lowercase_ = int(second_step[numbers_index * 2])
lowercase_ = int(second_step[(numbers_index * 2) + 1])
lowercase_ = self.numbers_to_letter(__snake_case , __snake_case)
lowercase_ = encoded_message + letter
return encoded_message
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = message.lower()
message.replace(""" """ , """""")
lowercase_ = np.empty(2 * len(__snake_case))
for letter_index in range(len(__snake_case)):
lowercase_ = self.letter_to_numbers(message[letter_index])
lowercase_ = numbers[0]
lowercase_ = numbers[1]
lowercase_ = first_step.reshape((2, len(__snake_case)))
lowercase_ = ''
for numbers_index in range(len(__snake_case)):
lowercase_ = int(second_step[0, numbers_index])
lowercase_ = int(second_step[1, numbers_index])
lowercase_ = self.numbers_to_letter(__snake_case , __snake_case)
lowercase_ = decoded_message + letter
return decoded_message
| 358
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Dict=9_9 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : str=4 , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : List[Any]=0.02 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
lowercase_ = initializer_range
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowercase_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase_ , )
lowercase_ = prepare_blenderbot_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = 99
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
lowercase_ = input_ids.shape[0]
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._get_config_and_data()
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = lm_model(input_ids=lowerCAmelCase_)
lowercase_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa)
lowercase_ = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa)
lowercase_ = lm_model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_)
lowercase_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(lowerCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowercase__ = True
lowercase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = FlaxBlenderbotModelTester(self)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : str):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase_ = np.ones((1, 1)) * model.config.eos_token_id
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""")
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
lowercase_ = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
lowercase_ = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCAmelCase_)
lowercase_ = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")
lowercase_ = ["""Sam"""]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""jax""")
lowercase_ = model.generate(**lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = """Sam is a great name. It means \"sun\" in Gaelic."""
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , **lowerCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 313
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"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase : List[str] = 637_8137.0
UpperCAmelCase : List[str] = 635_6752.31_4245
UpperCAmelCase : Any = 637_8137
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
lowercase_ = atan((1 - flattening) * tan(radians(lowerCAmelCase__ ) ) )
lowercase_ = atan((1 - flattening) * tan(radians(lowerCAmelCase__ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
lowercase_ = haversine_distance(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
lowercase_ = (b_lata + b_lata) / 2
lowercase_ = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
lowercase_ = (sin(lowerCAmelCase__ ) ** 2) * (cos(lowerCAmelCase__ ) ** 2)
lowercase_ = cos(sigma / 2 ) ** 2
lowercase_ = (sigma - sin(lowerCAmelCase__ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
lowercase_ = (cos(lowerCAmelCase__ ) ** 2) * (sin(lowerCAmelCase__ ) ** 2)
lowercase_ = sin(sigma / 2 ) ** 2
lowercase_ = (sigma + sin(lowerCAmelCase__ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313
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"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int]):
"""simple docstring"""
lowercase_ = """"""
lowercase_ = """"""
lowercase_ = []
lowercase_ = 0
lowercase_ = 2_5_6
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = cva.imread(a__ , 0)
lowercase_ = copy.deepcopy(self.img)
lowercase_ , lowercase_ , lowercase_ = plt.hist(self.img.ravel() , 2_5_6 , [0, 2_5_6] , label="""x""")
lowercase_ = np.sum(a__)
for i in range(len(a__)):
lowercase_ = x[i] / self.k
self.sk += prk
lowercase_ = (self.L - 1) * self.sk
if self.rem != 0:
lowercase_ = int(last % last)
lowercase_ = int(last + 1 if self.rem >= 0.5 else last)
self.last_list.append(a__)
lowercase_ = int(np.ma.count(self.img) / self.img[1].size)
lowercase_ = self.img[1].size
for i in range(self.number_of_cols):
for j in range(self.number_of_rows):
lowercase_ = self.img[j][i]
if num != self.last_list[num]:
lowercase_ = self.last_list[num]
cva.imwrite("""output_data/output.jpg""" , self.img)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
plt.hist(self.img.ravel() , 2_5_6 , [0, 2_5_6])
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
cva.imshow("""Output-Image""" , self.img)
cva.imshow("""Input-Image""" , self.original_image)
cva.waitKey(5_0_0_0)
cva.destroyAllWindows()
if __name__ == "__main__":
UpperCAmelCase : Any = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
UpperCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 360
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 313
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def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase_ = mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
lowercase_ = max(
mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , j - wt[i - 1] ) + val[i - 1] , )
lowercase_ = val
return f[i][j]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [[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_:
lowercase_ = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
lowercase_ = dp[i - 1][w_]
return dp[n][w_], dp
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
if not (isinstance(__lowerCAmelCase , (list, tuple) ) and isinstance(__lowerCAmelCase , (list, tuple) )):
raise ValueError(
"""Both the weights and values vectors must be either lists or tuples""" )
lowercase_ = len(__lowerCAmelCase )
if num_items != len(__lowerCAmelCase ):
lowercase_ = (
'''The number of weights must be the same as the number of values.\n'''
F'''But got {num_items} weights and {len(__lowerCAmelCase )} values'''
)
raise ValueError(__lowerCAmelCase )
for i in range(__lowerCAmelCase ):
if not isinstance(wt[i] , __lowerCAmelCase ):
lowercase_ = (
'''All weights must be integers but got weight of '''
F'''type {type(wt[i] )} at index {i}'''
)
raise TypeError(__lowerCAmelCase )
lowercase_ = knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase_ = set()
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return optimal_val, example_optional_set
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , i - 1 , __lowerCAmelCase , __lowerCAmelCase )
else:
optimal_set.add(__lowerCAmelCase )
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , i - 1 , j - wt[i - 1] , __lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : int = [3, 2, 4, 4]
UpperCAmelCase : int = [4, 3, 2, 3]
UpperCAmelCase : Tuple = 4
UpperCAmelCase : Any = 6
UpperCAmelCase : Any = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
UpperCAmelCase : List[Any] = 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
UpperCAmelCase : Dict = 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)
| 361
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 313
| 0
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = checkpoint
lowercase_ = {}
lowercase_ = vae_state_dict["""encoder.conv_in.weight"""]
lowercase_ = vae_state_dict["""encoder.conv_in.bias"""]
lowercase_ = vae_state_dict["""encoder.conv_out.weight"""]
lowercase_ = vae_state_dict["""encoder.conv_out.bias"""]
lowercase_ = vae_state_dict["""encoder.norm_out.weight"""]
lowercase_ = vae_state_dict["""encoder.norm_out.bias"""]
lowercase_ = vae_state_dict["""decoder.conv_in.weight"""]
lowercase_ = vae_state_dict["""decoder.conv_in.bias"""]
lowercase_ = vae_state_dict["""decoder.conv_out.weight"""]
lowercase_ = vae_state_dict["""decoder.conv_out.bias"""]
lowercase_ = vae_state_dict["""decoder.norm_out.weight"""]
lowercase_ = vae_state_dict["""decoder.norm_out.bias"""]
lowercase_ = vae_state_dict["""quant_conv.weight"""]
lowercase_ = vae_state_dict["""quant_conv.bias"""]
lowercase_ = vae_state_dict["""post_quant_conv.weight"""]
lowercase_ = vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
lowercase_ = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
lowercase_ = {
layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(UpperCAmelCase__ )
}
# Retrieves the keys for the decoder up blocks only
lowercase_ = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
lowercase_ = {
layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(UpperCAmelCase__ )
}
for i in range(UpperCAmelCase__ ):
lowercase_ = [key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]
if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
lowercase_ = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.weight''' )
lowercase_ = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.bias''' )
lowercase_ = renew_vae_resnet_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": F'''down.{i}.block''', """new""": F'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
lowercase_ = [key for key in vae_state_dict if """encoder.mid.block""" in key]
lowercase_ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
lowercase_ = [key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]
lowercase_ = renew_vae_resnet_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": F'''mid.block_{i}''', """new""": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
lowercase_ = [key for key in vae_state_dict if """encoder.mid.attn""" in key]
lowercase_ = renew_vae_attention_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
conv_attn_to_linear(UpperCAmelCase__ )
for i in range(UpperCAmelCase__ ):
lowercase_ = num_up_blocks - 1 - i
lowercase_ = [
key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
]
if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
lowercase_ = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.weight'''
]
lowercase_ = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.bias'''
]
lowercase_ = renew_vae_resnet_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": F'''up.{block_id}.block''', """new""": F'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
lowercase_ = [key for key in vae_state_dict if """decoder.mid.block""" in key]
lowercase_ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
lowercase_ = [key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]
lowercase_ = renew_vae_resnet_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": F'''mid.block_{i}''', """new""": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
lowercase_ = [key for key in vae_state_dict if """decoder.mid.attn""" in key]
lowercase_ = renew_vae_attention_paths(UpperCAmelCase__ )
lowercase_ = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__ )
conv_attn_to_linear(UpperCAmelCase__ )
return new_checkpoint
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase_ = requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
lowercase_ = io.BytesIO(r.content )
lowercase_ = OmegaConf.load(UpperCAmelCase__ )
lowercase_ = 5_12
lowercase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
lowercase_ = {}
with safe_open(UpperCAmelCase__ , framework="""pt""" , device="""cpu""" ) as f:
for key in f.keys():
lowercase_ = f.get_tensor(UpperCAmelCase__ )
else:
lowercase_ = torch.load(UpperCAmelCase__ , map_location=UpperCAmelCase__ )["""state_dict"""]
# Convert the VAE model.
lowercase_ = create_vae_diffusers_config(UpperCAmelCase__ , image_size=UpperCAmelCase__ )
lowercase_ = custom_convert_ldm_vae_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ = AutoencoderKL(**UpperCAmelCase__ )
vae.load_state_dict(UpperCAmelCase__ )
vae.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
UpperCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
UpperCAmelCase : Optional[Any] = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 363
|
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase_ = 0 if allow_empty_subarrays else float("""-inf""" )
lowercase_ = 0.0
for num in arr:
lowercase_ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowercase_ = max(__lowerCAmelCase , __lowerCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : Union[str, Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 313
| 0
|
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = args.log_outputs
lowercase_ = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
lowercase_ = load_metric("""wer""" )
lowercase_ = load_metric("""cer""" )
# compute metrics
lowercase_ = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
lowercase_ = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
lowercase_ = F'''WER: {wer_result}\nCER: {cer_result}'''
print(SCREAMING_SNAKE_CASE_ )
with open(F'''{dataset_id}_eval_results.txt''' , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
lowercase_ = F'''log_{dataset_id}_predictions.txt'''
lowercase_ = F'''log_{dataset_id}_targets.txt'''
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as p, open(SCREAMING_SNAKE_CASE_ , """w""" ) as t:
# mapping function to write output
def write_to_file(__lowerCAmelCase , __lowerCAmelCase ):
p.write(F'''{i}''' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F'''{i}''' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(SCREAMING_SNAKE_CASE_ , with_indices=SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
lowercase_ = re.sub(SCREAMING_SNAKE_CASE_ , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
lowercase_ = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
lowercase_ = """ """.join(text.split(SCREAMING_SNAKE_CASE_ ) )
return text
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=SCREAMING_SNAKE_CASE_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
lowercase_ = AutoFeatureExtractor.from_pretrained(args.model_id )
lowercase_ = feature_extractor.sampling_rate
# resample audio
lowercase_ = dataset.cast_column("""audio""" , Audio(sampling_rate=SCREAMING_SNAKE_CASE_ ) )
# load eval pipeline
if args.device is None:
lowercase_ = 0 if torch.cuda.is_available() else -1
lowercase_ = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__lowerCAmelCase ):
lowercase_ = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
lowercase_ = prediction["""text"""]
lowercase_ = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
lowercase_ = dataset.map(SCREAMING_SNAKE_CASE_ , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
UpperCAmelCase : Optional[int] = parser.parse_args()
main(args)
| 364
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 313
| 0
|
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : List[str] = "▁"
UpperCAmelCase : str = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class SCREAMING_SNAKE_CASE__ ( a__ , unittest.TestCase ):
lowercase__ = BertGenerationTokenizer
lowercase__ = False
lowercase__ = True
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
super().setUp()
lowercase_ = BertGenerationTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_)
tokenizer.save_pretrained(self.tmpdirname)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = '<s>'
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<unk>""")
self.assertEqual(vocab_keys[1] , """<s>""")
self.assertEqual(vocab_keys[-1] , """<pad>""")
self.assertEqual(len(SCREAMING_SNAKE_CASE_) , 1_0_0_2)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = BertGenerationTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_)
lowercase_ = tokenizer.tokenize("""This is a test""")
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
lowercase_ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
lowercase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
lowercase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_)
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""")
@slow
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = 'Hello World!'
lowercase_ = [1_8_5_3_6, 2_2_6_0, 1_0_1]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_))
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
lowercase_ = [
8_7_1,
4_1_9,
3_5_8,
9_4_6,
9_9_1,
2_5_2_1,
4_5_2,
3_5_8,
1_3_5_7,
3_8_7,
7_7_5_1,
3_5_3_6,
1_1_2,
9_8_5,
4_5_6,
1_2_6,
8_6_5,
9_3_8,
5_4_0_0,
5_7_3_4,
4_5_8,
1_3_6_8,
4_6_7,
7_8_6,
2_4_6_2,
5_2_4_6,
1_1_5_9,
6_3_3,
8_6_5,
4_5_1_9,
4_5_7,
5_8_2,
8_5_2,
2_5_5_7,
4_2_7,
9_1_6,
5_0_8,
4_0_5,
3_4_3_2_4,
4_9_7,
3_9_1,
4_0_8,
1_1_3_4_2,
1_2_4_4,
3_8_5,
1_0_0,
9_3_8,
9_8_5,
4_5_6,
5_7_4,
3_6_2,
1_2_5_9_7,
3_2_0_0,
3_1_2_9,
1_1_7_2,
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_))
@require_torch
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
lowercase_ = list(self.big_tokenizer.get_vocab().keys())[:1_0]
lowercase_ = ' '.join(SCREAMING_SNAKE_CASE_)
lowercase_ = self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , return_token_type_ids=SCREAMING_SNAKE_CASE_)
lowercase_ = self.big_tokenizer.batch_encode_plus(
[sequence + """ """ + sequence] , return_tensors="""pt""" , return_token_type_ids=SCREAMING_SNAKE_CASE_)
lowercase_ = BertGenerationConfig()
lowercase_ = BertGenerationEncoder(SCREAMING_SNAKE_CASE_)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE_)
model(**SCREAMING_SNAKE_CASE_)
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = {'input_ids': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name="""google/bert_for_seq_generation_L-24_bbc_encoder""" , revision="""c817d1fd1be2ffa69431227a1fe320544943d4db""" , )
| 365
|
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x20000 and cp <= 0x2a6df) #
or (cp >= 0x2a700 and cp <= 0x2b73f) #
or (cp >= 0x2b740 and cp <= 0x2b81f) #
or (cp >= 0x2b820 and cp <= 0x2ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2f800 and cp <= 0x2fa1f) #
): #
return True
return False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
for char in word:
lowercase_ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase_ = set()
for token in tokens:
lowercase_ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowercase_ = list(__lowerCAmelCase )
return word_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
lowercase_ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowercase_ = bert_tokens
lowercase_ , lowercase_ = 0, len(__lowerCAmelCase )
while start < end:
lowercase_ = True
if is_chinese(bert_word[start] ):
lowercase_ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowercase_ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowercase_ = """##""" + bert_word[j]
lowercase_ = start + i
lowercase_ = False
break
if single_word:
start += 1
return bert_word
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
lowercase_ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=5_12 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = []
for id in input_ids:
lowercase_ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowercase_ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowercase_ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.readlines()
lowercase_ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowercase_ = LTP(args.ltp ) # faster in GPU device
lowercase_ = BertTokenizer.from_pretrained(args.bert )
lowercase_ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowercase_ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
UpperCAmelCase : int = parser.parse_args()
main(args)
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase : Optional[Any] = 1.6021E-19 # units = C
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
lowercase_ = int(__lowerCAmelCase )
lowercase_ = int(__lowerCAmelCase )
lowercase_ = []
for temp in range(int(__lowerCAmelCase ) ):
series.append(F'''1 / {pow(temp + 1 , int(__lowerCAmelCase ) )}''' if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[str] = int(input("Enter the last number (nth term) of the P-Series"))
UpperCAmelCase : Tuple = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 313
| 0
|
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _SCREAMING_SNAKE_CASE () -> Tuple:
'''simple docstring'''
lowercase_ = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=__lowerCAmelCase , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=__lowerCAmelCase , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=__lowerCAmelCase )
return parser.parse_args()
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = parse_args()
# Import training_script as a module.
lowercase_ = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowercase_ = script_fpath.stem
lowercase_ = importlib.import_module(__lowerCAmelCase )
# Patch sys.argv
lowercase_ = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 367
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
self.assertEqual(x.component(0) , 1)
self.assertEqual(x.component(2) , 3)
lowercase_ = Vector()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(lowerCAmelCase_) , """(0,0,0,0,0,1)""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3, 4])
self.assertEqual(len(lowerCAmelCase_) , 4)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = Vector([1, 2])
lowercase_ = Vector([1, 2, 3, 4, 5])
lowercase_ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
lowercase_ = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3)
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3)
self.assertEqual(z.euclidean_length() , 0)
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x + y).component(0) , 2)
self.assertEqual((x + y).component(1) , 3)
self.assertEqual((x + y).component(2) , 4)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x - y).component(0) , 0)
self.assertEqual((x - y).component(1) , 1)
self.assertEqual((x - y).component(2) , 2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([2, -1, 4]) # for test of dot product
lowercase_ = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0) , """(3.0,6.0,9.0)""")
self.assertEqual((a * b) , 0)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.assertEqual(str(zero_vector(1_0)).count("""0""") , 1_0)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1)) , """(0,1,0)""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 0, 1])
self.assertEqual(str(axpy(2 , lowerCAmelCase_ , lowerCAmelCase_)) , """(3,4,7)""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0, 0, 0, 0])
lowercase_ = x.copy()
self.assertEqual(str(lowerCAmelCase_) , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0])
x.change_component(0 , 0)
x.change_component(1 , 1)
self.assertEqual(str(lowerCAmelCase_) , """(0,1,0)""")
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y] , a.minor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y] , a.cofactor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(-5 , a.determinant())
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3)
lowercase_ = Vector([1, 2, 3])
self.assertEqual("""(14,32,50)""" , str(a * x))
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
a.change_component(0 , 2 , 5)
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(7 , a.component(2 , 1) , 0.01)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5)) , )
if __name__ == "__main__":
unittest.main()
| 313
| 0
|
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class SCREAMING_SNAKE_CASE__ :
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple):
"""simple docstring"""
return None
class SCREAMING_SNAKE_CASE__ :
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
return None
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = [
# (model_name, model_kwargs)
("""bert-base-cased""", {}),
("""gpt2""", {"""use_cache""": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__a , """tf""" , 1_2 , **__a)
@require_torch
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__a , """pt""" , 1_2 , **__a)
@require_torch
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
from transformers import BertModel
lowercase_ = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""") as vocab_file:
vocab_file.write("""\n""".join(__a))
vocab_file.flush()
lowercase_ = BertTokenizerFast(vocab_file.name)
with TemporaryDirectory() as bert_save_dir:
lowercase_ = BertModel(BertConfig(vocab_size=len(__a)))
model.save_pretrained(__a)
self._test_export(__a , """pt""" , 1_2 , __a)
@require_tf
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase_ = self._test_export(__a , """tf""" , 1_2 , **__a)
lowercase_ = quantize(Path(__a))
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__a).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""")
@require_torch
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase_ = self._test_export(__a , """pt""" , 1_2 , **__a)
lowercase_ = quantize(__a)
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__a).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""")
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Any):
"""simple docstring"""
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowercase_ = Path(__a).joinpath("""model.onnx""")
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__a , __a , __a , __a , __a , **__a)
return path
except Exception as e:
self.fail(__a)
@require_torch
@require_tokenizers
@slow
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
from transformers import BertModel
lowercase_ = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
lowercase_ = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
self._test_infer_dynamic_axis(__a , __a , """pt""")
@require_tf
@require_tokenizers
@slow
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
from transformers import TFBertModel
lowercase_ = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
lowercase_ = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
self._test_infer_dynamic_axis(__a , __a , """tf""")
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = FeatureExtractionPipeline(__a , __a)
lowercase_ = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
lowercase_ , lowercase_ , lowercase_ , lowercase_ = infer_shapes(__a , __a)
# Assert all variables are present
self.assertEqual(len(__a) , len(__a))
self.assertTrue(all(var_name in shapes for var_name in variable_names))
self.assertSequenceEqual(variable_names[:3] , __a)
self.assertSequenceEqual(variable_names[3:] , __a)
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: """batch""", 1: """sequence"""})
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] , {0: """batch""", 1: """sequence"""})
self.assertDictEqual(shapes["""output_1"""] , {0: """batch"""})
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = ["""input_ids""", """attention_mask""", """token_type_ids"""]
lowercase_ = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
lowercase_ , lowercase_ = ensure_valid_input(FuncContiguousArgs() , __a , __a)
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__a) , 3)
# Should have exactly the same input names
self.assertEqual(set(__a) , set(__a))
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__a , (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]))
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowercase_ , lowercase_ = ensure_valid_input(FuncNonContiguousArgs() , __a , __a)
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__a) , 1)
self.assertEqual(len(__a) , 1)
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["""input_ids"""])
self.assertEqual(ordered_input_names[0] , """input_ids""")
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""") , """-test""")
self.assertEqual("""/home/something/my_fake_model-test.onnx""" , generated.as_posix())
| 368
|
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < end:
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ , lowercase_ = _in_place_partition(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
count += _in_place_quick_sort(__lowerCAmelCase , __lowerCAmelCase , p - 1 )
count += _in_place_quick_sort(__lowerCAmelCase , p + 1 , __lowerCAmelCase )
return count
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ = start - 1
for index in range(__lowerCAmelCase , __lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowercase_ = new_pivot_index + 1
lowercase_ = a[new_pivot_index]
lowercase_ = a[index]
lowercase_ = temp
lowercase_ = a[new_pivot_index + 1]
lowercase_ = a[end]
lowercase_ = temp
return new_pivot_index + 1, count
UpperCAmelCase : Union[str, Any] = TemporaryFile()
UpperCAmelCase : Optional[int] = 100 # 1000 elements are to be sorted
UpperCAmelCase , UpperCAmelCase : List[str] = 0, 1 # mean and standard deviation
UpperCAmelCase : Optional[Any] = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase : List[str] = np.load(outfile)
UpperCAmelCase : List[Any] = len(M) - 1
UpperCAmelCase : Optional[int] = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 313
| 0
|
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = old_name
if "patch_embed" in old_name:
lowercase_ = old_name.split(""".""" )
if layer == "0":
lowercase_ = old_name.replace("""0""" , """convolution1""" )
elif layer == "1":
lowercase_ = old_name.replace("""1""" , """batchnorm_before""" )
elif layer == "3":
lowercase_ = old_name.replace("""3""" , """convolution2""" )
else:
lowercase_ = old_name.replace("""4""" , """batchnorm_after""" )
if "network" in old_name and re.search(R"""\d\.\d""" , lowerCAmelCase_ ):
lowercase_ = r'\b\d{2}\b'
if bool(re.search(lowerCAmelCase_ , lowerCAmelCase_ ) ):
lowercase_ = re.search(R"""\d\.\d\d.""" , lowerCAmelCase_ ).group()
else:
lowercase_ = re.search(R"""\d\.\d.""" , lowerCAmelCase_ ).group()
if int(match[0] ) < 6:
lowercase_ = old_name.replace(lowerCAmelCase_ , """""" )
lowercase_ = trimmed_name.replace("""network""" , match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
lowercase_ = 'intermediate_stages.' + trimmed_name
else:
lowercase_ = old_name.replace(lowerCAmelCase_ , """""" )
if int(match[2] ) < num_meta4D_last_stage:
lowercase_ = trimmed_name.replace("""network""" , """meta4D_layers.blocks.""" + match[2] )
else:
lowercase_ = str(int(match[2] ) - num_meta4D_last_stage )
lowercase_ = trimmed_name.replace("""network""" , """meta3D_layers.blocks.""" + layer_index )
if "norm1" in old_name:
lowercase_ = trimmed_name.replace("""norm1""" , """layernorm1""" )
elif "norm2" in old_name:
lowercase_ = trimmed_name.replace("""norm2""" , """layernorm2""" )
elif "fc1" in old_name:
lowercase_ = trimmed_name.replace("""fc1""" , """linear_in""" )
elif "fc2" in old_name:
lowercase_ = trimmed_name.replace("""fc2""" , """linear_out""" )
lowercase_ = 'last_stage.' + trimmed_name
elif "network" in old_name and re.search(R""".\d.""" , lowerCAmelCase_ ):
lowercase_ = old_name.replace("""network""" , """intermediate_stages""" )
if "fc" in new_name:
lowercase_ = new_name.replace("""fc""" , """convolution""" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
lowercase_ = new_name.replace("""norm1""" , """batchnorm_before""" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
lowercase_ = new_name.replace("""norm2""" , """batchnorm_after""" )
if "proj" in new_name:
lowercase_ = new_name.replace("""proj""" , """projection""" )
if "dist_head" in new_name:
lowercase_ = new_name.replace("""dist_head""" , """distillation_classifier""" )
elif "head" in new_name:
lowercase_ = new_name.replace("""head""" , """classifier""" )
elif "patch_embed" in new_name:
lowercase_ = 'efficientformer.' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
lowercase_ = new_name.replace("""norm""" , """layernorm""" )
lowercase_ = 'efficientformer.' + new_name
else:
lowercase_ = 'efficientformer.encoder.' + new_name
return new_name
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
for key in checkpoint.copy().keys():
lowercase_ = checkpoint.pop(lowerCAmelCase_ )
lowercase_ = val
return checkpoint
def _SCREAMING_SNAKE_CASE () -> Any:
'''simple docstring'''
lowercase_ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase_ = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return image
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = torch.load(lowerCAmelCase_ , map_location="""cpu""" )['model']
lowercase_ = EfficientFormerConfig.from_json_file(lowerCAmelCase_ )
lowercase_ = EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_ )
lowercase_ = '_'.join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )
lowercase_ = config.depths[-1] - config.num_metaad_blocks + 1
lowercase_ = convert_torch_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
lowercase_ = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
# prepare image
lowercase_ = prepare_img()
lowercase_ = 2_56
lowercase_ = 2_24
lowercase_ = EfficientFormerImageProcessor(
size={"""shortest_edge""": image_size} , crop_size={"""height""": crop_size, """width""": crop_size} , resample=pillow_resamplings["""bicubic"""] , )
lowercase_ = processor(images=lowerCAmelCase_ , return_tensors="""pt""" ).pixel_values
# original processing pipeline
lowercase_ = Compose(
[
Resize(lowerCAmelCase_ , interpolation=pillow_resamplings["""bicubic"""] ),
CenterCrop(lowerCAmelCase_ ),
ToTensor(),
Normalize(lowerCAmelCase_ , lowerCAmelCase_ ),
] )
lowercase_ = image_transforms(lowerCAmelCase_ ).unsqueeze(0 )
assert torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase_ = model(lowerCAmelCase_ )
lowercase_ = outputs.logits
lowercase_ = (1, 10_00)
if "l1" in model_name:
lowercase_ = torch.Tensor(
[-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
assert torch.allclose(logits[0, :10] , lowerCAmelCase_ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
lowercase_ = torch.Tensor(
[-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
assert torch.allclose(logits[0, :10] , lowerCAmelCase_ , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
lowercase_ = torch.Tensor(
[-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(lowerCAmelCase_ )
print(F'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print("""Pushing model to the hub...""" )
model.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message="""Add model""" , use_temp_dir=lowerCAmelCase_ , )
processor.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message="""Add image processor""" , use_temp_dir=lowerCAmelCase_ , )
if __name__ == "__main__":
UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path",
default=None,
type=str,
required=True,
help="Path to EfficientFormer pytorch checkpoint.",
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for EfficientFormer model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
parser.set_defaults(push_to_hub=True)
UpperCAmelCase : int = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ = """"""
else:
lowercase_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''' )
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ = in_proj_weight[
: config.hidden_size, :
]
lowercase_ = in_proj_bias[: config.hidden_size]
lowercase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ = in_proj_bias[-config.hidden_size :]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = dct.pop(__lowerCAmelCase )
lowercase_ = val
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = ViTMSNConfig()
lowercase_ = 10_00
lowercase_ = """datasets/huggingface/label-files"""
lowercase_ = """imagenet-1k-id2label.json"""
lowercase_ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase ) , """r""" ) )
lowercase_ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowercase_ = idalabel
lowercase_ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase_ = 3_84
lowercase_ = 15_36
lowercase_ = 6
elif "l16" in checkpoint_url:
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
elif "b4" in checkpoint_url:
lowercase_ = 4
elif "l7" in checkpoint_url:
lowercase_ = 7
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
lowercase_ = ViTMSNModel(__lowerCAmelCase )
lowercase_ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""target_encoder"""]
lowercase_ = ViTImageProcessor(size=config.image_size )
remove_projection_head(__lowerCAmelCase )
lowercase_ = create_rename_keys(__lowerCAmelCase , base_model=__lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase , base_model=__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
lowercase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
lowercase_ = ViTImageProcessor(
size=config.image_size , image_mean=__lowerCAmelCase , image_std=__lowerCAmelCase )
lowercase_ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowercase_ = model(**__lowerCAmelCase )
lowercase_ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowercase_ = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
lowercase_ = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
lowercase_ = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
lowercase_ = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
lowercase_ = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __lowerCAmelCase , atol=1E-4 )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "perceiver"
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str]=2_5_6 , lowerCAmelCase_ : Dict=1_2_8_0 , lowerCAmelCase_ : List[Any]=7_6_8 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[Any]=2_6 , lowerCAmelCase_ : Optional[Any]=8 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Optional[Any]="kv" , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Union[str, Any]=0.02 , lowerCAmelCase_ : List[Any]=1E-12 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Union[str, Any]=2_6_2 , lowerCAmelCase_ : Union[str, Any]=2_0_4_8 , lowerCAmelCase_ : Any=5_6 , lowerCAmelCase_ : int=[3_6_8, 4_9_6] , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : Dict=1_9_2_0 , lowerCAmelCase_ : Optional[Any]=1_6 , lowerCAmelCase_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **lowerCAmelCase_ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_)
lowercase_ = num_latents
lowercase_ = d_latents
lowercase_ = d_model
lowercase_ = num_blocks
lowercase_ = num_self_attends_per_block
lowercase_ = num_self_attention_heads
lowercase_ = num_cross_attention_heads
lowercase_ = qk_channels
lowercase_ = v_channels
lowercase_ = cross_attention_shape_for_attention
lowercase_ = self_attention_widening_factor
lowercase_ = cross_attention_widening_factor
lowercase_ = hidden_act
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = use_query_residual
# masked language modeling attributes
lowercase_ = vocab_size
lowercase_ = max_position_embeddings
# image classification attributes
lowercase_ = image_size
# flow attributes
lowercase_ = train_size
# multimodal autoencoding attributes
lowercase_ = num_frames
lowercase_ = audio_samples_per_frame
lowercase_ = samples_per_patch
lowercase_ = output_shape
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
if self.task == "multiple-choice":
lowercase_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""inputs""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
@property
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return 1E-4
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[TensorType] = None , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 4_0 , lowerCAmelCase_ : int = 4_0 , ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , 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
lowercase_ = preprocessor.num_special_tokens_to_add(lowerCAmelCase_)
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase_)
# Generate dummy inputs according to compute batch and sequence
lowercase_ = [""" """.join(["""a"""]) * seq_length] * batch_size
lowercase_ = dict(preprocessor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""input_ids""")
return inputs
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase_ = self._generate_dummy_images(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = dict(preprocessor(images=lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""pixel_values""")
return inputs
else:
raise ValueError(
"""Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""")
| 313
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|
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : int = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase_ ):
lowercase__ = '''van'''
def __init__( self : Dict , lowerCAmelCase_ : Dict=2_2_4 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : Tuple=[7, 3, 3, 3] , lowerCAmelCase_ : Union[str, Any]=[4, 2, 2, 2] , lowerCAmelCase_ : List[str]=[6_4, 1_2_8, 3_2_0, 5_1_2] , lowerCAmelCase_ : Any=[3, 3, 1_2, 3] , lowerCAmelCase_ : Dict=[8, 8, 4, 4] , lowerCAmelCase_ : str="gelu" , lowerCAmelCase_ : Optional[Any]=0.02 , lowerCAmelCase_ : Optional[Any]=1E-6 , lowerCAmelCase_ : int=1E-2 , lowerCAmelCase_ : Optional[Any]=0.0 , lowerCAmelCase_ : Tuple=0.0 , **lowerCAmelCase_ : Dict , ):
"""simple docstring"""
super().__init__(**__snake_case)
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = patch_sizes
lowercase_ = strides
lowercase_ = hidden_sizes
lowercase_ = depths
lowercase_ = mlp_ratios
lowercase_ = hidden_act
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = layer_scale_init_value
lowercase_ = drop_path_rate
lowercase_ = dropout_rate
| 371
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BarthezTokenizer
lowercase__ = BarthezTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
super().setUp()
lowercase_ = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
tokenizer.save_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase_)
lowercase_ = tokenizer
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_) , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<s>""")
self.assertEqual(vocab_keys[1] , """<pad>""")
self.assertEqual(vocab_keys[-1] , """<mask>""")
self.assertEqual(len(lowerCAmelCase_) , 1_0_1_1_2_2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2)
@require_torch
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowercase_ = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
lowercase_ = self.tokenizer(
lowerCAmelCase_ , max_length=len(lowerCAmelCase_) , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_)
self.assertEqual((2, 6) , batch.input_ids.shape)
self.assertEqual((2, 6) , batch.attention_mask.shape)
lowercase_ = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
lowercase_ = rust_tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase_ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""moussaKam/mbarthez""" , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" , sequences=lowerCAmelCase_ , )
| 313
| 0
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase_ = 1
lowercase_ = 2
while i * i <= n:
lowercase_ = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def _SCREAMING_SNAKE_CASE () -> int:
'''simple docstring'''
lowercase_ = 1
lowercase_ = 1
while True:
i += 1
t_num += i
if count_divisors(__lowerCAmelCase ) > 5_00:
break
return t_num
if __name__ == "__main__":
print(solution())
| 350
|
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Optional[Any] = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class SCREAMING_SNAKE_CASE__ :
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = "gelu"
def __init__( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Any=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : str=9_9 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Dict=5 , lowerCAmelCase_ : Union[str, Any]=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Optional[int]=2_0 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
lowercase_ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
lowercase_ = np.concatenate([input_ids, eos_tensor] , axis=1)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase_ = prepare_pegasus_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.not_equal(__lowerCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowercase_ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxPegasusModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=None , **lowerCAmelCase_ : Optional[int]):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=lowerCAmelCase_)
lowercase_ = np.ones((1, 1))
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
lowercase_ = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")
lowercase_ = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
lowercase_ = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""np""" , truncation=lowerCAmelCase_ , max_length=5_1_2 , padding=lowerCAmelCase_)
lowercase_ = model.generate(**lowerCAmelCase_ , num_beams=2).sequences
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_)
assert tgt_text == decoded
| 313
| 0
|
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class SCREAMING_SNAKE_CASE__ :
@staticmethod
def _UpperCAmelCase ( *lowerCAmelCase_ : Any , **lowerCAmelCase_ : Any):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[int]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
UpperCAmelCase : int = (
"https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)
@is_pipeline_test
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = pipeline(
"""document-question-answering""" , model=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_)
lowercase_ = INVOICE_URL
lowercase_ = list(zip(*apply_tesseract(load_image(lowerCAmelCase_) , lowerCAmelCase_ , """""")))
lowercase_ = """What is the placebo?"""
lowercase_ = [
{
"""image""": load_image(lowerCAmelCase_),
"""question""": question,
},
{
"""image""": image,
"""question""": question,
},
{
"""image""": image,
"""question""": question,
"""word_boxes""": word_boxes,
},
]
return dqa_pipeline, examples
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = dqa_pipeline(lowerCAmelCase_ , top_k=2)
self.assertEqual(
lowerCAmelCase_ , [
[
{"""score""": ANY(lowerCAmelCase_), """answer""": ANY(lowerCAmelCase_), """start""": ANY(lowerCAmelCase_), """end""": ANY(lowerCAmelCase_)},
{"""score""": ANY(lowerCAmelCase_), """answer""": ANY(lowerCAmelCase_), """start""": ANY(lowerCAmelCase_), """end""": ANY(lowerCAmelCase_)},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = pipeline("""document-question-answering""" , model="""hf-internal-testing/tiny-random-layoutlmv2""")
lowercase_ = INVOICE_URL
lowercase_ = """How many cats are there?"""
lowercase_ = [
{"""score""": 0.0_001, """answer""": """oy 2312/2019""", """start""": 3_8, """end""": 3_9},
{"""score""": 0.0_001, """answer""": """oy 2312/2019 DUE""", """start""": 3_8, """end""": 4_0},
]
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(nested_simplify(lowerCAmelCase_ , decimals=4) , lowerCAmelCase_)
lowercase_ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2)
self.assertEqual(nested_simplify(lowerCAmelCase_ , decimals=4) , lowerCAmelCase_)
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowercase_ = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(lowerCAmelCase_ , [])
# We can optionnally pass directly the words and bounding boxes
lowercase_ = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
lowercase_ = []
lowercase_ = []
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , words=lowerCAmelCase_ , boxes=lowerCAmelCase_ , top_k=2)
self.assertEqual(lowerCAmelCase_ , [])
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , )
lowercase_ = INVOICE_URL
lowercase_ = """What is the invoice number?"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_944, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_009, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
lowercase_ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_944, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_009, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
lowercase_ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
[
{"""score""": 0.9_944, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_009, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , max_seq_len=5_0 , )
lowercase_ = INVOICE_URL
lowercase_ = """What is the invoice number?"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_974, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
{"""score""": 0.9_948, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
lowercase_ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_974, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
{"""score""": 0.9_948, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
lowercase_ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
[
{"""score""": 0.9_974, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
{"""score""": 0.9_948, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=lowerCAmelCase_)
lowercase_ = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=lowerCAmelCase_ , revision="""3dc6de3""" , )
lowercase_ = INVOICE_URL
lowercase_ = """What is the invoice number?"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.4_251, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_819, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
] , )
lowercase_ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.4_251, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_819, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
] , )
lowercase_ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
[
{"""score""": 0.4_251, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_819, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
]
]
* 2 , )
lowercase_ = list(zip(*apply_tesseract(load_image(lowerCAmelCase_) , lowerCAmelCase_ , """""")))
# This model should also work if `image` is set to None
lowercase_ = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.4_251, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.0_819, """answer""": """1110212019""", """start""": 2_3, """end""": 2_3},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=lowerCAmelCase_)
lowercase_ = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=lowerCAmelCase_ , revision="""3dc6de3""" , max_seq_len=5_0 , )
lowercase_ = INVOICE_URL
lowercase_ = """What is the invoice number?"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_999, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.9_998, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
lowercase_ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
[
{"""score""": 0.9_999, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.9_998, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
]
]
* 2 , )
lowercase_ = list(zip(*apply_tesseract(load_image(lowerCAmelCase_) , lowerCAmelCase_ , """""")))
# This model should also work if `image` is set to None
lowercase_ = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4) , [
{"""score""": 0.9_999, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
{"""score""": 0.9_998, """answer""": """us-001""", """start""": 1_6, """end""": 1_6},
] , )
@slow
@require_torch
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = pipeline(
"""document-question-answering""" , model="""naver-clova-ix/donut-base-finetuned-docvqa""" , tokenizer=AutoTokenizer.from_pretrained("""naver-clova-ix/donut-base-finetuned-docvqa""") , feature_extractor="""naver-clova-ix/donut-base-finetuned-docvqa""" , )
lowercase_ = INVOICE_URL
lowercase_ = """What is the invoice number?"""
lowercase_ = dqa_pipeline(image=lowerCAmelCase_ , question=lowerCAmelCase_ , top_k=2)
self.assertEqual(nested_simplify(lowerCAmelCase_ , decimals=4) , [{"""answer""": """us-001"""}])
@require_tf
@unittest.skip("""Document question answering not implemented in TF""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
| 351
|
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = None
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = True
lowercase__ = None
lowercase__ = 1
lowercase__ = None
lowercase__ = False
lowercase__ = None
lowercase__ = None
def _UpperCAmelCase ( self : int):
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(lowerCAmelCase_) for k, v in self.__dict__.items()})
| 313
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict=1_3 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : List[str]=4 , lowerCAmelCase_ : Dict=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : int=[2, 2, 3, 2] , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=3_7 , lowerCAmelCase_ : Optional[int]="gelu" , lowerCAmelCase_ : Tuple=1_0 , lowerCAmelCase_ : Tuple=0.02 , lowerCAmelCase_ : int=["stage2", "stage3", "stage4"] , lowerCAmelCase_ : Any=[2, 3, 4] , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = num_stages
lowercase_ = hidden_sizes
lowercase_ = depths
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = num_labels
lowercase_ = initializer_range
lowercase_ = out_features
lowercase_ = out_indices
lowercase_ = scope
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.num_labels)
lowercase_ = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = ConvNextVaModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = ConvNextVaForImageClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = ConvNextVaBackbone(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])
# verify backbone works with out_features=None
lowercase_ = None
lowercase_ = ConvNextVaBackbone(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowercase__ = (
{"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = ConvNextVaModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""")
def _UpperCAmelCase ( self : str):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase_ = True
if model_class.__name__ in [
*get_values(lowerCAmelCase_),
*get_values(lowerCAmelCase_),
]:
continue
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.train()
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
lowercase_ = model(**lowerCAmelCase_).loss
loss.backward()
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase_ = False
lowercase_ = True
if (
model_class.__name__
in [*get_values(lowerCAmelCase_), *get_values(lowerCAmelCase_)]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.gradient_checkpointing_enable()
model.train()
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
lowercase_ = model(**lowerCAmelCase_).loss
loss.backward()
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_) , expected_num_stages + 1)
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = ConvNextVaModel.from_pretrained(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
'''simple docstring'''
lowercase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""") if is_vision_available() else None
@slow
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""").to(lowerCAmelCase_)
lowercase_ = self.default_image_processor
lowercase_ = prepare_img()
lowercase_ = preprocessor(images=lowerCAmelCase_ , return_tensors="""pt""").to(lowerCAmelCase_)
# forward pass
with torch.no_grad():
lowercase_ = model(**lowerCAmelCase_)
# verify the logits
lowercase_ = torch.Size((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , lowerCAmelCase_)
lowercase_ = torch.tensor([0.9_996, 0.1_966, -0.4_386]).to(lowerCAmelCase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1E-4))
| 352
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : List[Any] = {
"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",
},
}
UpperCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 1_6384,
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = LEDTokenizer
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]="replace" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : List[Any]="</s>" , lowerCAmelCase_ : Optional[Any]="<s>" , lowerCAmelCase_ : Union[str, Any]="<unk>" , lowerCAmelCase_ : List[str]="<pad>" , lowerCAmelCase_ : Dict="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop("""type"""))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**lowerCAmelCase_)
lowercase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ = """post_processor"""
lowercase_ = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
if tokenizer_component_instance:
lowercase_ = 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:
lowercase_ = tuple(state["""sep"""])
if "cls" in state:
lowercase_ = tuple(state["""cls"""])
lowercase_ = False
if state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = add_prefix_space
lowercase_ = True
if state.get("""trim_offsets""" , lowerCAmelCase_) != trim_offsets:
lowercase_ = trim_offsets
lowercase_ = True
if changes_to_apply:
lowercase_ = getattr(lowerCAmelCase_ , state.pop("""type"""))
lowercase_ = component_class(**lowerCAmelCase_)
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""")
return None
return str(self._mask_token)
@mask_token.setter
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else value
lowercase_ = value
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
lowercase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_)
return tuple(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = [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 _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
lowercase_ = [self.sep_token_id]
lowercase_ = [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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = super()._pad(
encoded_inputs=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding_strategy=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ = len(encoded_inputs["""global_attention_mask"""]) != len(lowerCAmelCase_)
if needs_to_be_padded:
lowercase_ = len(lowerCAmelCase_) - 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`
lowercase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))
return encoded_inputs
| 313
| 0
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 50 ) -> int:
'''simple docstring'''
lowercase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"{solution() = }")
| 353
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ , lowercase_ = text, pattern
lowercase_ , lowercase_ = len(lowerCAmelCase_), len(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1):
if char == self.pattern[i]:
return i
return -1
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : int):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for i in range(self.textLen - self.patLen + 1):
lowercase_ = self.mismatch_in_text(lowerCAmelCase_)
if mismatch_index == -1:
positions.append(lowerCAmelCase_)
else:
lowercase_ = self.match_in_pattern(self.text[mismatch_index])
lowercase_ = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
UpperCAmelCase : List[Any] = "ABAABA"
UpperCAmelCase : str = "AB"
UpperCAmelCase : Dict = BoyerMooreSearch(text, pattern)
UpperCAmelCase : List[Any] = bms.bad_character_heuristic()
if len(positions) == 0:
print("No match found")
else:
print("Pattern found in following positions: ")
print(positions)
| 354
|
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=6_4 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=3_7 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Dict=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = MegatronBertModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MegatronBertForMaskedLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = MegatronBertForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForNextSentencePrediction(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForPreTraining(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , next_sentence_label=lowerCAmelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = MegatronBertForQuestionAnswering(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForTokenClassification(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MegatronBertForMultipleChoice(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
# test_resize_embeddings = False
lowercase__ = False
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
if return_labels:
if model_class in get_values(lowerCAmelCase_):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_)
return inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = MegatronBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return torch.tensor(
__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase , )
UpperCAmelCase : Any = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
@unittest.skip("""Model is not available.""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ = os.path.join(os.environ["""MYDIR"""] , lowerCAmelCase_)
lowercase_ = MegatronBertModel.from_pretrained(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.half()
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = torch.Size((1, 9, 1_0_2_4))
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3):
for jj in range(3):
lowercase_ = output[0, ii, jj]
lowercase_ = expected[3 * ii + jj]
lowercase_ = """ii={} jj={} a={} b={}""".format(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
self.assertTrue(math.isclose(lowerCAmelCase_ , lowerCAmelCase_ , rel_tol=lowerCAmelCase_ , abs_tol=lowerCAmelCase_) , msg=lowerCAmelCase_)
| 313
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "imagegpt"
lowercase__ = ["past_key_values"]
lowercase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[Any] , lowerCAmelCase_ : Optional[int]=5_1_2 + 1 , lowerCAmelCase_ : Dict=3_2 * 3_2 , lowerCAmelCase_ : List[str]=5_1_2 , lowerCAmelCase_ : Optional[Any]=2_4 , lowerCAmelCase_ : int=8 , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int="quick_gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=1E-5 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : str=False , **lowerCAmelCase_ : Union[str, Any] , ):
"""simple docstring"""
lowercase_ = vocab_size
lowercase_ = n_positions
lowercase_ = n_embd
lowercase_ = n_layer
lowercase_ = n_head
lowercase_ = n_inner
lowercase_ = activation_function
lowercase_ = resid_pdrop
lowercase_ = embd_pdrop
lowercase_ = attn_pdrop
lowercase_ = layer_norm_epsilon
lowercase_ = initializer_range
lowercase_ = scale_attn_weights
lowercase_ = use_cache
lowercase_ = scale_attn_by_inverse_layer_idx
lowercase_ = reorder_and_upcast_attn
lowercase_ = tie_word_embeddings
super().__init__(tie_word_embeddings=lowerCAmelCase_ , **lowerCAmelCase_)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
@property
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
])
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : "FeatureExtractionMixin" , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional["TensorType"] = None , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 3_2 , lowerCAmelCase_ : int = 3_2 , ):
"""simple docstring"""
lowercase_ = self._generate_dummy_images(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = dict(preprocessor(images=lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
return inputs
| 355
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows != columns:
lowercase_ = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__lowerCAmelCase )
lowercase_ = np.zeros((rows, columns) )
lowercase_ = np.zeros((rows, columns) )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase_ = (table[i][j] - total) / upper[j][j]
lowercase_ = 1
for j in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
lowercase_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = (EulerDiscreteScheduler,)
lowercase__ = 10
def _UpperCAmelCase ( self : Optional[int] , **lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = {
"""num_train_timesteps""": 1_1_0_0,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**lowerCAmelCase_)
return config
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02]):
self.check_over_configs(beta_start=lowerCAmelCase_ , beta_end=lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase_)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase_)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.scheduler_classes[0]
lowercase_ = self.get_scheduler_config()
lowercase_ = scheduler_class(**lowerCAmelCase_)
scheduler.set_timesteps(self.num_inference_steps)
lowercase_ = torch.manual_seed(0)
lowercase_ = self.dummy_model()
lowercase_ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase_ = sample.to(lowerCAmelCase_)
for i, t in enumerate(scheduler.timesteps):
lowercase_ = scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_)
lowercase_ = output.prev_sample
lowercase_ = torch.sum(torch.abs(lowerCAmelCase_))
lowercase_ = torch.mean(torch.abs(lowerCAmelCase_))
assert abs(result_sum.item() - 10.0_807) < 1E-2
assert abs(result_mean.item() - 0.0_131) < 1E-3
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.scheduler_classes[0]
lowercase_ = self.get_scheduler_config(prediction_type="""v_prediction""")
lowercase_ = scheduler_class(**lowerCAmelCase_)
scheduler.set_timesteps(self.num_inference_steps)
lowercase_ = torch.manual_seed(0)
lowercase_ = self.dummy_model()
lowercase_ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase_ = sample.to(lowerCAmelCase_)
for i, t in enumerate(scheduler.timesteps):
lowercase_ = scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_)
lowercase_ = output.prev_sample
lowercase_ = torch.sum(torch.abs(lowerCAmelCase_))
lowercase_ = torch.mean(torch.abs(lowerCAmelCase_))
assert abs(result_sum.item() - 0.0_002) < 1E-2
assert abs(result_mean.item() - 2.2676E-06) < 1E-3
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.scheduler_classes[0]
lowercase_ = self.get_scheduler_config()
lowercase_ = scheduler_class(**lowerCAmelCase_)
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase_)
lowercase_ = torch.manual_seed(0)
lowercase_ = self.dummy_model()
lowercase_ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowercase_ = sample.to(lowerCAmelCase_)
for t in scheduler.timesteps:
lowercase_ = scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_)
lowercase_ = output.prev_sample
lowercase_ = torch.sum(torch.abs(lowerCAmelCase_))
lowercase_ = torch.mean(torch.abs(lowerCAmelCase_))
assert abs(result_sum.item() - 10.0_807) < 1E-2
assert abs(result_mean.item() - 0.0_131) < 1E-3
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.scheduler_classes[0]
lowercase_ = self.get_scheduler_config()
lowercase_ = scheduler_class(**lowerCAmelCase_ , use_karras_sigmas=lowerCAmelCase_)
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase_)
lowercase_ = torch.manual_seed(0)
lowercase_ = self.dummy_model()
lowercase_ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowercase_ = sample.to(lowerCAmelCase_)
for t in scheduler.timesteps:
lowercase_ = scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_)
lowercase_ = output.prev_sample
lowercase_ = torch.sum(torch.abs(lowerCAmelCase_))
lowercase_ = torch.mean(torch.abs(lowerCAmelCase_))
assert abs(result_sum.item() - 124.52_299_499_511_719) < 1E-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963) < 1E-3
| 356
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [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(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = KandinskyImgaImgPipeline
lowercase__ = ["prompt", "image_embeds", "negative_image_embeds", "image"]
lowercase__ = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
lowercase__ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowercase__ = False
@property
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
return 3_2
@property
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
return 3_2
@property
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
return self.time_input_dim
@property
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
return self.time_input_dim * 4
@property
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
return 1_0_0
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
return tokenizer
@property
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
lowercase_ = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
lowercase_ = MultilingualCLIP(lowerCAmelCase_)
lowercase_ = text_encoder.eval()
return text_encoder
@property
def _UpperCAmelCase ( self : int):
"""simple docstring"""
torch.manual_seed(0)
lowercase_ = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_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""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
lowercase_ = UNetaDConditionModel(**lowerCAmelCase_)
return model
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
torch.manual_seed(0)
lowercase_ = VQModel(**self.dummy_movq_kwargs)
return model
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.dummy_text_encoder
lowercase_ = self.dummy_tokenizer
lowercase_ = self.dummy_unet
lowercase_ = self.dummy_movq
lowercase_ = {
"""num_train_timesteps""": 1_0_0_0,
"""beta_schedule""": """linear""",
"""beta_start""": 0.00_085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
lowercase_ = DDIMScheduler(**lowerCAmelCase_)
lowercase_ = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any]=0):
"""simple docstring"""
lowercase_ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
lowercase_ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowerCAmelCase_)
# create init_image
lowercase_ = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
lowercase_ = image.cpu().permute(0 , 2 , 3 , 1)[0]
lowercase_ = Image.fromarray(np.uinta(lowerCAmelCase_)).convert("""RGB""").resize((2_5_6, 2_5_6))
if str(lowerCAmelCase_).startswith("""mps"""):
lowercase_ = torch.manual_seed(lowerCAmelCase_)
else:
lowercase_ = torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
lowercase_ = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 6_4,
"""width""": 6_4,
"""num_inference_steps""": 1_0,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = """cpu"""
lowercase_ = self.get_dummy_components()
lowercase_ = self.pipeline_class(**lowerCAmelCase_)
lowercase_ = pipe.to(lowerCAmelCase_)
pipe.set_progress_bar_config(disable=lowerCAmelCase_)
lowercase_ = pipe(**self.get_dummy_inputs(lowerCAmelCase_))
lowercase_ = output.images
lowercase_ = pipe(
**self.get_dummy_inputs(lowerCAmelCase_) , return_dict=lowerCAmelCase_ , )[0]
lowercase_ = image[0, -3:, -3:, -1]
lowercase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowercase_ = np.array(
[0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233])
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 ):
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""")
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
lowercase_ = """A red cartoon frog, 4k"""
lowercase_ = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa)
pipe_prior.to(lowerCAmelCase_)
lowercase_ = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa)
lowercase_ = pipeline.to(lowerCAmelCase_)
pipeline.set_progress_bar_config(disable=lowerCAmelCase_)
lowercase_ = torch.Generator(device="""cpu""").manual_seed(0)
lowercase_ , lowercase_ = pipe_prior(
lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
lowercase_ = pipeline(
lowerCAmelCase_ , image=lowerCAmelCase_ , image_embeds=lowerCAmelCase_ , negative_image_embeds=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="""np""" , )
lowercase_ = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_)
| 357
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , 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]
| 313
| 0
|
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=10_24 , __lowerCAmelCase=10_24 , __lowerCAmelCase=False , **__lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = AutoTokenizer.from_pretrained(__lowerCAmelCase )
lowercase_ = SeqaSeqDataset(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , type_path="""train""" , **__lowerCAmelCase )
lowercase_ = tok.pad_token_id
def get_lens(__lowerCAmelCase ):
lowercase_ = tqdm(
DataLoader(__lowerCAmelCase , batch_size=5_12 , num_workers=8 , shuffle=__lowerCAmelCase , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
lowercase_ = []
for batch in dl:
lowercase_ = batch["""input_ids"""].ne(__lowerCAmelCase ).sum(1 ).tolist()
lowercase_ = batch["""labels"""].ne(__lowerCAmelCase ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(__lowerCAmelCase , __lowerCAmelCase ):
max_lens.append(max(__lowerCAmelCase , __lowerCAmelCase ) )
else:
max_lens.extend(__lowerCAmelCase )
return max_lens
lowercase_ = get_lens(__lowerCAmelCase )
lowercase_ = SeqaSeqDataset(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , type_path="""val""" , **__lowerCAmelCase )
lowercase_ = get_lens(__lowerCAmelCase )
pickle_save(__lowerCAmelCase , train_ds.len_file )
pickle_save(__lowerCAmelCase , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 358
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Dict=9_9 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : str=4 , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : List[Any]=0.02 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
lowercase_ = initializer_range
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowercase_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase_ , )
lowercase_ = prepare_blenderbot_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = 99
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
lowercase_ = input_ids.shape[0]
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._get_config_and_data()
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = lm_model(input_ids=lowerCAmelCase_)
lowercase_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa)
lowercase_ = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa)
lowercase_ = lm_model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_)
lowercase_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(lowerCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowercase__ = True
lowercase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = FlaxBlenderbotModelTester(self)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : str):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase_ = np.ones((1, 1)) * model.config.eos_token_id
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""")
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
lowercase_ = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
lowercase_ = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCAmelCase_)
lowercase_ = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")
lowercase_ = ["""Sam"""]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""jax""")
lowercase_ = model.generate(**lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = """Sam is a great name. It means \"sun\" in Gaelic."""
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , **lowerCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 313
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = StableUnCLIPImgaImgPipeline
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowercase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase__ = frozenset([] )
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = 3_2
lowercase_ = embedder_hidden_size
# image encoding components
lowercase_ = CLIPImageProcessor(crop_size=3_2 , size=3_2)
torch.manual_seed(0)
lowercase_ = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCAmelCase_ , projection_dim=lowerCAmelCase_ , num_hidden_layers=5 , num_attention_heads=4 , image_size=3_2 , intermediate_size=3_7 , patch_size=1 , ))
# regular denoising components
torch.manual_seed(0)
lowercase_ = StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase_)
lowercase_ = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""")
torch.manual_seed(0)
lowercase_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
torch.manual_seed(0)
lowercase_ = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCAmelCase_ , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ))
torch.manual_seed(0)
lowercase_ = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCAmelCase_ , layers_per_block=1 , upcast_attention=lowerCAmelCase_ , use_linear_projection=lowerCAmelCase_ , )
torch.manual_seed(0)
lowercase_ = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.00_085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=lowerCAmelCase_ , steps_offset=1 , )
torch.manual_seed(0)
lowercase_ = AutoencoderKL()
lowercase_ = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Any=0 , lowerCAmelCase_ : List[str]=True):
"""simple docstring"""
if str(lowerCAmelCase_).startswith("""mps"""):
lowercase_ = torch.manual_seed(lowerCAmelCase_)
else:
lowercase_ = torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
lowercase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
if pil_image:
lowercase_ = input_image * 0.5 + 0.5
lowercase_ = input_image.clamp(0 , 1)
lowercase_ = input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
lowercase_ = DiffusionPipeline.numpy_to_pil(lowerCAmelCase_)[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase_ = self.get_dummy_components()
lowercase_ = StableUnCLIPImgaImgPipeline(**lowerCAmelCase_)
lowercase_ = sd_pipe.to(lowerCAmelCase_)
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)
lowercase_ = self.get_dummy_inputs(lowerCAmelCase_)
inputs.update({"""image_embeds""": None})
lowercase_ = sd_pipe(**lowerCAmelCase_).images
lowercase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowercase_ = np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase_)
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCAmelCase_)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
lowercase_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy""")
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-l-img2img""" , torch_dtype=torch.floataa)
pipe.to(lowerCAmelCase_)
pipe.set_progress_bar_config(disable=lowerCAmelCase_)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="""cpu""").manual_seed(0)
lowercase_ = pipe(lowerCAmelCase_ , """anime turle""" , generator=lowerCAmelCase_ , output_type="""np""")
lowercase_ = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
lowercase_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy""")
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa)
pipe.to(lowerCAmelCase_)
pipe.set_progress_bar_config(disable=lowerCAmelCase_)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="""cpu""").manual_seed(0)
lowercase_ = pipe(lowerCAmelCase_ , """anime turle""" , generator=lowerCAmelCase_ , output_type="""np""")
lowercase_ = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa)
lowercase_ = pipe.to(lowerCAmelCase_)
pipe.set_progress_bar_config(disable=lowerCAmelCase_)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = pipe(
lowerCAmelCase_ , """anime turtle""" , num_inference_steps=2 , output_type="""np""" , )
lowercase_ = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 359
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313
| 0
|
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
UpperCAmelCase : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ["""integration""", """unit"""] ):
continue
item.add_marker(pytest.mark.unit )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
config.addinivalue_line("""markers""" , """torchaudio_latest: mark test to run with torchaudio>=0.12""" )
@pytest.fixture(autouse=__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = tmp_path_factory.getbasetemp() / """cache"""
lowercase_ = test_hf_cache_home / """datasets"""
lowercase_ = test_hf_cache_home / """metrics"""
lowercase_ = test_hf_cache_home / """modules"""
monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE""" , str(__lowerCAmelCase ) )
monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE""" , str(__lowerCAmelCase ) )
monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE""" , str(__lowerCAmelCase ) )
lowercase_ = test_hf_datasets_cache / """downloads"""
monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH""" , str(__lowerCAmelCase ) )
lowercase_ = test_hf_datasets_cache / """downloads""" / """extracted"""
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(__lowerCAmelCase ) )
@pytest.fixture(autouse=__lowerCAmelCase , scope="""session""" )
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Dict:
'''simple docstring'''
monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS""" , __lowerCAmelCase )
@pytest.fixture
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING""" , __lowerCAmelCase )
| 360
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 313
| 0
|
from math import factorial, radians
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 18 , __lowerCAmelCase = 10 ) -> float:
'''simple docstring'''
lowercase_ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
lowercase_ = radians(__lowerCAmelCase )
lowercase_ = angle_in_radians
lowercase_ = 3
lowercase_ = -1
for _ in range(__lowerCAmelCase ):
result += (b * (angle_in_radians**a)) / factorial(__lowerCAmelCase )
lowercase_ = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
__import__("doctest").testmod()
| 361
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 313
| 0
|
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCAmelCase : Any = pd.read_csv("sample_data.csv", header=None)
UpperCAmelCase : Union[str, Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
UpperCAmelCase : Union[str, Any] = df.iloc[:, 1:2]
UpperCAmelCase : Dict = actual_data.values.reshape(len_data, 1)
UpperCAmelCase : Union[str, Any] = MinMaxScaler().fit_transform(actual_data)
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : int = 5
UpperCAmelCase : Dict = 20
UpperCAmelCase : List[str] = len_data - periods * look_back
UpperCAmelCase : Optional[Any] = actual_data[:division]
UpperCAmelCase : Optional[Any] = actual_data[division - look_back :]
UpperCAmelCase : Optional[int] = [], []
UpperCAmelCase : List[Any] = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
UpperCAmelCase : Dict = np.array(train_x)
UpperCAmelCase : Optional[Any] = np.array(test_x)
UpperCAmelCase : Tuple = np.array([list(i.ravel()) for i in train_y])
UpperCAmelCase : Any = np.array([list(i.ravel()) for i in test_y])
UpperCAmelCase : Any = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
UpperCAmelCase : int = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
UpperCAmelCase : str = model.predict(x_test)
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
"""simple docstring"""
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 363
|
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase_ = 0 if allow_empty_subarrays else float("""-inf""" )
lowercase_ = 0.0
for num in arr:
lowercase_ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowercase_ = max(__lowerCAmelCase , __lowerCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : Union[str, Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 313
| 0
|
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 364
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 313
| 0
|
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> np.array:
'''simple docstring'''
lowercase_ = F'''{sampling_rate}'''
lowercase_ = """1"""
lowercase_ = """f32le"""
lowercase_ = [
"""ffmpeg""",
"""-i""",
"""pipe:0""",
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
try:
with subprocess.Popen(__lowerCAmelCase , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
lowercase_ = ffmpeg_process.communicate(__lowerCAmelCase )
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to load audio files from filename""" ) from error
lowercase_ = output_stream[0]
lowercase_ = np.frombuffer(__lowerCAmelCase , np.floataa )
if audio.shape[0] == 0:
raise ValueError("""Malformed soundfile""" )
return audio
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = "f32le" , ) -> List[Any]:
'''simple docstring'''
lowercase_ = F'''{sampling_rate}'''
lowercase_ = """1"""
if format_for_conversion == "s16le":
lowercase_ = 2
elif format_for_conversion == "f32le":
lowercase_ = 4
else:
raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
lowercase_ = platform.system()
if system == "Linux":
lowercase_ = """alsa"""
lowercase_ = """default"""
elif system == "Darwin":
lowercase_ = """avfoundation"""
lowercase_ = """:0"""
elif system == "Windows":
lowercase_ = """dshow"""
lowercase_ = """default"""
lowercase_ = [
"""ffmpeg""",
"""-f""",
format_,
"""-i""",
input_,
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-fflags""",
"""nobuffer""",
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
lowercase_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
lowercase_ = _ffmpeg_stream(__lowerCAmelCase , __lowerCAmelCase )
for item in iterator:
yield item
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = "f32le" , ) -> str:
'''simple docstring'''
if stream_chunk_s is not None:
lowercase_ = stream_chunk_s
else:
lowercase_ = chunk_length_s
lowercase_ = ffmpeg_microphone(__lowerCAmelCase , __lowerCAmelCase , format_for_conversion=__lowerCAmelCase )
if format_for_conversion == "s16le":
lowercase_ = np.intaa
lowercase_ = 2
elif format_for_conversion == "f32le":
lowercase_ = np.floataa
lowercase_ = 4
else:
raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
lowercase_ = chunk_length_s / 6
lowercase_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__lowerCAmelCase , (int, float) ):
lowercase_ = [stride_length_s, stride_length_s]
lowercase_ = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
lowercase_ = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
lowercase_ = datetime.datetime.now()
lowercase_ = datetime.timedelta(seconds=__lowerCAmelCase )
for item in chunk_bytes_iter(__lowerCAmelCase , __lowerCAmelCase , stride=(stride_left, stride_right) , stream=__lowerCAmelCase ):
# Put everything back in numpy scale
lowercase_ = np.frombuffer(item["""raw"""] , dtype=__lowerCAmelCase )
lowercase_ = (
item["""stride"""][0] // size_of_sample,
item["""stride"""][1] // size_of_sample,
)
lowercase_ = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = False ) -> List[Any]:
'''simple docstring'''
lowercase_ = b""""""
lowercase_ , lowercase_ = 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}''' )
lowercase_ = 0
for raw in iterator:
acc += raw
if stream and len(__lowerCAmelCase ) < chunk_len:
lowercase_ = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__lowerCAmelCase ) >= chunk_len:
# We are flushing the accumulator
lowercase_ = (_stride_left, stride_right)
lowercase_ = {"""raw""": acc[:chunk_len], """stride""": stride}
if stream:
lowercase_ = False
yield item
lowercase_ = stride_left
lowercase_ = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__lowerCAmelCase ) > stride_left:
lowercase_ = {"""raw""": acc, """stride""": (_stride_left, 0)}
if stream:
lowercase_ = False
yield item
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase_ = 2**24 # 16Mo
try:
with subprocess.Popen(__lowerCAmelCase , stdout=subprocess.PIPE , bufsize=__lowerCAmelCase ) as ffmpeg_process:
while True:
lowercase_ = ffmpeg_process.stdout.read(__lowerCAmelCase )
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
| 365
|
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x20000 and cp <= 0x2a6df) #
or (cp >= 0x2a700 and cp <= 0x2b73f) #
or (cp >= 0x2b740 and cp <= 0x2b81f) #
or (cp >= 0x2b820 and cp <= 0x2ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2f800 and cp <= 0x2fa1f) #
): #
return True
return False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
for char in word:
lowercase_ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase_ = set()
for token in tokens:
lowercase_ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowercase_ = list(__lowerCAmelCase )
return word_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
lowercase_ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowercase_ = bert_tokens
lowercase_ , lowercase_ = 0, len(__lowerCAmelCase )
while start < end:
lowercase_ = True
if is_chinese(bert_word[start] ):
lowercase_ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowercase_ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowercase_ = """##""" + bert_word[j]
lowercase_ = start + i
lowercase_ = False
break
if single_word:
start += 1
return bert_word
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
lowercase_ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=5_12 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = []
for id in input_ids:
lowercase_ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowercase_ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowercase_ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.readlines()
lowercase_ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowercase_ = LTP(args.ltp ) # faster in GPU device
lowercase_ = BertTokenizer.from_pretrained(args.bert )
lowercase_ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowercase_ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
UpperCAmelCase : int = parser.parse_args()
main(args)
| 313
| 0
|
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
UpperCAmelCase : Any = logging.getLogger(__name__)
UpperCAmelCase : Optional[int] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase : Dict = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = field(
default=__UpperCAmelCase , metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__UpperCAmelCase )} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
lowercase__ = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"""--config_overrides can't be used in combination with --config_name or --model_name_or_path""")
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowercase__ = field(default=__UpperCAmelCase , metadata={"help": "The input training data file (a text file)."} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "An optional input train ref data file for whole word masking in Chinese."} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
lowercase__ = field(
default=5 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated. Default to the max input length of the model."
)
} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowercase__ = field(
default=0.15 , metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
if self.train_file is not None:
lowercase_ = self.train_file.split(""".""")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
lowercase_ = self.validation_file.split(""".""")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = [json.loads(__lowerCAmelCase ) for line in f.read().splitlines() if (len(__lowerCAmelCase ) > 0 and not line.isspace())]
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = {c: dataset[c] for c in dataset.column_names}
lowercase_ = refs
return Dataset.from_dict(__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ , lowercase_ , lowercase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ , lowercase_ , lowercase_ = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
lowercase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}''' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , __lowerCAmelCase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase_ = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''train[:{data_args.validation_split_percentage}%]''' , )
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''train[{data_args.validation_split_percentage}%:]''' , )
else:
lowercase_ = {}
if data_args.train_file is not None:
lowercase_ = data_args.train_file
if data_args.validation_file is not None:
lowercase_ = data_args.validation_file
lowercase_ = data_args.train_file.split(""".""" )[-1]
if extension == "txt":
lowercase_ = """text"""
lowercase_ = load_dataset(__lowerCAmelCase , data_files=__lowerCAmelCase )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowercase_ = AutoConfig.from_pretrained(model_args.config_name , **__lowerCAmelCase )
elif model_args.model_name_or_path:
lowercase_ = AutoConfig.from_pretrained(model_args.model_name_or_path , **__lowerCAmelCase )
else:
lowercase_ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
lowercase_ = {
"""cache_dir""": model_args.cache_dir,
"""use_fast""": model_args.use_fast_tokenizer,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
lowercase_ = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **__lowerCAmelCase )
elif model_args.model_name_or_path:
lowercase_ = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **__lowerCAmelCase )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )
if model_args.model_name_or_path:
lowercase_ = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
lowercase_ = AutoModelForMaskedLM.from_config(__lowerCAmelCase )
model.resize_token_embeddings(len(__lowerCAmelCase ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
lowercase_ = datasets["""train"""].column_names
else:
lowercase_ = datasets["""validation"""].column_names
lowercase_ = """text""" if """text""" in column_names else column_names[0]
lowercase_ = """max_length""" if data_args.pad_to_max_length else False
def tokenize_function(__lowerCAmelCase ):
# Remove empty lines
lowercase_ = [line for line in examples["""text"""] if len(__lowerCAmelCase ) > 0 and not line.isspace()]
return tokenizer(examples["""text"""] , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=data_args.max_seq_length )
lowercase_ = datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
lowercase_ = add_chinese_references(tokenized_datasets["""train"""] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
lowercase_ = add_chinese_references(
tokenized_datasets["""validation"""] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
lowercase_ = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
lowercase_ = False
# Data collator
# This one will take care of randomly masking the tokens.
lowercase_ = DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
lowercase_ = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None , eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
lowercase_ = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
lowercase_ = model_args.model_name_or_path
else:
lowercase_ = None
lowercase_ = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
trainer.save_model() # Saves the tokenizer too for easy upload
lowercase_ = os.path.join(training_args.output_dir , """train_results.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
logger.info("""***** Train results *****""" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F''' {key} = {value}''' )
writer.write(F'''{key} = {value}\n''' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# Evaluation
lowercase_ = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase_ = trainer.evaluate()
lowercase_ = math.exp(eval_output["""eval_loss"""] )
lowercase_ = perplexity
lowercase_ = os.path.join(training_args.output_dir , """eval_results_mlm_wwm.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in sorted(results.items() ):
logger.info(F''' {key} = {value}''' )
writer.write(F'''{key} = {value}\n''' )
return results
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Tuple:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 366
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
lowercase_ = int(__lowerCAmelCase )
lowercase_ = int(__lowerCAmelCase )
lowercase_ = []
for temp in range(int(__lowerCAmelCase ) ):
series.append(F'''1 / {pow(temp + 1 , int(__lowerCAmelCase ) )}''' if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[str] = int(input("Enter the last number (nth term) of the P-Series"))
UpperCAmelCase : Tuple = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 313
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 367
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
self.assertEqual(x.component(0) , 1)
self.assertEqual(x.component(2) , 3)
lowercase_ = Vector()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(lowerCAmelCase_) , """(0,0,0,0,0,1)""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3, 4])
self.assertEqual(len(lowerCAmelCase_) , 4)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = Vector([1, 2])
lowercase_ = Vector([1, 2, 3, 4, 5])
lowercase_ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
lowercase_ = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3)
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3)
self.assertEqual(z.euclidean_length() , 0)
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x + y).component(0) , 2)
self.assertEqual((x + y).component(1) , 3)
self.assertEqual((x + y).component(2) , 4)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x - y).component(0) , 0)
self.assertEqual((x - y).component(1) , 1)
self.assertEqual((x - y).component(2) , 2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([2, -1, 4]) # for test of dot product
lowercase_ = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0) , """(3.0,6.0,9.0)""")
self.assertEqual((a * b) , 0)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.assertEqual(str(zero_vector(1_0)).count("""0""") , 1_0)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1)) , """(0,1,0)""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 0, 1])
self.assertEqual(str(axpy(2 , lowerCAmelCase_ , lowerCAmelCase_)) , """(3,4,7)""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0, 0, 0, 0])
lowercase_ = x.copy()
self.assertEqual(str(lowerCAmelCase_) , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0])
x.change_component(0 , 0)
x.change_component(1 , 1)
self.assertEqual(str(lowerCAmelCase_) , """(0,1,0)""")
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y] , a.minor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y] , a.cofactor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(-5 , a.determinant())
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3)
lowercase_ = Vector([1, 2, 3])
self.assertEqual("""(14,32,50)""" , str(a * x))
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
a.change_component(0 , 2 , 5)
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(7 , a.component(2 , 1) , 0.01)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5)) , )
if __name__ == "__main__":
unittest.main()
| 313
| 0
|
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BertJapaneseTokenizer
lowercase__ = False
lowercase__ = True
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
super().setUp()
lowercase_ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
lowercase_ = 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]))
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = """こんにちは、世界。 \nこんばんは、世界。"""
lowercase_ = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.get_input_output_texts(lowerCAmelCase_)
lowercase_ = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
lowercase_ = tokenizer.decode(lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_)
return text, ids
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.tokenizer_class(self.vocab_file)
lowercase_ = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""")
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4])
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""")
self.assertIsNotNone(lowerCAmelCase_)
lowercase_ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4])
lowercase_ = os.path.join(self.tmpdirname , """tokenizer.bin""")
with open(lowerCAmelCase_ , """wb""") as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_)
with open(lowerCAmelCase_ , """rb""") as handle:
lowercase_ = pickle.load(lowerCAmelCase_)
lowercase_ = tokenizer_new.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = MecabTokenizer(mecab_dic="""ipadic""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _UpperCAmelCase ( self : int):
"""simple docstring"""
try:
lowercase_ = MecabTokenizer(mecab_dic="""unidic_lite""")
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
try:
lowercase_ = MecabTokenizer(mecab_dic="""unidic""")
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = MecabTokenizer(do_lower_case=lowerCAmelCase_ , mecab_dic="""ipadic""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
try:
lowercase_ = MecabTokenizer(
do_lower_case=lowerCAmelCase_ , normalize_text=lowerCAmelCase_ , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""")
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = MecabTokenizer(normalize_text=lowerCAmelCase_ , mecab_dic="""ipadic""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""")
self.assertIsNotNone(lowerCAmelCase_)
lowercase_ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4])
lowercase_ = os.path.join(self.tmpdirname , """tokenizer.bin""")
with open(lowerCAmelCase_ , """wb""") as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_)
with open(lowerCAmelCase_ , """rb""") as handle:
lowercase_ = pickle.load(lowerCAmelCase_)
lowercase_ = tokenizer_new.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@require_sudachi
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = SudachiTokenizer(sudachi_dict_type="""core""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""")
self.assertListEqual(tokenizer.tokenize("""外国人参政権""") , ["""外国""", """人""", """参政""", """権"""])
@require_sudachi
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""")
self.assertListEqual(tokenizer.tokenize("""外国人参政権""") , ["""外国人""", """参政権"""])
@require_sudachi
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""")
self.assertListEqual(tokenizer.tokenize("""外国人参政権""") , ["""外国人参政権"""])
@require_sudachi
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = SudachiTokenizer(do_lower_case=lowerCAmelCase_ , sudachi_dict_type="""core""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = SudachiTokenizer(normalize_text=lowerCAmelCase_ , sudachi_dict_type="""core""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = SudachiTokenizer(trim_whitespace=lowerCAmelCase_ , sudachi_dict_type="""core""")
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""")
self.assertIsNotNone(lowerCAmelCase_)
lowercase_ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4])
lowercase_ = os.path.join(self.tmpdirname , """tokenizer.bin""")
with open(lowerCAmelCase_ , """wb""") as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_)
with open(lowerCAmelCase_ , """rb""") as handle:
lowercase_ = pickle.load(lowerCAmelCase_)
lowercase_ = tokenizer_new.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@require_jumanpp
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = JumanppTokenizer(do_lower_case=lowerCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = JumanppTokenizer(normalize_text=lowerCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = JumanppTokenizer(trim_whitespace=lowerCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""") , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
lowercase_ = {}
for i, token in enumerate(lowerCAmelCase_):
lowercase_ = i
lowercase_ = WordpieceTokenizer(vocab=lowerCAmelCase_ , unk_token="""[UNK]""")
self.assertListEqual(tokenizer.tokenize("""""") , [])
self.assertListEqual(tokenizer.tokenize("""こんにちは""") , ["""こんにちは"""])
self.assertListEqual(tokenizer.tokenize("""こんばんは""") , ["""こん""", """##ばんは"""])
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""") , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""])
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""")
lowercase_ = tokenizer.subword_tokenizer
lowercase_ = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""")
self.assertListEqual(lowerCAmelCase_ , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""])
lowercase_ = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""")
self.assertListEqual(lowerCAmelCase_ , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""])
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""")
lowercase_ = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase_)
lowercase_ = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase_)
lowercase_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
lowercase_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BertJapaneseTokenizer
lowercase__ = False
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
super().setUp()
lowercase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowercase_ = 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]))
def _UpperCAmelCase ( self : Tuple , **lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowerCAmelCase_)
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = """こんにちは、世界。 \nこんばんは、世界。"""
lowercase_ = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def _UpperCAmelCase ( self : str):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
pass # TODO add if relevant
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""")
lowercase_ = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""")
self.assertListEqual(
lowerCAmelCase_ , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [3, 4, 5, 6, 7, 1_1, 9, 1_0, 1_2, 3, 4, 8, 4, 7, 1_1, 9, 1_0, 1_2])
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowercase_ = {}
for i, token in enumerate(lowerCAmelCase_):
lowercase_ = i
lowercase_ = CharacterTokenizer(vocab=lowerCAmelCase_ , unk_token="""[UNK]""")
self.assertListEqual(tokenizer.tokenize("""""") , [])
self.assertListEqual(tokenizer.tokenize("""こんにちは""") , ["""こ""", """ん""", """に""", """ち""", """は"""])
self.assertListEqual(tokenizer.tokenize("""こんにちほ""") , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""])
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""")
lowercase_ = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase_)
lowercase_ = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase_)
lowercase_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
lowercase_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """cl-tohoku/bert-base-japanese"""
lowercase_ = AutoTokenizer.from_pretrained(lowerCAmelCase_)
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""") as cm:
BertTokenizer.from_pretrained(lowerCAmelCase_)
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from."""))
lowercase_ = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""") as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase_)
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from."""))
| 368
|
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < end:
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ , lowercase_ = _in_place_partition(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
count += _in_place_quick_sort(__lowerCAmelCase , __lowerCAmelCase , p - 1 )
count += _in_place_quick_sort(__lowerCAmelCase , p + 1 , __lowerCAmelCase )
return count
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ = start - 1
for index in range(__lowerCAmelCase , __lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowercase_ = new_pivot_index + 1
lowercase_ = a[new_pivot_index]
lowercase_ = a[index]
lowercase_ = temp
lowercase_ = a[new_pivot_index + 1]
lowercase_ = a[end]
lowercase_ = temp
return new_pivot_index + 1, count
UpperCAmelCase : Union[str, Any] = TemporaryFile()
UpperCAmelCase : Optional[int] = 100 # 1000 elements are to be sorted
UpperCAmelCase , UpperCAmelCase : List[str] = 0, 1 # mean and standard deviation
UpperCAmelCase : Optional[Any] = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase : List[str] = np.load(outfile)
UpperCAmelCase : List[Any] = len(M) - 1
UpperCAmelCase : Optional[int] = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ = """"""
else:
lowercase_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''' )
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ = in_proj_weight[
: config.hidden_size, :
]
lowercase_ = in_proj_bias[: config.hidden_size]
lowercase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ = in_proj_bias[-config.hidden_size :]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = dct.pop(__lowerCAmelCase )
lowercase_ = val
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = ViTMSNConfig()
lowercase_ = 10_00
lowercase_ = """datasets/huggingface/label-files"""
lowercase_ = """imagenet-1k-id2label.json"""
lowercase_ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase ) , """r""" ) )
lowercase_ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowercase_ = idalabel
lowercase_ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase_ = 3_84
lowercase_ = 15_36
lowercase_ = 6
elif "l16" in checkpoint_url:
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
elif "b4" in checkpoint_url:
lowercase_ = 4
elif "l7" in checkpoint_url:
lowercase_ = 7
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
lowercase_ = ViTMSNModel(__lowerCAmelCase )
lowercase_ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""target_encoder"""]
lowercase_ = ViTImageProcessor(size=config.image_size )
remove_projection_head(__lowerCAmelCase )
lowercase_ = create_rename_keys(__lowerCAmelCase , base_model=__lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase , base_model=__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
lowercase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
lowercase_ = ViTImageProcessor(
size=config.image_size , image_mean=__lowerCAmelCase , image_std=__lowerCAmelCase )
lowercase_ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowercase_ = model(**__lowerCAmelCase )
lowercase_ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowercase_ = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
lowercase_ = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
lowercase_ = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
lowercase_ = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
lowercase_ = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __lowerCAmelCase , atol=1E-4 )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313
| 0
|
"""simple docstring"""
import argparse
import os
import re
UpperCAmelCase : Union[str, Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
UpperCAmelCase : Any = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase : int = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase : List[str] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase : Union[str, Any] = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase : List[str] = re.compile(r"\[([^\]]+)\]")
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = _re_indent.search(__lowerCAmelCase )
return "" if search is None else search.groups()[0]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase="" , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> List[Any]:
'''simple docstring'''
lowercase_ = 0
lowercase_ = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(__lowerCAmelCase ):
index += 1
lowercase_ = ["""\n""".join(lines[:index] )]
else:
lowercase_ = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowercase_ = [lines[index]]
index += 1
while index < len(__lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(__lowerCAmelCase ) )
if index < len(__lowerCAmelCase ) - 1:
lowercase_ = [lines[index + 1]]
index += 1
else:
lowercase_ = []
else:
blocks.append("""\n""".join(__lowerCAmelCase ) )
lowercase_ = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__lowerCAmelCase ) > 0:
blocks.append("""\n""".join(__lowerCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__lowerCAmelCase ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def _inner(__lowerCAmelCase ):
return key(__lowerCAmelCase ).lower().replace("""_""" , """""" )
return _inner
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=None ) -> Optional[Any]:
'''simple docstring'''
def noop(__lowerCAmelCase ):
return x
if key is None:
lowercase_ = noop
# Constants are all uppercase, they go first.
lowercase_ = [obj for obj in objects if key(__lowerCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowercase_ = [obj for obj in objects if key(__lowerCAmelCase )[0].isupper() and not key(__lowerCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
lowercase_ = [obj for obj in objects if not key(__lowerCAmelCase )[0].isupper()]
lowercase_ = ignore_underscore(__lowerCAmelCase )
return sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def _replace(__lowerCAmelCase ):
lowercase_ = match.groups()[0]
if "," not in imports:
return F'''[{imports}]'''
lowercase_ = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowercase_ = keys[:-1]
return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] ) + "]"
lowercase_ = import_statement.split("""\n""" )
if len(__lowerCAmelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowercase_ = 2 if lines[1].strip() == """[""" else 1
lowercase_ = [(i, _re_strip_line.search(__lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowercase_ = sort_objects(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] )
lowercase_ = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__lowerCAmelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowercase_ = _re_bracket_content.sub(_replace , lines[1] )
else:
lowercase_ = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowercase_ = keys[:-1]
lowercase_ = get_indent(lines[1] ) + """, """.join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] )
return "\n".join(__lowerCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
lowercase_ = _re_bracket_content.sub(_replace , __lowerCAmelCase )
return import_statement
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=True ) -> Dict:
'''simple docstring'''
with open(__lowerCAmelCase , encoding="""utf-8""" ) as f:
lowercase_ = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowercase_ = split_code_in_indented_blocks(
__lowerCAmelCase , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__lowerCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowercase_ = main_blocks[block_idx]
lowercase_ = block.split("""\n""" )
# Get to the start of the imports.
lowercase_ = 0
while line_idx < len(__lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowercase_ = len(__lowerCAmelCase )
else:
line_idx += 1
if line_idx >= len(__lowerCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
lowercase_ = """\n""".join(block_lines[line_idx:-1] )
lowercase_ = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowercase_ = split_code_in_indented_blocks(__lowerCAmelCase , indent_level=__lowerCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
lowercase_ = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowercase_ = [(pattern.search(__lowerCAmelCase ).groups()[0] if pattern.search(__lowerCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowercase_ = [(i, key) for i, key in enumerate(__lowerCAmelCase ) if key is not None]
lowercase_ = [x[0] for x in sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowercase_ = 0
lowercase_ = []
for i in range(len(__lowerCAmelCase ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
lowercase_ = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(__lowerCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
lowercase_ = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(__lowerCAmelCase ):
if check_only:
return True
else:
print(F'''Overwriting {file}.''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write("""\n""".join(__lowerCAmelCase ) )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase=True ) -> int:
'''simple docstring'''
lowercase_ = []
for root, _, files in os.walk(__lowerCAmelCase ):
if "__init__.py" in files:
lowercase_ = sort_imports(os.path.join(__lowerCAmelCase , """__init__.py""" ) , check_only=__lowerCAmelCase )
if result:
lowercase_ = [os.path.join(__lowerCAmelCase , """__init__.py""" )]
if len(__lowerCAmelCase ) > 0:
raise ValueError(F'''Would overwrite {len(__lowerCAmelCase )} files, run `make style`.''' )
if __name__ == "__main__":
UpperCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
UpperCAmelCase : Optional[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 370
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "perceiver"
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str]=2_5_6 , lowerCAmelCase_ : Dict=1_2_8_0 , lowerCAmelCase_ : List[Any]=7_6_8 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[Any]=2_6 , lowerCAmelCase_ : Optional[Any]=8 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Optional[Any]="kv" , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Union[str, Any]=0.02 , lowerCAmelCase_ : List[Any]=1E-12 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Union[str, Any]=2_6_2 , lowerCAmelCase_ : Union[str, Any]=2_0_4_8 , lowerCAmelCase_ : Any=5_6 , lowerCAmelCase_ : int=[3_6_8, 4_9_6] , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : Dict=1_9_2_0 , lowerCAmelCase_ : Optional[Any]=1_6 , lowerCAmelCase_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **lowerCAmelCase_ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_)
lowercase_ = num_latents
lowercase_ = d_latents
lowercase_ = d_model
lowercase_ = num_blocks
lowercase_ = num_self_attends_per_block
lowercase_ = num_self_attention_heads
lowercase_ = num_cross_attention_heads
lowercase_ = qk_channels
lowercase_ = v_channels
lowercase_ = cross_attention_shape_for_attention
lowercase_ = self_attention_widening_factor
lowercase_ = cross_attention_widening_factor
lowercase_ = hidden_act
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = use_query_residual
# masked language modeling attributes
lowercase_ = vocab_size
lowercase_ = max_position_embeddings
# image classification attributes
lowercase_ = image_size
# flow attributes
lowercase_ = train_size
# multimodal autoencoding attributes
lowercase_ = num_frames
lowercase_ = audio_samples_per_frame
lowercase_ = samples_per_patch
lowercase_ = output_shape
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
if self.task == "multiple-choice":
lowercase_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""inputs""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
@property
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return 1E-4
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[TensorType] = None , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 4_0 , lowerCAmelCase_ : int = 4_0 , ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , 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
lowercase_ = preprocessor.num_special_tokens_to_add(lowerCAmelCase_)
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase_)
# Generate dummy inputs according to compute batch and sequence
lowercase_ = [""" """.join(["""a"""]) * seq_length] * batch_size
lowercase_ = dict(preprocessor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""input_ids""")
return inputs
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase_ = self._generate_dummy_images(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = dict(preprocessor(images=lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""pixel_values""")
return inputs
else:
raise ValueError(
"""Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""")
| 313
| 0
|
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
__lowerCamelCase : str = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
if args.student_type == "roberta":
lowercase_ = False
elif args.student_type == "gpt2":
lowercase_ = False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
if args.student_type == "roberta":
lowercase_ = False
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = argparse.ArgumentParser(description="""Training""" )
parser.add_argument("""--force""" , action="""store_true""" , help="""Overwrite dump_path if it already exists.""" )
parser.add_argument(
"""--dump_path""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The output directory (log, checkpoints, parameters, etc.)""" )
parser.add_argument(
"""--data_file""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" , )
parser.add_argument(
"""--student_type""" , type=__lowerCAmelCase , choices=["""distilbert""", """roberta""", """gpt2"""] , required=__lowerCAmelCase , help="""The student type (DistilBERT, RoBERTa).""" , )
parser.add_argument("""--student_config""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""Path to the student configuration.""" )
parser.add_argument(
"""--student_pretrained_weights""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""Load student initialization checkpoint.""" )
parser.add_argument(
"""--teacher_type""" , choices=["""bert""", """roberta""", """gpt2"""] , required=__lowerCAmelCase , help="""Teacher type (BERT, RoBERTa).""" )
parser.add_argument("""--teacher_name""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The teacher model.""" )
parser.add_argument("""--temperature""" , default=2.0 , type=__lowerCAmelCase , help="""Temperature for the softmax temperature.""" )
parser.add_argument(
"""--alpha_ce""" , default=0.5 , type=__lowerCAmelCase , help="""Linear weight for the distillation loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_mlm""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" , )
parser.add_argument("""--alpha_clm""" , default=0.5 , type=__lowerCAmelCase , help="""Linear weight for the CLM loss. Must be >=0.""" )
parser.add_argument("""--alpha_mse""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight of the MSE loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_cos""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight of the cosine embedding loss. Must be >=0.""" )
parser.add_argument(
"""--mlm""" , action="""store_true""" , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.""" )
parser.add_argument(
"""--mlm_mask_prop""" , default=0.15 , type=__lowerCAmelCase , help="""Proportion of tokens for which we need to make a prediction.""" , )
parser.add_argument("""--word_mask""" , default=0.8 , type=__lowerCAmelCase , help="""Proportion of tokens to mask out.""" )
parser.add_argument("""--word_keep""" , default=0.1 , type=__lowerCAmelCase , help="""Proportion of tokens to keep.""" )
parser.add_argument("""--word_rand""" , default=0.1 , type=__lowerCAmelCase , help="""Proportion of tokens to randomly replace.""" )
parser.add_argument(
"""--mlm_smoothing""" , default=0.7 , type=__lowerCAmelCase , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" , )
parser.add_argument("""--token_counts""" , type=__lowerCAmelCase , help="""The token counts in the data_file for MLM.""" )
parser.add_argument(
"""--restrict_ce_to_mask""" , action="""store_true""" , help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" , )
parser.add_argument(
"""--freeze_pos_embs""" , action="""store_true""" , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" , )
parser.add_argument(
"""--freeze_token_type_embds""" , action="""store_true""" , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" , )
parser.add_argument("""--n_epoch""" , type=__lowerCAmelCase , default=3 , help="""Number of pass on the whole dataset.""" )
parser.add_argument("""--batch_size""" , type=__lowerCAmelCase , default=5 , help="""Batch size (for each process).""" )
parser.add_argument(
"""--group_by_size""" , action="""store_false""" , help="""If true, group sequences that have similar length into the same batch. Default is true.""" , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=__lowerCAmelCase , default=50 , help="""Gradient accumulation for larger training batches.""" , )
parser.add_argument("""--warmup_prop""" , default=0.05 , type=__lowerCAmelCase , help="""Linear warmup proportion.""" )
parser.add_argument("""--weight_decay""" , default=0.0 , type=__lowerCAmelCase , help="""Weight decay if we apply some.""" )
parser.add_argument("""--learning_rate""" , default=5E-4 , type=__lowerCAmelCase , help="""The initial learning rate for Adam.""" )
parser.add_argument("""--adam_epsilon""" , default=1E-6 , type=__lowerCAmelCase , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , default=5.0 , type=__lowerCAmelCase , help="""Max gradient norm.""" )
parser.add_argument("""--initializer_range""" , default=0.02 , type=__lowerCAmelCase , help="""Random initialization range.""" )
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=__lowerCAmelCase , default="""O1""" , 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_gpu""" , type=__lowerCAmelCase , default=1 , help="""Number of GPUs in the node.""" )
parser.add_argument("""--local_rank""" , type=__lowerCAmelCase , default=-1 , help="""Distributed training - Local rank""" )
parser.add_argument("""--seed""" , type=__lowerCAmelCase , default=56 , help="""Random seed""" )
parser.add_argument("""--log_interval""" , type=__lowerCAmelCase , default=5_00 , help="""Tensorboard logging interval.""" )
parser.add_argument("""--checkpoint_interval""" , type=__lowerCAmelCase , default=40_00 , help="""Checkpoint interval.""" )
lowercase_ = parser.parse_args()
sanity_checks(__lowerCAmelCase )
# ARGS #
init_gpu_params(__lowerCAmelCase )
set_seed(__lowerCAmelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
""" itUse `--force` if you want to overwrite it""" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , """parameters.json""" ) , """w""" ) as f:
json.dump(vars(__lowerCAmelCase ) , __lowerCAmelCase , indent=4 )
git_log(args.dump_path )
lowercase_ , lowercase_ , lowercase_ = MODEL_CLASSES[args.student_type]
lowercase_ , lowercase_ , lowercase_ = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowercase_ = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowercase_ = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowercase_ = tokenizer.all_special_tokens.index(__lowerCAmelCase )
lowercase_ = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
lowercase_ = special_tok_ids
lowercase_ = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , """rb""" ) as fp:
lowercase_ = pickle.load(__lowerCAmelCase )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , """rb""" ) as fp:
lowercase_ = pickle.load(__lowerCAmelCase )
lowercase_ = np.maximum(__lowerCAmelCase , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowercase_ = 0.0 # do not predict special tokens
lowercase_ = torch.from_numpy(__lowerCAmelCase )
else:
lowercase_ = None
lowercase_ = LmSeqsDataset(params=__lowerCAmelCase , data=__lowerCAmelCase )
logger.info("""Data loader created.""" )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
lowercase_ = student_config_class.from_pretrained(args.student_config )
lowercase_ = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowercase_ = student_model_class.from_pretrained(args.student_pretrained_weights , config=__lowerCAmelCase )
else:
lowercase_ = student_model_class(__lowerCAmelCase )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info("""Student loaded.""" )
# TEACHER #
lowercase_ = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__lowerCAmelCase )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__lowerCAmelCase , __lowerCAmelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__lowerCAmelCase , __lowerCAmelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowercase_ = Distiller(
params=__lowerCAmelCase , dataset=__lowerCAmelCase , token_probs=__lowerCAmelCase , student=__lowerCAmelCase , teacher=__lowerCAmelCase )
distiller.train()
logger.info("""Let's go get some drinks.""" )
if __name__ == "__main__":
main()
| 371
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BarthezTokenizer
lowercase__ = BarthezTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
super().setUp()
lowercase_ = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
tokenizer.save_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase_)
lowercase_ = tokenizer
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_) , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<s>""")
self.assertEqual(vocab_keys[1] , """<pad>""")
self.assertEqual(vocab_keys[-1] , """<mask>""")
self.assertEqual(len(lowerCAmelCase_) , 1_0_1_1_2_2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2)
@require_torch
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowercase_ = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
lowercase_ = self.tokenizer(
lowerCAmelCase_ , max_length=len(lowerCAmelCase_) , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_)
self.assertEqual((2, 6) , batch.input_ids.shape)
self.assertEqual((2, 6) , batch.attention_mask.shape)
lowercase_ = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
lowercase_ = rust_tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase_ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""moussaKam/mbarthez""" , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" , sequences=lowerCAmelCase_ , )
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any=1_3 , lowerCAmelCase_ : Dict=7 , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : List[Any]=9_9 , lowerCAmelCase_ : str=[1, 1, 2] , lowerCAmelCase_ : List[Any]=1 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : List[Any]=4 , lowerCAmelCase_ : Optional[int]=8 , lowerCAmelCase_ : Union[str, Any]=3_7 , lowerCAmelCase_ : List[Any]="gelu_new" , lowerCAmelCase_ : List[Any]=0.1 , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : List[Any]=0.0 , lowerCAmelCase_ : List[Any]=5_1_2 , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : int=0.02 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : Tuple=4 , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Union[str, Any]=False , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = block_sizes
lowercase_ = num_decoder_layers
lowercase_ = d_model
lowercase_ = n_head
lowercase_ = d_head
lowercase_ = d_inner
lowercase_ = hidden_act
lowercase_ = hidden_dropout
lowercase_ = attention_dropout
lowercase_ = activation_dropout
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = 2
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
lowercase_ = initializer_std
# Used in the tests to check the size of the first attention layer
lowercase_ = n_head
# Used in the tests to check the size of the first hidden state
lowercase_ = self.d_model
# Used in the tests to check the number of output hidden states/attentions
lowercase_ = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
lowercase_ = self.num_hidden_layers + 2
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str , ):
"""simple docstring"""
lowercase_ = TFFunnelModel(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
lowercase_ = [input_ids, input_mask]
lowercase_ = model(lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
lowercase_ = False
lowercase_ = TFFunnelModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
lowercase_ = False
lowercase_ = TFFunnelModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , ):
"""simple docstring"""
lowercase_ = TFFunnelBaseModel(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
lowercase_ = [input_ids, input_mask]
lowercase_ = model(lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model))
lowercase_ = False
lowercase_ = TFFunnelBaseModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model))
lowercase_ = False
lowercase_ = TFFunnelBaseModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model))
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , ):
"""simple docstring"""
lowercase_ = TFFunnelForPreTraining(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length))
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
lowercase_ = TFFunnelForMaskedLM(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Any , ):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = TFFunnelForSequenceClassification(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = TFFunnelForMultipleChoice(config=lowerCAmelCase_)
lowercase_ = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1) , (1, self.num_choices, 1))
lowercase_ = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1) , (1, self.num_choices, 1))
lowercase_ = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1) , (1, self.num_choices, 1))
lowercase_ = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple , ):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = TFFunnelForTokenClassification(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : str , ):
"""simple docstring"""
lowercase_ = TFFunnelForQuestionAnswering(config=lowerCAmelCase_)
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase__ = (
{
"feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
"fill-mask": TFFunnelForMaskedLM,
"question-answering": TFFunnelForQuestionAnswering,
"text-classification": TFFunnelForSequenceClassification,
"token-classification": TFFunnelForTokenClassification,
"zero-shot": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = TFFunnelModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_)
@require_tf
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = TFFunnelModelTester(self , base=lowerCAmelCase_)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_)
| 350
|
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Optional[Any] = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class SCREAMING_SNAKE_CASE__ :
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = "gelu"
def __init__( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Any=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : str=9_9 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Dict=5 , lowerCAmelCase_ : Union[str, Any]=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Optional[int]=2_0 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
lowercase_ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
lowercase_ = np.concatenate([input_ids, eos_tensor] , axis=1)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase_ = prepare_pegasus_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.not_equal(__lowerCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowercase_ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxPegasusModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=None , **lowerCAmelCase_ : Optional[int]):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=lowerCAmelCase_)
lowercase_ = np.ones((1, 1))
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
lowercase_ = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")
lowercase_ = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
lowercase_ = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""np""" , truncation=lowerCAmelCase_ , max_length=5_1_2 , padding=lowerCAmelCase_)
lowercase_ = model.generate(**lowerCAmelCase_ , num_beams=2).sequences
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_)
assert tgt_text == decoded
| 313
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "lxmert"
lowercase__ = {}
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any]=3_0_5_2_2 , lowerCAmelCase_ : Optional[int]=7_6_8 , lowerCAmelCase_ : str=1_2 , lowerCAmelCase_ : str=9_5_0_0 , lowerCAmelCase_ : Optional[Any]=1_6_0_0 , lowerCAmelCase_ : List[str]=4_0_0 , lowerCAmelCase_ : Dict=3_0_7_2 , lowerCAmelCase_ : List[Any]="gelu" , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : Any=5_1_2 , lowerCAmelCase_ : int=2 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : Optional[Any]=1E-12 , lowerCAmelCase_ : str=9 , lowerCAmelCase_ : int=5 , lowerCAmelCase_ : Optional[int]=5 , lowerCAmelCase_ : Optional[Any]=2_0_4_8 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : str=6.67 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : str=True , **lowerCAmelCase_ : Any , ):
"""simple docstring"""
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_attention_heads
lowercase_ = hidden_act
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = num_qa_labels
lowercase_ = num_object_labels
lowercase_ = num_attr_labels
lowercase_ = l_layers
lowercase_ = x_layers
lowercase_ = r_layers
lowercase_ = visual_feat_dim
lowercase_ = visual_pos_dim
lowercase_ = visual_loss_normalizer
lowercase_ = task_matched
lowercase_ = task_mask_lm
lowercase_ = task_obj_predict
lowercase_ = task_qa
lowercase_ = visual_obj_loss
lowercase_ = visual_attr_loss
lowercase_ = visual_feat_loss
lowercase_ = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**lowerCAmelCase_)
| 351
|
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = None
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = True
lowercase__ = None
lowercase__ = 1
lowercase__ = None
lowercase__ = False
lowercase__ = None
lowercase__ = None
def _UpperCAmelCase ( self : int):
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(lowerCAmelCase_) for k, v in self.__dict__.items()})
| 313
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : Dict=1_0 , lowerCAmelCase_ : Any=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : int=[1, 1, 2, 1] , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[Any]="relu" , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : int=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = embeddings_size
lowercase_ = hidden_sizes
lowercase_ = depths
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = hidden_act
lowercase_ = num_labels
lowercase_ = scope
lowercase_ = len(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = self.get_config()
return config, pixel_values
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = FlaxRegNetModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = FlaxRegNetForImageClassification(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = FlaxRegNetModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_)
@unittest.skip(reason="""RegNet does not use inputs_embeds""")
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Any):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_) , expected_num_stages + 1)
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def model_jitted(lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Union[str, Any]):
return model(pixel_values=lowerCAmelCase_ , **lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = model_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = model_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _SCREAMING_SNAKE_CASE () -> int:
'''simple docstring'''
lowercase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""") if is_vision_available() else None
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""")
lowercase_ = self.default_image_processor
lowercase_ = prepare_img()
lowercase_ = image_processor(images=lowerCAmelCase_ , return_tensors="""np""")
lowercase_ = model(**lowerCAmelCase_)
# verify the logits
lowercase_ = (1, 1_0_0_0)
self.assertEqual(outputs.logits.shape , lowerCAmelCase_)
lowercase_ = jnp.array([-0.4_180, -1.5_051, -3.4_836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1E-4))
| 352
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : List[Any] = {
"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",
},
}
UpperCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 1_6384,
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = LEDTokenizer
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]="replace" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : List[Any]="</s>" , lowerCAmelCase_ : Optional[Any]="<s>" , lowerCAmelCase_ : Union[str, Any]="<unk>" , lowerCAmelCase_ : List[str]="<pad>" , lowerCAmelCase_ : Dict="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop("""type"""))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**lowerCAmelCase_)
lowercase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ = """post_processor"""
lowercase_ = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
if tokenizer_component_instance:
lowercase_ = 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:
lowercase_ = tuple(state["""sep"""])
if "cls" in state:
lowercase_ = tuple(state["""cls"""])
lowercase_ = False
if state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = add_prefix_space
lowercase_ = True
if state.get("""trim_offsets""" , lowerCAmelCase_) != trim_offsets:
lowercase_ = trim_offsets
lowercase_ = True
if changes_to_apply:
lowercase_ = getattr(lowerCAmelCase_ , state.pop("""type"""))
lowercase_ = component_class(**lowerCAmelCase_)
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""")
return None
return str(self._mask_token)
@mask_token.setter
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else value
lowercase_ = value
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
lowercase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_)
return tuple(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = [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 _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
lowercase_ = [self.sep_token_id]
lowercase_ = [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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = super()._pad(
encoded_inputs=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding_strategy=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ = len(encoded_inputs["""global_attention_mask"""]) != len(lowerCAmelCase_)
if needs_to_be_padded:
lowercase_ = len(lowerCAmelCase_) - 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`
lowercase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))
return encoded_inputs
| 313
| 0
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : List[Any] = {
"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",
},
}
UpperCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 1_6384,
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = LEDTokenizer
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]="replace" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : List[Any]="</s>" , lowerCAmelCase_ : Optional[Any]="<s>" , lowerCAmelCase_ : Union[str, Any]="<unk>" , lowerCAmelCase_ : List[str]="<pad>" , lowerCAmelCase_ : Dict="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop("""type"""))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**lowerCAmelCase_)
lowercase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ = """post_processor"""
lowercase_ = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
if tokenizer_component_instance:
lowercase_ = 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:
lowercase_ = tuple(state["""sep"""])
if "cls" in state:
lowercase_ = tuple(state["""cls"""])
lowercase_ = False
if state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = add_prefix_space
lowercase_ = True
if state.get("""trim_offsets""" , lowerCAmelCase_) != trim_offsets:
lowercase_ = trim_offsets
lowercase_ = True
if changes_to_apply:
lowercase_ = getattr(lowerCAmelCase_ , state.pop("""type"""))
lowercase_ = component_class(**lowerCAmelCase_)
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""")
return None
return str(self._mask_token)
@mask_token.setter
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else value
lowercase_ = value
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
lowercase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_)
return tuple(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = [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 _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
lowercase_ = [self.sep_token_id]
lowercase_ = [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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = super()._pad(
encoded_inputs=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding_strategy=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ = len(encoded_inputs["""global_attention_mask"""]) != len(lowerCAmelCase_)
if needs_to_be_padded:
lowercase_ = len(lowerCAmelCase_) - 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`
lowercase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))
return encoded_inputs
| 353
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 313
| 0
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
# General docstring
UpperCAmelCase : Dict = "MobileNetV1Config"
# Base docstring
UpperCAmelCase : int = "google/mobilenet_v1_1.0_224"
UpperCAmelCase : Any = [1, 1024, 7, 7]
# Image classification docstring
UpperCAmelCase : Any = "google/mobilenet_v1_1.0_224"
UpperCAmelCase : Tuple = "tabby, tabby cat"
UpperCAmelCase : str = [
"google/mobilenet_v1_1.0_224",
"google/mobilenet_v1_0.75_192",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase_ = {}
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = model.mobilenet_va
else:
lowercase_ = model
lowercase_ = """MobilenetV1/Conv2d_0/"""
lowercase_ = backbone.conv_stem.convolution.weight
lowercase_ = backbone.conv_stem.normalization.bias
lowercase_ = backbone.conv_stem.normalization.weight
lowercase_ = backbone.conv_stem.normalization.running_mean
lowercase_ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
lowercase_ = i + 1
lowercase_ = i * 2
lowercase_ = backbone.layer[pt_index]
lowercase_ = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ = pointer.convolution.weight
lowercase_ = pointer.normalization.bias
lowercase_ = pointer.normalization.weight
lowercase_ = pointer.normalization.running_mean
lowercase_ = pointer.normalization.running_var
lowercase_ = backbone.layer[pt_index + 1]
lowercase_ = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ = pointer.convolution.weight
lowercase_ = pointer.normalization.bias
lowercase_ = pointer.normalization.weight
lowercase_ = pointer.normalization.running_mean
lowercase_ = pointer.normalization.running_var
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = """MobilenetV1/Logits/Conv2d_1c_1x1/"""
lowercase_ = model.classifier.weight
lowercase_ = model.classifier.bias
return tf_to_pt_map
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"""Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
"""https://www.tensorflow.org/install/ for installation instructions.""" )
raise
# Load weights from TF model
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
# Build TF to PyTorch weights loading map
lowercase_ = _build_tf_to_pytorch_map(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ = tf_weights[name]
if "depthwise_weights" in name:
logger.info("""Transposing depthwise""" )
lowercase_ = np.transpose(__lowerCAmelCase , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("""Transposing""" )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ = array.squeeze().transpose()
else:
lowercase_ = np.transpose(__lowerCAmelCase , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ = torch.from_numpy(__lowerCAmelCase )
tf_weights.pop(__lowerCAmelCase , __lowerCAmelCase )
tf_weights.pop(name + """/RMSProp""" , __lowerCAmelCase )
tf_weights.pop(name + """/RMSProp_1""" , __lowerCAmelCase )
tf_weights.pop(name + """/ExponentialMovingAverage""" , __lowerCAmelCase )
logger.info(F'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}''' )
return model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> torch.Tensor:
'''simple docstring'''
lowercase_ , lowercase_ = features.shape[-2:]
lowercase_ , lowercase_ = conv_layer.stride
lowercase_ , lowercase_ = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ = max(kernel_height - stride_height , 0 )
else:
lowercase_ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ = max(kernel_width - stride_width , 0 )
else:
lowercase_ = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ = pad_along_width // 2
lowercase_ = pad_along_width - pad_left
lowercase_ = pad_along_height // 2
lowercase_ = pad_along_height - pad_top
lowercase_ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__lowerCAmelCase , __lowerCAmelCase , """constant""" , 0.0 )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Union[str, Any] , lowerCAmelCase_ : MobileNetVaConfig , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int] = 1 , lowerCAmelCase_ : Optional[int] = 1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[bool] = True , lowerCAmelCase_ : Optional[bool or str] = True , ):
"""simple docstring"""
super().__init__()
lowercase_ = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''')
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''')
lowercase_ = 0 if config.tf_padding else int((kernel_size - 1) / 2)
lowercase_ = nn.Convad(
in_channels=lowerCAmelCase_ , out_channels=lowerCAmelCase_ , kernel_size=lowerCAmelCase_ , stride=lowerCAmelCase_ , padding=lowerCAmelCase_ , groups=lowerCAmelCase_ , bias=lowerCAmelCase_ , padding_mode="""zeros""" , )
if use_normalization:
lowercase_ = nn.BatchNormad(
num_features=lowerCAmelCase_ , eps=config.layer_norm_eps , momentum=0.9_997 , affine=lowerCAmelCase_ , track_running_stats=lowerCAmelCase_ , )
else:
lowercase_ = None
if use_activation:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
lowercase_ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , lowerCAmelCase_):
lowercase_ = ACTaFN[config.hidden_act]
else:
lowercase_ = config.hidden_act
else:
lowercase_ = None
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : torch.Tensor):
"""simple docstring"""
if self.config.tf_padding:
lowercase_ = apply_tf_padding(lowerCAmelCase_ , self.convolution)
lowercase_ = self.convolution(lowerCAmelCase_)
if self.normalization is not None:
lowercase_ = self.normalization(lowerCAmelCase_)
if self.activation is not None:
lowercase_ = self.activation(lowerCAmelCase_)
return features
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = MobileNetVaConfig
lowercase__ = load_tf_weights_in_mobilenet_va
lowercase__ = "mobilenet_v1"
lowercase__ = "pixel_values"
lowercase__ = False
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : Union[nn.Linear, nn.Convad]):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , (nn.Linear, nn.Convad)):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(lowerCAmelCase_ , nn.BatchNormad):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
UpperCAmelCase : str = r"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
UpperCAmelCase : List[str] = r"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : List[Any] , lowerCAmelCase_ : MobileNetVaConfig , lowerCAmelCase_ : bool = True):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config
lowercase_ = 3_2
lowercase_ = max(int(depth * config.depth_multiplier) , config.min_depth)
lowercase_ = MobileNetVaConvLayer(
lowerCAmelCase_ , in_channels=config.num_channels , out_channels=lowerCAmelCase_ , kernel_size=3 , stride=2 , )
lowercase_ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ = nn.ModuleList()
for i in range(1_3):
lowercase_ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ = max(int(depth * config.depth_multiplier) , config.min_depth)
self.layer.append(
MobileNetVaConvLayer(
lowerCAmelCase_ , in_channels=lowerCAmelCase_ , out_channels=lowerCAmelCase_ , kernel_size=3 , stride=strides[i] , groups=lowerCAmelCase_ , ))
self.layer.append(
MobileNetVaConvLayer(
lowerCAmelCase_ , in_channels=lowerCAmelCase_ , out_channels=lowerCAmelCase_ , kernel_size=1 , ))
lowercase_ = nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase_ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[torch.Tensor] = None , lowerCAmelCase_ : Optional[bool] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""")
lowercase_ = self.conv_stem(lowerCAmelCase_)
lowercase_ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
lowercase_ = layer_module(lowerCAmelCase_)
if output_hidden_states:
lowercase_ = all_hidden_states + (hidden_states,)
lowercase_ = hidden_states
if self.pooler is not None:
lowercase_ = torch.flatten(self.pooler(lowerCAmelCase_) , start_dim=1)
else:
lowercase_ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCAmelCase_ , pooler_output=lowerCAmelCase_ , hidden_states=lowerCAmelCase_ , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Union[str, Any] , lowerCAmelCase_ : MobileNetVaConfig):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = MobileNetVaModel(lowerCAmelCase_)
lowercase_ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ = nn.Dropout(config.classifier_dropout_prob , inplace=lowerCAmelCase_)
lowercase_ = nn.Linear(lowerCAmelCase_ , config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Optional[torch.Tensor] = None , lowerCAmelCase_ : Optional[bool] = None , lowerCAmelCase_ : Optional[torch.Tensor] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.mobilenet_va(lowerCAmelCase_ , output_hidden_states=lowerCAmelCase_ , return_dict=lowerCAmelCase_)
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier(self.dropout(lowerCAmelCase_))
lowercase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ = """single_label_classification"""
else:
lowercase_ = """multi_label_classification"""
if self.config.problem_type == "regression":
lowercase_ = MSELoss()
if self.num_labels == 1:
lowercase_ = loss_fct(logits.squeeze() , labels.squeeze())
else:
lowercase_ = loss_fct(lowerCAmelCase_ , lowerCAmelCase_)
elif self.config.problem_type == "single_label_classification":
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
lowercase_ = BCEWithLogitsLoss()
lowercase_ = loss_fct(lowerCAmelCase_ , lowerCAmelCase_)
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=lowerCAmelCase_ , logits=lowerCAmelCase_ , hidden_states=outputs.hidden_states , )
| 354
|
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=6_4 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=3_7 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Dict=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = MegatronBertModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MegatronBertForMaskedLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = MegatronBertForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForNextSentencePrediction(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForPreTraining(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , next_sentence_label=lowerCAmelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = MegatronBertForQuestionAnswering(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForTokenClassification(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MegatronBertForMultipleChoice(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
# test_resize_embeddings = False
lowercase__ = False
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
if return_labels:
if model_class in get_values(lowerCAmelCase_):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_)
return inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = MegatronBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return torch.tensor(
__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase , )
UpperCAmelCase : Any = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
@unittest.skip("""Model is not available.""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ = os.path.join(os.environ["""MYDIR"""] , lowerCAmelCase_)
lowercase_ = MegatronBertModel.from_pretrained(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.half()
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = torch.Size((1, 9, 1_0_2_4))
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3):
for jj in range(3):
lowercase_ = output[0, ii, jj]
lowercase_ = expected[3 * ii + jj]
lowercase_ = """ii={} jj={} a={} b={}""".format(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
self.assertTrue(math.isclose(lowerCAmelCase_ , lowerCAmelCase_ , rel_tol=lowerCAmelCase_ , abs_tol=lowerCAmelCase_) , msg=lowerCAmelCase_)
| 313
| 0
|
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase : int = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = Github(os.environ["""GITHUB_TOKEN"""] )
lowercase_ = g.get_repo("""huggingface/diffusers""" )
lowercase_ = repo.get_issues(state="""open""" )
for issue in open_issues:
lowercase_ = sorted(issue.get_comments() , key=lambda __lowerCAmelCase : i.created_at , reverse=__lowerCAmelCase )
lowercase_ = comments[0] if len(__lowerCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 355
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows != columns:
lowercase_ = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__lowerCAmelCase )
lowercase_ = np.zeros((rows, columns) )
lowercase_ = np.zeros((rows, columns) )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase_ = (table[i][j] - total) / upper[j][j]
lowercase_ = 1
for j in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
lowercase_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Any = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",
"MobileBertOnnxConfig",
],
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Optional[Any] = ["MobileBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Optional[int] = [
"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileBertForMaskedLM",
"MobileBertForMultipleChoice",
"MobileBertForNextSentencePrediction",
"MobileBertForPreTraining",
"MobileBertForQuestionAnswering",
"MobileBertForSequenceClassification",
"MobileBertForTokenClassification",
"MobileBertLayer",
"MobileBertModel",
"MobileBertPreTrainedModel",
"load_tf_weights_in_mobilebert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = [
"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertMainLayer",
"TFMobileBertModel",
"TFMobileBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 356
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [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(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
| 0
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = RobertaConfig
lowercase__ = "roberta"
def __init__( self : Tuple , lowerCAmelCase_ : int):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = RobertaEmbeddings(lowerCAmelCase_)
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = RobertaConfig
lowercase__ = "roberta"
def __init__( self : int , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = config.num_hidden_layers
lowercase_ = DeeRobertaModel(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.Linear(config.hidden_size , self.config.num_labels)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=-1 , lowerCAmelCase_ : Optional[int]=False , ):
"""simple docstring"""
lowercase_ = self.num_layers
try:
lowercase_ = self.roberta(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , )
lowercase_ = outputs[1]
lowercase_ = self.dropout(lowerCAmelCase_)
lowercase_ = self.classifier(lowerCAmelCase_)
lowercase_ = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowercase_ = e.message
lowercase_ = e.exit_layer
lowercase_ = outputs[0]
if not self.training:
lowercase_ = entropy(lowerCAmelCase_)
lowercase_ = []
lowercase_ = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
# work with highway exits
lowercase_ = []
for highway_exit in outputs[-1]:
lowercase_ = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCAmelCase_)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(highway_logits.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(highway_logits.view(-1 , self.num_labels) , labels.view(-1))
highway_losses.append(lowerCAmelCase_)
if train_highway:
lowercase_ = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
lowercase_ = (loss,) + outputs
if not self.training:
lowercase_ = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowercase_ = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 357
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , 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]
| 313
| 0
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 358
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Dict=9_9 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : str=4 , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : List[Any]=0.02 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
lowercase_ = initializer_range
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowercase_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase_ , )
lowercase_ = prepare_blenderbot_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = 99
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
lowercase_ = input_ids.shape[0]
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._get_config_and_data()
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = lm_model(input_ids=lowerCAmelCase_)
lowercase_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa)
lowercase_ = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa)
lowercase_ = lm_model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_)
lowercase_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(lowerCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowercase__ = True
lowercase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = FlaxBlenderbotModelTester(self)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : str):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase_ = np.ones((1, 1)) * model.config.eos_token_id
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""")
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
lowercase_ = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
lowercase_ = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCAmelCase_)
lowercase_ = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")
lowercase_ = ["""Sam"""]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""jax""")
lowercase_ = model.generate(**lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = """Sam is a great name. It means \"sun\" in Gaelic."""
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , **lowerCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 313
| 0
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "deformable_detr"
lowercase__ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Tuple , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : Optional[int]=3_0_0 , lowerCAmelCase_ : int=1_0_2_4 , lowerCAmelCase_ : Optional[Any]=6 , lowerCAmelCase_ : List[Any]=1_0_2_4 , lowerCAmelCase_ : str=8 , lowerCAmelCase_ : List[str]=6 , lowerCAmelCase_ : Union[str, Any]=1_0_2_4 , lowerCAmelCase_ : Optional[int]=8 , lowerCAmelCase_ : Dict=0.0 , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : int="relu" , lowerCAmelCase_ : Optional[int]=2_5_6 , lowerCAmelCase_ : List[Any]=0.1 , lowerCAmelCase_ : str=0.0 , lowerCAmelCase_ : List[Any]=0.0 , lowerCAmelCase_ : Tuple=0.02 , lowerCAmelCase_ : Optional[int]=1.0 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : List[str]="sine" , lowerCAmelCase_ : Optional[int]="resnet50" , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : str=False , lowerCAmelCase_ : Optional[Any]=3_0_0 , lowerCAmelCase_ : Dict=False , lowerCAmelCase_ : Optional[int]=1 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : List[Any]=2 , lowerCAmelCase_ : List[Any]=1 , lowerCAmelCase_ : Tuple=1 , lowerCAmelCase_ : Any=5 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Optional[int]=0.25 , lowerCAmelCase_ : List[str]=False , **lowerCAmelCase_ : Dict , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""")
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""")
lowercase_ = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_):
lowercase_ = backbone_config.get("""model_type""")
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(lowerCAmelCase_)
lowercase_ = use_timm_backbone
lowercase_ = backbone_config
lowercase_ = num_channels
lowercase_ = num_queries
lowercase_ = max_position_embeddings
lowercase_ = d_model
lowercase_ = encoder_ffn_dim
lowercase_ = encoder_layers
lowercase_ = encoder_attention_heads
lowercase_ = decoder_ffn_dim
lowercase_ = decoder_layers
lowercase_ = decoder_attention_heads
lowercase_ = dropout
lowercase_ = attention_dropout
lowercase_ = activation_dropout
lowercase_ = activation_function
lowercase_ = init_std
lowercase_ = init_xavier_std
lowercase_ = encoder_layerdrop
lowercase_ = auxiliary_loss
lowercase_ = position_embedding_type
lowercase_ = backbone
lowercase_ = use_pretrained_backbone
lowercase_ = dilation
# deformable attributes
lowercase_ = num_feature_levels
lowercase_ = encoder_n_points
lowercase_ = decoder_n_points
lowercase_ = two_stage
lowercase_ = two_stage_num_proposals
lowercase_ = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""")
# Hungarian matcher
lowercase_ = class_cost
lowercase_ = bbox_cost
lowercase_ = giou_cost
# Loss coefficients
lowercase_ = mask_loss_coefficient
lowercase_ = dice_loss_coefficient
lowercase_ = bbox_loss_coefficient
lowercase_ = giou_loss_coefficient
lowercase_ = eos_coefficient
lowercase_ = focal_alpha
lowercase_ = disable_custom_kernels
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_)
@property
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
return self.d_model
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = copy.deepcopy(self.__dict__)
if self.backbone_config is not None:
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
| 359
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313
| 0
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
lowercase_ = len(__lowerCAmelCase )
for _ in range(__lowerCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lowercase_ , lowercase_ = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCAmelCase : Dict = list(range(10, 0, -1))
print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")
| 360
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 313
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|
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Any=1_3 , lowerCAmelCase_ : int=7 , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[int]=False , lowerCAmelCase_ : str=True , lowerCAmelCase_ : List[Any]=9_9 , lowerCAmelCase_ : Any=3_2 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : List[str]=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Tuple="gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Optional[Any]=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Dict=3 , lowerCAmelCase_ : Tuple=4 , lowerCAmelCase_ : Optional[Any]=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , use_stable_embedding=lowerCAmelCase_ , )
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = OpenLlamaModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , ):
"""simple docstring"""
lowercase_ = True
lowercase_ = OpenLlamaModel(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , )
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
lowercase_ = OpenLlamaForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : int , ):
"""simple docstring"""
lowercase_ = True
lowercase_ = True
lowercase_ = OpenLlamaForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
# first forward pass
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , use_cache=lowerCAmelCase_ , )
lowercase_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase_ = ids_tensor((self.batch_size, 3) , config.vocab_size)
lowercase_ = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
lowercase_ = torch.cat([input_ids, next_tokens] , dim=-1)
lowercase_ = torch.cat([input_mask, next_mask] , dim=-1)
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , output_hidden_states=lowerCAmelCase_ , )["""hidden_states"""][0]
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , output_hidden_states=lowerCAmelCase_ , )["""hidden_states"""][0]
# select random slice
lowercase_ = ids_tensor((1,) , output_from_past.shape[-1]).item()
lowercase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-3))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowercase__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowercase__ = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = OpenLlamaModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase_ = type
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = 3
lowercase_ = input_dict["""input_ids"""]
lowercase_ = input_ids.ne(1).to(lowerCAmelCase_)
lowercase_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
lowercase_ = OpenLlamaForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = 3
lowercase_ = """single_label_classification"""
lowercase_ = input_dict["""input_ids"""]
lowercase_ = input_ids.ne(1).to(lowerCAmelCase_)
lowercase_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
lowercase_ = OpenLlamaForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = 3
lowercase_ = """multi_label_classification"""
lowercase_ = input_dict["""input_ids"""]
lowercase_ = input_ids.ne(1).to(lowerCAmelCase_)
lowercase_ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size).to(torch.float)
lowercase_ = OpenLlamaForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)])
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = ids_tensor([1, 1_0] , config.vocab_size)
lowercase_ = ids_tensor([1, int(config.max_position_embeddings * 1.5)] , config.vocab_size)
set_seed(4_2) # Fixed seed at init time so the two models get the same random weights
lowercase_ = OpenLlamaModel(lowerCAmelCase_)
original_model.to(lowerCAmelCase_)
original_model.eval()
lowercase_ = original_model(lowerCAmelCase_).last_hidden_state
lowercase_ = original_model(lowerCAmelCase_).last_hidden_state
set_seed(4_2) # Fixed seed at init time so the two models get the same random weights
lowercase_ = {"""type""": scaling_type, """factor""": 10.0}
lowercase_ = OpenLlamaModel(lowerCAmelCase_)
scaled_model.to(lowerCAmelCase_)
scaled_model.eval()
lowercase_ = scaled_model(lowerCAmelCase_).last_hidden_state
lowercase_ = scaled_model(lowerCAmelCase_).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-5))
else:
self.assertFalse(torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-5))
| 361
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
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|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = IFInpaintingSuperResolutionPipeline
lowercase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
lowercase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"} )
lowercase__ = PipelineTesterMixin.required_optional_params - {"latents"}
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return self._get_superresolution_dummy_components()
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple=0):
"""simple docstring"""
if str(lowerCAmelCase_).startswith("""mps"""):
lowercase_ = torch.manual_seed(lowerCAmelCase_)
else:
lowercase_ = torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
lowercase_ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
lowercase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
lowercase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
lowercase_ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""")
def _UpperCAmelCase ( self : str):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self._test_save_load_local()
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(lowerCAmelCase_ , """hidden_sizes"""))
self.parent.assertTrue(hasattr(lowerCAmelCase_ , """num_attention_heads"""))
self.parent.assertTrue(hasattr(lowerCAmelCase_ , """num_encoder_blocks"""))
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Optional[int]=6_4 , lowerCAmelCase_ : Tuple=3 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : Any=[2, 2, 2, 2] , lowerCAmelCase_ : str=[8, 4, 2, 1] , lowerCAmelCase_ : Any=[1_6, 3_2, 6_4, 1_2_8] , lowerCAmelCase_ : List[str]=[1, 4, 8, 1_6] , lowerCAmelCase_ : Optional[Any]=[1, 2, 4, 8] , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : Tuple=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = num_encoder_blocks
lowercase_ = sr_ratios
lowercase_ = depths
lowercase_ = hidden_sizes
lowercase_ = downsampling_rates
lowercase_ = num_attention_heads
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = scope
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
lowercase_ = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = SegformerModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_)
lowercase_ = lowercase_ = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = SegformerForSemanticSegmentation(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
lowercase_ = model(lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
self.parent.assertGreater(result.loss , 0.0)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = 1
lowercase_ = SegformerForSemanticSegmentation(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size)).to(lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertGreater(result.loss , 0.0)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = SegformerModelTester(self)
lowercase_ = SegformerConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase_)
@unittest.skip("""SegFormer does not use inputs_embeds""")
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
pass
@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""")
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = True
for model_class in self.all_model_classes:
lowercase_ = True
lowercase_ = False
lowercase_ = True
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.attentions
lowercase_ = sum(self.model_tester.depths)
self.assertEqual(len(lowerCAmelCase_) , lowerCAmelCase_)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase_ = True
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.attentions
self.assertEqual(len(lowerCAmelCase_) , lowerCAmelCase_)
# verify the first attentions (first block, first layer)
lowercase_ = (self.model_tester.image_size // 4) ** 2
lowercase_ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
lowercase_ = (self.model_tester.image_size // 3_2) ** 2
lowercase_ = (self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:]) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
lowercase_ = len(lowerCAmelCase_)
# Check attention is always last and order is fine
lowercase_ = True
lowercase_ = True
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
self.assertEqual(out_len + 1 , len(lowerCAmelCase_))
lowercase_ = outputs.attentions
self.assertEqual(len(lowerCAmelCase_) , lowerCAmelCase_)
# verify the first attentions (first block, first layer)
lowercase_ = (self.model_tester.image_size // 4) ** 2
lowercase_ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple):
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.hidden_states
lowercase_ = self.model_tester.num_encoder_blocks
self.assertEqual(len(lowerCAmelCase_) , lowerCAmelCase_)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
if not self.model_tester.is_training:
return
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCAmelCase_):
continue
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.train()
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
lowercase_ = model(**lowerCAmelCase_).loss
loss.backward()
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
pass
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = SegformerModel.from_pretrained(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE () -> int:
'''simple docstring'''
lowercase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=lowerCAmelCase_ , align=lowerCAmelCase_ , do_random_crop=lowerCAmelCase_)
lowercase_ = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""").to(
lowerCAmelCase_)
lowercase_ = prepare_img()
lowercase_ = image_processor(images=lowerCAmelCase_ , return_tensors="""pt""")
lowercase_ = encoded_inputs.pixel_values.to(lowerCAmelCase_)
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)
lowercase_ = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8))
self.assertEqual(outputs.logits.shape , lowerCAmelCase_)
lowercase_ = torch.tensor(
[
[[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
[[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
[[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
]).to(lowerCAmelCase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , lowerCAmelCase_ , atol=1E-4))
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=lowerCAmelCase_ , align=lowerCAmelCase_ , do_random_crop=lowerCAmelCase_)
lowercase_ = SegformerForSemanticSegmentation.from_pretrained(
"""nvidia/segformer-b1-finetuned-cityscapes-1024-1024""").to(lowerCAmelCase_)
lowercase_ = prepare_img()
lowercase_ = image_processor(images=lowerCAmelCase_ , return_tensors="""pt""")
lowercase_ = encoded_inputs.pixel_values.to(lowerCAmelCase_)
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)
lowercase_ = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8))
self.assertEqual(outputs.logits.shape , lowerCAmelCase_)
lowercase_ = torch.tensor(
[
[[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
[[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
[[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
]).to(lowerCAmelCase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , lowerCAmelCase_ , atol=1E-1))
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=lowerCAmelCase_ , align=lowerCAmelCase_ , do_random_crop=lowerCAmelCase_)
lowercase_ = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""").to(
lowerCAmelCase_)
lowercase_ = prepare_img()
lowercase_ = image_processor(images=lowerCAmelCase_ , return_tensors="""pt""")
lowercase_ = encoded_inputs.pixel_values.to(lowerCAmelCase_)
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)
lowercase_ = outputs.logits.detach().cpu()
lowercase_ = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_ , target_sizes=[(5_0_0, 3_0_0)])
lowercase_ = torch.Size((5_0_0, 3_0_0))
self.assertEqual(segmentation[0].shape , lowerCAmelCase_)
lowercase_ = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_)
lowercase_ = torch.Size((1_2_8, 1_2_8))
self.assertEqual(segmentation[0].shape , lowerCAmelCase_)
| 363
|
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase_ = 0 if allow_empty_subarrays else float("""-inf""" )
lowercase_ = 0.0
for num in arr:
lowercase_ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowercase_ = max(__lowerCAmelCase , __lowerCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : Union[str, Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 313
| 0
|
UpperCAmelCase : str = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
UpperCAmelCase : list[bool | None] = [None] * 1000_0000
UpperCAmelCase : str = True
UpperCAmelCase : List[Any] = False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> bool:
'''simple docstring'''
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
lowercase_ = chain(next_number(__lowerCAmelCase ) )
lowercase_ = number_chain
while number < 10_00_00_00:
lowercase_ = number_chain
number *= 10
return number_chain
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 10_00_00_00 ) -> int:
'''simple docstring'''
for i in range(1 , __lowerCAmelCase ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"{solution() = }")
| 364
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 313
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|
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
UpperCAmelCase : List[str] = ""
UpperCAmelCase : List[str] = ""
UpperCAmelCase : Any = ""
UpperCAmelCase : str = ""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> None:
'''simple docstring'''
lowercase_ = tweepy.OAuthHandler(__lowerCAmelCase , __lowerCAmelCase )
auth.set_access_token(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = tweepy.API(__lowerCAmelCase )
# initialize a list to hold all the tweepy Tweets
lowercase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
lowercase_ = api.user_timeline(screen_name=__lowerCAmelCase , count=2_00 )
# save most recent tweets
alltweets.extend(__lowerCAmelCase )
# save the id of the oldest tweet less one
lowercase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__lowerCAmelCase ) > 0:
print(F'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
lowercase_ = api.user_timeline(
screen_name=__lowerCAmelCase , count=2_00 , max_id=__lowerCAmelCase )
# save most recent tweets
alltweets.extend(__lowerCAmelCase )
# update the id of the oldest tweet less one
lowercase_ = alltweets[-1].id - 1
print(F'''...{len(__lowerCAmelCase )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
lowercase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F'''new_{screen_name}_tweets.csv''' , """w""" ) as f:
lowercase_ = csv.writer(__lowerCAmelCase )
writer.writerow(["""id""", """created_at""", """text"""] )
writer.writerows(__lowerCAmelCase )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("FirePing32")
| 365
|
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x20000 and cp <= 0x2a6df) #
or (cp >= 0x2a700 and cp <= 0x2b73f) #
or (cp >= 0x2b740 and cp <= 0x2b81f) #
or (cp >= 0x2b820 and cp <= 0x2ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2f800 and cp <= 0x2fa1f) #
): #
return True
return False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
for char in word:
lowercase_ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase_ = set()
for token in tokens:
lowercase_ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowercase_ = list(__lowerCAmelCase )
return word_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
lowercase_ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowercase_ = bert_tokens
lowercase_ , lowercase_ = 0, len(__lowerCAmelCase )
while start < end:
lowercase_ = True
if is_chinese(bert_word[start] ):
lowercase_ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowercase_ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowercase_ = """##""" + bert_word[j]
lowercase_ = start + i
lowercase_ = False
break
if single_word:
start += 1
return bert_word
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
lowercase_ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for i in range(0 , len(__lowerCAmelCase ) , 1_00 ):
lowercase_ = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=5_12 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowercase_ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = []
for id in input_ids:
lowercase_ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowercase_ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowercase_ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowercase_ = f.readlines()
lowercase_ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowercase_ = LTP(args.ltp ) # faster in GPU device
lowercase_ = BertTokenizer.from_pretrained(args.bert )
lowercase_ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowercase_ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
UpperCAmelCase : int = parser.parse_args()
main(args)
| 313
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|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return 0
elif n == 2:
return 1
else:
lowercase_ = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
lowercase_ = 2
while digits < n:
index += 1
lowercase_ = len(str(fibonacci(__lowerCAmelCase ) ) )
return index
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 10_00 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(__lowerCAmelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 366
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
lowercase_ = int(__lowerCAmelCase )
lowercase_ = int(__lowerCAmelCase )
lowercase_ = []
for temp in range(int(__lowerCAmelCase ) ):
series.append(F'''1 / {pow(temp + 1 , int(__lowerCAmelCase ) )}''' if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[str] = int(input("Enter the last number (nth term) of the P-Series"))
UpperCAmelCase : Tuple = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 313
| 0
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
"""simple docstring"""
lowercase__ = "sequence-classification"
def __init__( self : List[Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
if type(lowerCAmelCase_) == dict:
lowercase_ = Namespace(**lowerCAmelCase_)
lowercase_ = glue_output_modes[hparams.task]
lowercase_ = glue_tasks_num_labels[hparams.task]
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ , self.mode)
def _UpperCAmelCase ( self : Optional[Any] , **lowerCAmelCase_ : List[str]):
"""simple docstring"""
return self.model(**lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
lowercase_ = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
lowercase_ = self(**lowerCAmelCase_)
lowercase_ = outputs[0]
lowercase_ = self.trainer.lr_schedulers[0]["""scheduler"""]
lowercase_ = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.hparams
lowercase_ = processors[args.task]()
lowercase_ = processor.get_labels()
for mode in ["train", "dev"]:
lowercase_ = self._feature_file(lowerCAmelCase_)
if os.path.exists(lowerCAmelCase_) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , lowerCAmelCase_)
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir)
lowercase_ = (
processor.get_dev_examples(args.data_dir)
if mode == """dev"""
else processor.get_train_examples(args.data_dir)
)
lowercase_ = convert_examples_to_features(
lowerCAmelCase_ , 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""" , lowerCAmelCase_)
torch.save(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : int , lowerCAmelCase_ : bool = False):
"""simple docstring"""
lowercase_ = """dev""" if mode == """test""" else mode
lowercase_ = self._feature_file(lowerCAmelCase_)
logger.info("""Loading features from cached file %s""" , lowerCAmelCase_)
lowercase_ = torch.load(lowerCAmelCase_)
lowercase_ = torch.tensor([f.input_ids for f in features] , dtype=torch.long)
lowercase_ = torch.tensor([f.attention_mask for f in features] , dtype=torch.long)
lowercase_ = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long)
if self.hparams.glue_output_mode == "classification":
lowercase_ = torch.tensor([f.label for f in features] , dtype=torch.long)
elif self.hparams.glue_output_mode == "regression":
lowercase_ = torch.tensor([f.label for f in features] , dtype=torch.float)
return DataLoader(
TensorDataset(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_) , batch_size=lowerCAmelCase_ , shuffle=lowerCAmelCase_ , )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
lowercase_ = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
lowercase_ = self(**lowerCAmelCase_)
lowercase_ , lowercase_ = outputs[:2]
lowercase_ = logits.detach().cpu().numpy()
lowercase_ = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = torch.stack([x["""val_loss"""] for x in outputs]).mean().detach().cpu().item()
lowercase_ = np.concatenate([x["""pred"""] for x in outputs] , axis=0)
if self.hparams.glue_output_mode == "classification":
lowercase_ = np.argmax(lowerCAmelCase_ , axis=1)
elif self.hparams.glue_output_mode == "regression":
lowercase_ = np.squeeze(lowerCAmelCase_)
lowercase_ = np.concatenate([x["""target"""] for x in outputs] , axis=0)
lowercase_ = [[] for _ in range(out_label_ids.shape[0])]
lowercase_ = [[] for _ in range(out_label_ids.shape[0])]
lowercase_ = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task , lowerCAmelCase_ , lowerCAmelCase_)}
lowercase_ = dict(results.items())
lowercase_ = results
return ret, preds_list, out_label_list
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : list):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._eval_end(lowerCAmelCase_)
lowercase_ = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._eval_end(lowerCAmelCase_)
lowercase_ = 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 _UpperCAmelCase ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : Any):
"""simple docstring"""
BaseTransformer.add_model_specific_args(lowerCAmelCase_ , lowerCAmelCase_)
parser.add_argument(
"""--max_seq_length""" , default=1_2_8 , type=lowerCAmelCase_ , 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=lowerCAmelCase_ , required=lowerCAmelCase_ , help="""The GLUE task to run""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=lowerCAmelCase_ , 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 _SCREAMING_SNAKE_CASE () -> List[Any]:
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
add_generic_args(__lowerCAmelCase , os.getcwd() )
lowercase_ = GLUETransformer.add_model_specific_args(__lowerCAmelCase , os.getcwd() )
lowercase_ = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
lowercase_ = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}''' , )
os.makedirs(args.output_dir )
lowercase_ = GLUETransformer(__lowerCAmelCase )
lowercase_ = generic_train(__lowerCAmelCase , __lowerCAmelCase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
lowercase_ = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=__lowerCAmelCase ) )
lowercase_ = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(__lowerCAmelCase )
if __name__ == "__main__":
main()
| 367
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
self.assertEqual(x.component(0) , 1)
self.assertEqual(x.component(2) , 3)
lowercase_ = Vector()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(lowerCAmelCase_) , """(0,0,0,0,0,1)""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3, 4])
self.assertEqual(len(lowerCAmelCase_) , 4)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = Vector([1, 2])
lowercase_ = Vector([1, 2, 3, 4, 5])
lowercase_ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
lowercase_ = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3)
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3)
self.assertEqual(z.euclidean_length() , 0)
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x + y).component(0) , 2)
self.assertEqual((x + y).component(1) , 3)
self.assertEqual((x + y).component(2) , 4)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 1, 1])
self.assertEqual((x - y).component(0) , 0)
self.assertEqual((x - y).component(1) , 1)
self.assertEqual((x - y).component(2) , 2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([2, -1, 4]) # for test of dot product
lowercase_ = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0) , """(3.0,6.0,9.0)""")
self.assertEqual((a * b) , 0)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.assertEqual(str(zero_vector(1_0)).count("""0""") , 1_0)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1)) , """(0,1,0)""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 2, 3])
lowercase_ = Vector([1, 0, 1])
self.assertEqual(str(axpy(2 , lowerCAmelCase_ , lowerCAmelCase_)) , """(3,4,7)""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0, 0, 0, 0])
lowercase_ = x.copy()
self.assertEqual(str(lowerCAmelCase_) , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Vector([1, 0, 0])
x.change_component(0 , 0)
x.change_component(1 , 1)
self.assertEqual(str(lowerCAmelCase_) , """(0,1,0)""")
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y] , a.minor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y] , a.cofactor(lowerCAmelCase_ , lowerCAmelCase_))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(-5 , a.determinant())
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3)
lowercase_ = Vector([1, 2, 3])
self.assertEqual("""(14,32,50)""" , str(a * x))
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2))
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
a.change_component(0 , 2 , 5)
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCAmelCase_))
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(7 , a.component(2 , 1) , 0.01)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
lowercase_ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b))
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5)) , )
if __name__ == "__main__":
unittest.main()
| 313
| 0
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple=1_3 , lowerCAmelCase_ : Any=7 , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=3_2 , lowerCAmelCase_ : Any=5 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : Tuple=3_7 , lowerCAmelCase_ : Optional[Any]="gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Any=5_1_2 , lowerCAmelCase_ : Any=1_6 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : List[str]=0.02 , lowerCAmelCase_ : Optional[int]=4 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_attention_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_choices
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_attention_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = True
lowercase__ = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxRoFormerModelTester(self)
@slow
def _UpperCAmelCase ( self : str):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=lowerCAmelCase_)
lowercase_ = model(np.ones((1, 1)))
self.assertIsNotNone(lowerCAmelCase_)
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
lowercase_ = jnp.array([[0, 1, 2, 3, 4, 5]])
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = 5_0_0_0_0
lowercase_ = (1, 6, vocab_size)
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4))
| 368
|
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < end:
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ , lowercase_ = _in_place_partition(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
count += _in_place_quick_sort(__lowerCAmelCase , __lowerCAmelCase , p - 1 )
count += _in_place_quick_sort(__lowerCAmelCase , p + 1 , __lowerCAmelCase )
return count
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0
lowercase_ = randint(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = a[end]
lowercase_ = a[pivot]
lowercase_ = temp
lowercase_ = start - 1
for index in range(__lowerCAmelCase , __lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowercase_ = new_pivot_index + 1
lowercase_ = a[new_pivot_index]
lowercase_ = a[index]
lowercase_ = temp
lowercase_ = a[new_pivot_index + 1]
lowercase_ = a[end]
lowercase_ = temp
return new_pivot_index + 1, count
UpperCAmelCase : Union[str, Any] = TemporaryFile()
UpperCAmelCase : Optional[int] = 100 # 1000 elements are to be sorted
UpperCAmelCase , UpperCAmelCase : List[str] = 0, 1 # mean and standard deviation
UpperCAmelCase : Optional[Any] = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase : List[str] = np.load(outfile)
UpperCAmelCase : List[Any] = len(M) - 1
UpperCAmelCase : Optional[int] = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 313
| 0
|
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "SpeechT5FeatureExtractor"
lowercase__ = "SpeechT5Tokenizer"
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int):
"""simple docstring"""
super().__init__(lowerCAmelCase_ , lowerCAmelCase_)
def __call__( self : Tuple , *lowerCAmelCase_ : Tuple , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.pop("""audio""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""text""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""text_target""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""audio_target""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""sampling_rate""" , lowerCAmelCase_)
if audio is not None and text is not None:
raise ValueError(
"""Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?""")
if audio_target is not None and text_target is not None:
raise ValueError(
"""Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?""")
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"""You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.""")
if audio is not None:
lowercase_ = self.feature_extractor(lowerCAmelCase_ , *lowerCAmelCase_ , sampling_rate=lowerCAmelCase_ , **lowerCAmelCase_)
elif text is not None:
lowercase_ = self.tokenizer(lowerCAmelCase_ , **lowerCAmelCase_)
else:
lowercase_ = None
if audio_target is not None:
lowercase_ = self.feature_extractor(audio_target=lowerCAmelCase_ , *lowerCAmelCase_ , sampling_rate=lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = targets["""input_values"""]
elif text_target is not None:
lowercase_ = self.tokenizer(lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = targets["""input_ids"""]
else:
lowercase_ = None
if inputs is None:
return targets
if targets is not None:
lowercase_ = labels
lowercase_ = targets.get("""attention_mask""")
if decoder_attention_mask is not None:
lowercase_ = decoder_attention_mask
return inputs
def _UpperCAmelCase ( self : Optional[Any] , *lowerCAmelCase_ : Optional[Any] , **lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = kwargs.pop("""input_values""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""input_ids""" , lowerCAmelCase_)
lowercase_ = kwargs.pop("""labels""" , lowerCAmelCase_)
if input_values is not None and input_ids is not None:
raise ValueError("""Cannot process both `input_values` and `input_ids` inputs.""")
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"""You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.""")
if input_values is not None:
lowercase_ = self.feature_extractor.pad(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_)
elif input_ids is not None:
lowercase_ = self.tokenizer.pad(lowerCAmelCase_ , **lowerCAmelCase_)
else:
lowercase_ = None
if labels is not None:
if "input_ids" in labels or (isinstance(lowerCAmelCase_ , lowerCAmelCase_) and "input_ids" in labels[0]):
lowercase_ = self.tokenizer.pad(lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = targets["""input_ids"""]
else:
lowercase_ = self.feature_extractor.feature_size
lowercase_ = self.feature_extractor.num_mel_bins
lowercase_ = self.feature_extractor.pad(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = feature_size_hack
lowercase_ = targets["""input_values"""]
else:
lowercase_ = None
if inputs is None:
return targets
if targets is not None:
lowercase_ = labels
lowercase_ = targets.get("""attention_mask""")
if decoder_attention_mask is not None:
lowercase_ = decoder_attention_mask
return inputs
def _UpperCAmelCase ( self : Optional[Any] , *lowerCAmelCase_ : Any , **lowerCAmelCase_ : List[str]):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any] , *lowerCAmelCase_ : str , **lowerCAmelCase_ : List[str]):
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_)
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ = """"""
else:
lowercase_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''' )
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ = in_proj_weight[
: config.hidden_size, :
]
lowercase_ = in_proj_bias[: config.hidden_size]
lowercase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ = in_proj_bias[-config.hidden_size :]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = dct.pop(__lowerCAmelCase )
lowercase_ = val
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = ViTMSNConfig()
lowercase_ = 10_00
lowercase_ = """datasets/huggingface/label-files"""
lowercase_ = """imagenet-1k-id2label.json"""
lowercase_ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase ) , """r""" ) )
lowercase_ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowercase_ = idalabel
lowercase_ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase_ = 3_84
lowercase_ = 15_36
lowercase_ = 6
elif "l16" in checkpoint_url:
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
elif "b4" in checkpoint_url:
lowercase_ = 4
elif "l7" in checkpoint_url:
lowercase_ = 7
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
lowercase_ = ViTMSNModel(__lowerCAmelCase )
lowercase_ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""target_encoder"""]
lowercase_ = ViTImageProcessor(size=config.image_size )
remove_projection_head(__lowerCAmelCase )
lowercase_ = create_rename_keys(__lowerCAmelCase , base_model=__lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase , base_model=__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
lowercase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
lowercase_ = ViTImageProcessor(
size=config.image_size , image_mean=__lowerCAmelCase , image_std=__lowerCAmelCase )
lowercase_ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowercase_ = model(**__lowerCAmelCase )
lowercase_ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowercase_ = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
lowercase_ = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
lowercase_ = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
lowercase_ = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
lowercase_ = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __lowerCAmelCase , atol=1E-4 )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313
| 0
|
"""simple docstring"""
from math import pow, sqrt
def _SCREAMING_SNAKE_CASE (*__lowerCAmelCase ) -> bool:
'''simple docstring'''
lowercase_ = len(__lowerCAmelCase ) > 0 and all(value > 0.0 for value in values )
return result
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float | ValueError:
'''simple docstring'''
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(__lowerCAmelCase , __lowerCAmelCase )
else ValueError("""Input Error: Molar mass values must greater than 0.""" )
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float | ValueError:
'''simple docstring'''
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float | ValueError:
'''simple docstring'''
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float | ValueError:
'''simple docstring'''
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float | ValueError:
'''simple docstring'''
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
| 370
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "perceiver"
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str]=2_5_6 , lowerCAmelCase_ : Dict=1_2_8_0 , lowerCAmelCase_ : List[Any]=7_6_8 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[Any]=2_6 , lowerCAmelCase_ : Optional[Any]=8 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Optional[Any]="kv" , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Union[str, Any]=0.02 , lowerCAmelCase_ : List[Any]=1E-12 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Union[str, Any]=2_6_2 , lowerCAmelCase_ : Union[str, Any]=2_0_4_8 , lowerCAmelCase_ : Any=5_6 , lowerCAmelCase_ : int=[3_6_8, 4_9_6] , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : Dict=1_9_2_0 , lowerCAmelCase_ : Optional[Any]=1_6 , lowerCAmelCase_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **lowerCAmelCase_ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_)
lowercase_ = num_latents
lowercase_ = d_latents
lowercase_ = d_model
lowercase_ = num_blocks
lowercase_ = num_self_attends_per_block
lowercase_ = num_self_attention_heads
lowercase_ = num_cross_attention_heads
lowercase_ = qk_channels
lowercase_ = v_channels
lowercase_ = cross_attention_shape_for_attention
lowercase_ = self_attention_widening_factor
lowercase_ = cross_attention_widening_factor
lowercase_ = hidden_act
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = use_query_residual
# masked language modeling attributes
lowercase_ = vocab_size
lowercase_ = max_position_embeddings
# image classification attributes
lowercase_ = image_size
# flow attributes
lowercase_ = train_size
# multimodal autoencoding attributes
lowercase_ = num_frames
lowercase_ = audio_samples_per_frame
lowercase_ = samples_per_patch
lowercase_ = output_shape
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
@property
def _UpperCAmelCase ( self : str):
"""simple docstring"""
if self.task == "multiple-choice":
lowercase_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""inputs""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
@property
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return 1E-4
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : int = -1 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[TensorType] = None , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 4_0 , lowerCAmelCase_ : int = 4_0 , ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , 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
lowercase_ = preprocessor.num_special_tokens_to_add(lowerCAmelCase_)
lowercase_ = compute_effective_axis_dimension(
lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase_)
# Generate dummy inputs according to compute batch and sequence
lowercase_ = [""" """.join(["""a"""]) * seq_length] * batch_size
lowercase_ = dict(preprocessor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""input_ids""")
return inputs
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ = compute_effective_axis_dimension(lowerCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase_ = self._generate_dummy_images(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = dict(preprocessor(images=lowerCAmelCase_ , return_tensors=lowerCAmelCase_))
lowercase_ = inputs.pop("""pixel_values""")
return inputs
else:
raise ValueError(
"""Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""")
| 313
| 0
|
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
__lowerCamelCase : int = True
from torch.cuda.amp import autocast
__lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Whether to log verbose messages or not."} , )
lowercase__ = field(
default=2.0 , metadata={"help": "Maximum temperature for gumbel softmax."} )
lowercase__ = field(
default=0.5 , metadata={"help": "Minimum temperature for gumbel softmax."} )
lowercase__ = field(
default=0.99_99_95 , metadata={"help": "Decay of gumbel temperature during training."} )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
lowercase_ = logging.WARNING
if model_args.verbose_logging:
lowercase_ = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
lowercase_ = logging.INFO
logger.setLevel(__lowerCAmelCase )
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowercase__ = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
lowercase__ = field(
default="validation" , metadata={
"help": (
"The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
lowercase__ = field(
default="file" , metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
lowercase__ = field(
default=1 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
lowercase__ = field(
default=__UpperCAmelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowercase__ = field(
default=20.0 , metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = 42
lowercase__ = "longest"
lowercase__ = None
lowercase__ = None
def __call__( self : Optional[int] , lowerCAmelCase_ : List[Dict[str, Union[List[int], torch.Tensor]]]):
"""simple docstring"""
lowercase_ = self.feature_extractor.pad(
lowerCAmelCase_ , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
lowercase_ = self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1])
lowercase_ = batch["""input_values"""].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
lowercase_ = self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1)).to(
torch.long)
lowercase_ = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch["""input_values"""].device)
# these two operations makes sure that all values
# before the output lengths indices are attended to
lowercase_ = 1
lowercase_ = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
# sample randomly masked indices
lowercase_ = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=lowerCAmelCase_ , min_masks=2 , )
return batch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : int , *lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : str=1.0 , **lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
super().__init__(*lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = 0
lowercase_ = max_gumbel_temp
lowercase_ = min_gumbel_temp
lowercase_ = gumbel_temp_decay
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : nn.Module , lowerCAmelCase_ : Dict[str, Union[torch.Tensor, Any]]):
"""simple docstring"""
model.train()
lowercase_ = self._prepare_inputs(lowerCAmelCase_)
if self.use_amp:
with autocast():
lowercase_ = self.compute_loss(lowerCAmelCase_ , lowerCAmelCase_)
else:
lowercase_ = self.compute_loss(lowerCAmelCase_ , lowerCAmelCase_)
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowercase_ = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowercase_ = loss.sum() / (inputs["""mask_time_indices"""]).sum()
else:
raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''')
if self.args.gradient_accumulation_steps > 1:
lowercase_ = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase_).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase_ , self.optimizer) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase_)
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp))
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp))
return loss.detach()
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase_ , lowercase_ , lowercase_ = parser.parse_args_into_dataclasses()
configure_logger(__lowerCAmelCase , __lowerCAmelCase )
# Downloading and loading a dataset from the hub.
lowercase_ = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
lowercase_ = DatasetDict()
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]''' , cache_dir=model_args.cache_dir , )
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]''' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
lowercase_ = DatasetDict()
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="""validation""" , cache_dir=model_args.cache_dir , )
lowercase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}''' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
lowercase_ = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=__lowerCAmelCase )
def prepare_dataset(__lowerCAmelCase ):
# check that all files have the correct sampling rate
lowercase_ , lowercase_ = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
lowercase_ = datasets.map(
__lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["""train"""].column_names )
# filter audio files that are too long
lowercase_ = vectorized_datasets.filter(
lambda __lowerCAmelCase : len(data["""speech"""] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(__lowerCAmelCase ):
return feature_extractor(batch["""speech"""] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
lowercase_ = vectorized_datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["""train"""].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
lowercase_ = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"""PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
""" ``config.feat_extract_norm='layer'""" )
lowercase_ = WavaVecaForPreTraining(__lowerCAmelCase )
lowercase_ = DataCollatorForWavaVecaPretraining(model=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
lowercase_ = WavaVecaPreTrainer(
model=__lowerCAmelCase , data_collator=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=vectorized_datasets["""train"""] , eval_dataset=vectorized_datasets["""validation"""] , tokenizer=__lowerCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 371
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = BarthezTokenizer
lowercase__ = BarthezTokenizerFast
lowercase__ = True
lowercase__ = True
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
super().setUp()
lowercase_ = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
tokenizer.save_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase_)
lowercase_ = tokenizer
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """<pad>"""
lowercase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_) , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<s>""")
self.assertEqual(vocab_keys[1] , """<pad>""")
self.assertEqual(vocab_keys[-1] , """<mask>""")
self.assertEqual(len(lowerCAmelCase_) , 1_0_1_1_2_2)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2)
@require_torch
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowercase_ = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
lowercase_ = self.tokenizer(
lowerCAmelCase_ , max_length=len(lowerCAmelCase_) , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , return_tensors="""pt""")
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_)
self.assertEqual((2, 6) , batch.input_ids.shape)
self.assertEqual((2, 6) , batch.attention_mask.shape)
lowercase_ = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = """I was born in 92000, and this is falsé."""
lowercase_ = tokenizer.tokenize(lowerCAmelCase_)
lowercase_ = rust_tokenizer.tokenize(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = self.get_rust_tokenizer()
lowercase_ = tokenizer.encode(lowerCAmelCase_)
lowercase_ = rust_tokenizer.encode(lowerCAmelCase_)
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase_ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""moussaKam/mbarthez""" , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" , sequences=lowerCAmelCase_ , )
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350
|
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Optional[Any] = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class SCREAMING_SNAKE_CASE__ :
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = "gelu"
def __init__( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Any=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : str=9_9 , lowerCAmelCase_ : Tuple=3_2 , lowerCAmelCase_ : Dict=5 , lowerCAmelCase_ : Union[str, Any]=4 , lowerCAmelCase_ : Dict=3_7 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Optional[int]=2_0 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
lowercase_ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
lowercase_ = np.concatenate([input_ids, eos_tensor] , axis=1)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase_ = prepare_pegasus_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.not_equal(__lowerCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowercase_ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = FlaxPegasusModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=None , **lowerCAmelCase_ : Optional[int]):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=lowerCAmelCase_)
lowercase_ = np.ones((1, 1))
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
lowercase_ = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")
lowercase_ = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
lowercase_ = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""np""" , truncation=lowerCAmelCase_ , max_length=5_1_2 , padding=lowerCAmelCase_)
lowercase_ = model.generate(**lowerCAmelCase_ , num_beams=2).sequences
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_)
assert tgt_text == decoded
| 313
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase : Any = TypeVar("T")
UpperCAmelCase : List[Any] = TypeVar("U")
class SCREAMING_SNAKE_CASE__ ( Generic[T, U] ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : T | None , lowerCAmelCase_ : U | None):
"""simple docstring"""
lowercase_ = key
lowercase_ = val
lowercase_ = None
lowercase_ = None
def __repr__( self : List[Any]):
"""simple docstring"""
return (
F'''Node: key: {self.key}, val: {self.val}, '''
F'''has next: {bool(self.next)}, has prev: {bool(self.prev)}'''
)
class SCREAMING_SNAKE_CASE__ ( Generic[T, U] ):
def __init__( self : Optional[int]):
"""simple docstring"""
lowercase_ = DoubleLinkedListNode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = DoubleLinkedListNode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ , lowercase_ = self.rear, self.head
def __repr__( self : Any):
"""simple docstring"""
lowercase_ = ["""DoubleLinkedList"""]
lowercase_ = self.head
while node.next is not None:
rep.append(str(lowerCAmelCase_))
lowercase_ = node.next
rep.append(str(self.rear))
return ",\n ".join(lowerCAmelCase_)
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : DoubleLinkedListNode[T, U]):
"""simple docstring"""
lowercase_ = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
lowercase_ = node
lowercase_ = previous
lowercase_ = node
lowercase_ = self.rear
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : DoubleLinkedListNode[T, U]):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
lowercase_ = node.next
lowercase_ = node.prev
lowercase_ = None
lowercase_ = None
return node
class SCREAMING_SNAKE_CASE__ ( Generic[T, U] ):
lowercase__ = {}
def __init__( self : Optional[Any] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = DoubleLinkedList()
lowercase_ = capacity
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = {}
def __repr__( self : Any):
"""simple docstring"""
return (
F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
F'''capacity={self.capacity}, current size={self.num_keys})'''
)
def __contains__( self : Dict , lowerCAmelCase_ : T):
"""simple docstring"""
return key in self.cache
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : T):
"""simple docstring"""
if key in self.cache:
self.hits += 1
lowercase_ = self.cache[key]
lowercase_ = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowerCAmelCase_)
return node.val
self.miss += 1
return None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : T , lowerCAmelCase_ : U):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
lowercase_ = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowerCAmelCase_) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
lowercase_ = DoubleLinkedListNode(lowerCAmelCase_ , lowerCAmelCase_)
self.list.add(self.cache[key])
self.num_keys += 1
else:
# bump node to the end of the list, update value
lowercase_ = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
lowercase_ = value
self.list.add(lowerCAmelCase_)
@classmethod
def _UpperCAmelCase ( cls : Tuple , lowerCAmelCase_ : int = 1_2_8):
"""simple docstring"""
def cache_decorator_inner(lowerCAmelCase_ : Callable[[T], U]) -> Callable[..., U]:
def cache_decorator_wrapper(*lowerCAmelCase_ : T) -> U:
if func not in cls.decorator_function_to_instance_map:
lowercase_ = LRUCache(lowerCAmelCase_)
lowercase_ = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
lowercase_ = func(*lowerCAmelCase_)
cls.decorator_function_to_instance_map[func].put(args[0] , lowerCAmelCase_)
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowerCAmelCase_ , """cache_info""" , lowerCAmelCase_) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351
|
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = None
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = True
lowercase__ = None
lowercase__ = 1
lowercase__ = None
lowercase__ = False
lowercase__ = None
lowercase__ = None
def _UpperCAmelCase ( self : int):
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(lowerCAmelCase_) for k, v in self.__dict__.items()})
| 313
| 0
|
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1_00_00_00 , __lowerCAmelCase = 10 ) -> int:
'''simple docstring'''
lowercase_ = defaultdict(__lowerCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
lowercase_ = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
lowercase_ = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__lowerCAmelCase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F"{solution() = }")
| 352
|
"""simple docstring"""
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
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : List[Any] = {
"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",
},
}
UpperCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 1_6384,
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = LEDTokenizer
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]="replace" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : List[Any]="</s>" , lowerCAmelCase_ : Optional[Any]="<s>" , lowerCAmelCase_ : Union[str, Any]="<unk>" , lowerCAmelCase_ : List[str]="<pad>" , lowerCAmelCase_ : Dict="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
lowercase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = getattr(lowerCAmelCase_ , pre_tok_state.pop("""type"""))
lowercase_ = add_prefix_space
lowercase_ = pre_tok_class(**lowerCAmelCase_)
lowercase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ = """post_processor"""
lowercase_ = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
if tokenizer_component_instance:
lowercase_ = 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:
lowercase_ = tuple(state["""sep"""])
if "cls" in state:
lowercase_ = tuple(state["""cls"""])
lowercase_ = False
if state.get("""add_prefix_space""" , lowerCAmelCase_) != add_prefix_space:
lowercase_ = add_prefix_space
lowercase_ = True
if state.get("""trim_offsets""" , lowerCAmelCase_) != trim_offsets:
lowercase_ = trim_offsets
lowercase_ = True
if changes_to_apply:
lowercase_ = getattr(lowerCAmelCase_ , state.pop("""type"""))
lowercase_ = component_class(**lowerCAmelCase_)
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""")
return None
return str(self._mask_token)
@mask_token.setter
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else value
lowercase_ = value
def _UpperCAmelCase ( self : Dict , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = kwargs.get("""is_split_into_words""" , lowerCAmelCase_)
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(*lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
lowercase_ = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_)
return tuple(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None):
"""simple docstring"""
lowercase_ = [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 _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
lowercase_ = [self.sep_token_id]
lowercase_ = [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 _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[bool] = None , ):
"""simple docstring"""
lowercase_ = super()._pad(
encoded_inputs=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding_strategy=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ = len(encoded_inputs["""global_attention_mask"""]) != len(lowerCAmelCase_)
if needs_to_be_padded:
lowercase_ = len(lowerCAmelCase_) - 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`
lowercase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side))
return encoded_inputs
| 313
| 0
|
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
if not sentence:
return ""
lowercase_ = dict(zip(__lowerCAmelCase , __lowerCAmelCase ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 353
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 313
| 0
|
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = WavaVecaForSequenceClassification.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
lowercase_ = downstream_dict["""projector.weight"""]
lowercase_ = downstream_dict["""projector.bias"""]
lowercase_ = downstream_dict["""model.post_net.linear.weight"""]
lowercase_ = downstream_dict["""model.post_net.linear.bias"""]
return model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = WavaVecaForAudioFrameClassification.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
lowercase_ = downstream_dict["""model.linear.weight"""]
lowercase_ = downstream_dict["""model.linear.bias"""]
return model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase_ = WavaVecaForXVector.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
lowercase_ = downstream_dict["""connector.weight"""]
lowercase_ = downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowercase_ = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
lowercase_ = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
lowercase_ = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
lowercase_ = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
lowercase_ = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
lowercase_ = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
lowercase_ = downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
lowercase_ = checkpoint["""Downstream"""]
lowercase_ = WavaVecaConfig.from_pretrained(__lowerCAmelCase )
lowercase_ = WavaVecaFeatureExtractor.from_pretrained(
__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , do_normalize=__lowerCAmelCase )
lowercase_ = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
lowercase_ = convert_classification(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif arch.endswith("""ForAudioFrameClassification""" ):
lowercase_ = convert_diarization(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif arch.endswith("""ForXVector""" ):
lowercase_ = convert_xvector(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
lowercase_ = checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(__lowerCAmelCase )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
UpperCAmelCase : Optional[int] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 354
|
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any]=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Tuple=9_9 , lowerCAmelCase_ : List[str]=6_4 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=3_7 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : Optional[int]=1_6 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : List[Any]=0.02 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Optional[Any]=4 , lowerCAmelCase_ : Dict=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_input_mask
lowercase_ = use_token_type_ids
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = embedding_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = type_vocab_size
lowercase_ = type_sequence_label_size
lowercase_ = initializer_range
lowercase_ = num_labels
lowercase_ = num_choices
lowercase_ = scope
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowercase_ = None
if self.use_input_mask:
lowercase_ = random_attention_mask([self.batch_size, self.seq_length])
lowercase_ = None
if self.use_token_type_ids:
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowercase_ = None
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowercase_ = ids_tensor([self.batch_size] , self.num_choices)
lowercase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = MegatronBertModel(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = MegatronBertForMaskedLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = MegatronBertForCausalLM(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForNextSentencePrediction(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = MegatronBertForPreTraining(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , next_sentence_label=lowerCAmelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple):
"""simple docstring"""
lowercase_ = MegatronBertForQuestionAnswering(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForSequenceClassification(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = MegatronBertForTokenClassification(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.num_choices
lowercase_ = MegatronBertForMultipleChoice(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
lowercase_ = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
# test_resize_embeddings = False
lowercase__ = False
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any]=False):
"""simple docstring"""
lowercase_ = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_)
if return_labels:
if model_class in get_values(lowerCAmelCase_):
lowercase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_)
lowercase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_)
return inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = MegatronBertModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=3_7)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return torch.tensor(
__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase , )
UpperCAmelCase : Any = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
@unittest.skip("""Model is not available.""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ = os.path.join(os.environ["""MYDIR"""] , lowerCAmelCase_)
lowercase_ = MegatronBertModel.from_pretrained(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.half()
lowercase_ = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]])
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)[0]
lowercase_ = torch.Size((1, 9, 1_0_2_4))
self.assertEqual(output.shape , lowerCAmelCase_)
lowercase_ = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3):
for jj in range(3):
lowercase_ = output[0, ii, jj]
lowercase_ = expected[3 * ii + jj]
lowercase_ = """ii={} jj={} a={} b={}""".format(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
self.assertTrue(math.isclose(lowerCAmelCase_ , lowerCAmelCase_ , rel_tol=lowerCAmelCase_ , abs_tol=lowerCAmelCase_) , msg=lowerCAmelCase_)
| 313
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"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = value
lowercase_ = None
lowercase_ = None
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , lowerCAmelCase_ : Node):
"""simple docstring"""
lowercase_ = tree
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Node | None):
"""simple docstring"""
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left) + self.depth_first_search(node.right)
)
def __iter__( self : Optional[int]):
"""simple docstring"""
yield self.depth_first_search(self.tree)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 355
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows != columns:
lowercase_ = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__lowerCAmelCase )
lowercase_ = np.zeros((rows, columns) )
lowercase_ = np.zeros((rows, columns) )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase_ = (table[i][j] - total) / upper[j][j]
lowercase_ = 1
for j in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = sum(lower[i][k] * upper[k][j] for k in range(__lowerCAmelCase ) )
lowercase_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
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|
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
lowercase_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
lowercase_ = 1
if upper_limit > 0:
lowercase_ = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(__lowerCAmelCase ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
UpperCAmelCase : Optional[Any] = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(F"The Catalan numbers from 0 through {N} are:")
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod()
| 356
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [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(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
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|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : Optional[int]=3_2 , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : Any=1_0 , lowerCAmelCase_ : Dict=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : Dict=[1, 1, 2, 1] , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Optional[int]="relu" , lowerCAmelCase_ : Union[str, Any]=3 , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = embeddings_size
lowercase_ = hidden_sizes
lowercase_ = depths
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = hidden_act
lowercase_ = num_labels
lowercase_ = scope
lowercase_ = len(lowerCAmelCase_)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.num_labels)
lowercase_ = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = TFResNetModel(config=lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = self.num_labels
lowercase_ = TFResNetForImageClassification(lowerCAmelCase_)
lowercase_ = model(lowerCAmelCase_ , labels=lowerCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
lowercase__ = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ = TFResNetModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_))
lowercase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_) , expected_num_stages + 1)
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ = layer_type
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_)
@slow
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = TFResNetModel.from_pretrained(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
'''simple docstring'''
lowercase_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
lowercase_ = self.default_image_processor
lowercase_ = prepare_img()
lowercase_ = image_processor(images=lowerCAmelCase_ , return_tensors="""tf""")
# forward pass
lowercase_ = model(**lowerCAmelCase_)
# verify the logits
lowercase_ = tf.TensorShape((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , lowerCAmelCase_)
lowercase_ = tf.constant([-11.1_069, -9.7_877, -8.3_777])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCAmelCase_ , atol=1E-4))
| 357
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , 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]
| 313
| 0
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = MODEL_FOR_CAUSAL_LM_MAPPING
lowercase__ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""pt""")
# Using `do_sample=False` to force deterministic output
lowercase_ = text_generator("""This is a test""" , do_sample=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
] , )
lowercase_ = text_generator(["""This is a test""", """This is a second test"""])
self.assertEqual(
lowerCAmelCase_ , [
[
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
""" oscope. oscope. FiliFili@@"""
)
}
],
] , )
lowercase_ = text_generator("""This is a test""" , do_sample=lowerCAmelCase_ , num_return_sequences=2 , return_tensors=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
] , )
lowercase_ = text_generator.model.config.eos_token_id
lowercase_ = """<pad>"""
lowercase_ = text_generator(
["""This is a test""", """This is a second test"""] , do_sample=lowerCAmelCase_ , num_return_sequences=2 , batch_size=2 , return_tensors=lowerCAmelCase_ , )
self.assertEqual(
lowerCAmelCase_ , [
[
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
],
[
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
{"""generated_token_ids""": ANY(lowerCAmelCase_)},
],
] , )
@require_tf
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""tf""")
# Using `do_sample=False` to force deterministic output
lowercase_ = text_generator("""This is a test""" , do_sample=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
] , )
lowercase_ = text_generator(["""This is a test""", """This is a second test"""] , do_sample=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
[
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
""" Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
)
}
],
] , )
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = TextGenerationPipeline(model=lowerCAmelCase_ , tokenizer=lowerCAmelCase_)
return text_generator, ["This is a test", "Another test"]
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = """Hello I believe in"""
lowercase_ = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""")
lowercase_ = text_generator(lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}] , )
lowercase_ = text_generator(lowerCAmelCase_ , stop_sequence=""" fe""")
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": """Hello I believe in fe"""}])
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Any):
"""simple docstring"""
lowercase_ = text_generator.model
lowercase_ = text_generator.tokenizer
lowercase_ = text_generator("""This is a test""")
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": ANY(lowerCAmelCase_)}])
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""))
lowercase_ = text_generator("""This is a test""" , return_full_text=lowerCAmelCase_)
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": ANY(lowerCAmelCase_)}])
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""])
lowercase_ = pipeline(task="""text-generation""" , model=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , return_full_text=lowerCAmelCase_)
lowercase_ = text_generator("""This is a test""")
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": ANY(lowerCAmelCase_)}])
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""])
lowercase_ = text_generator("""This is a test""" , return_full_text=lowerCAmelCase_)
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": ANY(lowerCAmelCase_)}])
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""))
lowercase_ = text_generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
[{"""generated_text""": ANY(lowerCAmelCase_)}, {"""generated_text""": ANY(lowerCAmelCase_)}],
[{"""generated_text""": ANY(lowerCAmelCase_)}, {"""generated_text""": ANY(lowerCAmelCase_)}],
] , )
if text_generator.tokenizer.pad_token is not None:
lowercase_ = text_generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=lowerCAmelCase_)
self.assertEqual(
lowerCAmelCase_ , [
[{"""generated_text""": ANY(lowerCAmelCase_)}, {"""generated_text""": ANY(lowerCAmelCase_)}],
[{"""generated_text""": ANY(lowerCAmelCase_)}, {"""generated_text""": ANY(lowerCAmelCase_)}],
] , )
with self.assertRaises(lowerCAmelCase_):
lowercase_ = text_generator("""test""" , return_full_text=lowerCAmelCase_ , return_text=lowerCAmelCase_)
with self.assertRaises(lowerCAmelCase_):
lowercase_ = text_generator("""test""" , return_full_text=lowerCAmelCase_ , return_tensors=lowerCAmelCase_)
with self.assertRaises(lowerCAmelCase_):
lowercase_ = text_generator("""test""" , return_text=lowerCAmelCase_ , return_tensors=lowerCAmelCase_)
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
lowercase_ = text_generator("""""")
self.assertEqual(lowerCAmelCase_ , [{"""generated_text""": ANY(lowerCAmelCase_)}])
else:
with self.assertRaises((ValueError, AssertionError)):
lowercase_ = text_generator("""""")
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
lowercase_ = ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
if (
tokenizer.model_max_length < 1_0_0_0_0
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
text_generator("""This is a test""" * 5_0_0 , max_new_tokens=2_0)
lowercase_ = text_generator("""This is a test""" * 5_0_0 , handle_long_generation="""hole""" , max_new_tokens=2_0)
# Hole strategy cannot work
with self.assertRaises(lowerCAmelCase_):
text_generator(
"""This is a test""" * 5_0_0 , handle_long_generation="""hole""" , max_new_tokens=tokenizer.model_max_length + 1_0 , )
@require_torch
@require_accelerate
@require_torch_gpu
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
import torch
# Classic `model_kwargs`
lowercase_ = pipeline(
model="""hf-internal-testing/tiny-random-bloom""" , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0))
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa)
lowercase_ = pipe("""This is a test""")
self.assertEqual(
lowerCAmelCase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
lowercase_ = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.bfloataa)
self.assertEqual(pipe.model.device , torch.device(0))
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa)
lowercase_ = pipe("""This is a test""")
self.assertEqual(
lowerCAmelCase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
lowercase_ = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""")
self.assertEqual(pipe.model.device , torch.device(0))
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa)
lowercase_ = pipe("""This is a test""")
self.assertEqual(
lowerCAmelCase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
@require_torch
@require_torch_gpu
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
import torch
lowercase_ = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device=0 , torch_dtype=torch.floataa)
pipe("""This is a test""")
@require_torch
@require_accelerate
@require_torch_gpu
def _UpperCAmelCase ( self : str):
"""simple docstring"""
import torch
lowercase_ = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.floataa)
pipe("""This is a test""" , do_sample=lowerCAmelCase_ , top_p=0.5)
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = """Hello world"""
lowercase_ = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""")
if text_generator.model.framework == "tf":
lowercase_ = logging.get_logger("""transformers.generation.tf_utils""")
else:
lowercase_ = logging.get_logger("""transformers.generation.utils""")
lowercase_ = """Both `max_new_tokens`""" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(lowerCAmelCase_) as cl:
lowercase_ = text_generator(lowerCAmelCase_ , max_length=1_0 , max_new_tokens=1)
self.assertIn(lowerCAmelCase_ , cl.out)
# The user only sets one -> no warning
with CaptureLogger(lowerCAmelCase_) as cl:
lowercase_ = text_generator(lowerCAmelCase_ , max_new_tokens=1)
self.assertNotIn(lowerCAmelCase_ , cl.out)
with CaptureLogger(lowerCAmelCase_) as cl:
lowercase_ = text_generator(lowerCAmelCase_ , max_length=1_0)
self.assertNotIn(lowerCAmelCase_ , cl.out)
| 358
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase : Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
lowercase_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int=1_3 , lowerCAmelCase_ : Optional[Any]=7 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Dict=9_9 , lowerCAmelCase_ : List[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : str=4 , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : List[Any]=0.02 , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = is_training
lowercase_ = use_labels
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = eos_token_id
lowercase_ = pad_token_id
lowercase_ = bos_token_id
lowercase_ = initializer_range
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowercase_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCAmelCase_ , )
lowercase_ = prepare_blenderbot_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
return config, inputs_dict
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""")
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]):
"""simple docstring"""
lowercase_ = 2_0
lowercase_ = model_class_name(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""])
lowercase_ , lowercase_ = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
lowercase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowercase_ = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase_ = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , past_key_values=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""")
lowercase_ = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase_ , decoder_position_ids=lowerCAmelCase_ , )
lowercase_ = model.decode(lowerCAmelCase_ , lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_)
lowercase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''')
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
lowercase__ = 99
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
lowercase_ = input_ids.shape[0]
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ = self._get_config_and_data()
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = lm_model(input_ids=lowerCAmelCase_)
lowercase_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
lowercase_ = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa)
lowercase_ = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa)
lowercase_ = lm_model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_)
lowercase_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCAmelCase_)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa)
lowercase_ = shift_tokens_right(lowerCAmelCase_ , 1 , 2)
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
lowercase_ = np.equal(lowerCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(lowerCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowercase__ = True
lowercase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = FlaxBlenderbotModelTester(self)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model_class(lowerCAmelCase_)
@jax.jit
def encode_jitted(lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : str):
return model.encode(input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_)
with self.subTest("""JIT Enabled"""):
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = encode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""])
lowercase_ = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any]):
return model.decode(
decoder_input_ids=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , encoder_outputs=lowerCAmelCase_ , )
with self.subTest("""JIT Enabled"""):
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
with self.subTest("""JIT Disabled"""):
with jax.disable_jit():
lowercase_ = decode_jitted(**lowerCAmelCase_).to_tuple()
self.assertEqual(len(lowerCAmelCase_) , len(lowerCAmelCase_))
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase_ = np.ones((1, 1)) * model.config.eos_token_id
lowercase_ = model(lowerCAmelCase_)
self.assertIsNotNone(lowerCAmelCase_)
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""")
@slow
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
lowercase_ = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
lowercase_ = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
lowercase_ = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCAmelCase_)
lowercase_ = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")
lowercase_ = ["""Sam"""]
lowercase_ = tokenizer(lowerCAmelCase_ , return_tensors="""jax""")
lowercase_ = model.generate(**lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = """Sam is a great name. It means \"sun\" in Gaelic."""
lowercase_ = tokenizer.batch_decode(lowerCAmelCase_ , **lowerCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 313
| 0
|
"""simple docstring"""
UpperCAmelCase : Optional[int] = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 359
|
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase : Dict = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase : Union[str, Any] = 10
UpperCAmelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[MinHash]:
'''simple docstring'''
if len(__lowerCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase_ = MinHash(num_perm=__lowerCAmelCase )
for token in set(__lowerCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(__lowerCAmelCase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , *,
lowerCAmelCase_ : float = 0.85 , ):
"""simple docstring"""
lowercase_ = duplication_jaccard_threshold
lowercase_ = NUM_PERM
lowercase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm)
lowercase_ = defaultdict(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : MinHash):
"""simple docstring"""
lowercase_ = self._index.query(lowerCAmelCase_)
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''')
return
self._index.insert(lowerCAmelCase_ , lowerCAmelCase_)
if len(lowerCAmelCase_) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(lowerCAmelCase_)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowercase_ = [base] + list(lowerCAmelCase_)
# reformat the cluster to be a list of dict
lowercase_ = [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
duplicate_clusters.append(lowerCAmelCase_)
return duplicate_clusters
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str]):
"""simple docstring"""
lowercase_ = self.get_duplicate_clusters()
with open(lowerCAmelCase_ , """w""") as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ = element
lowercase_ = get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__lowerCAmelCase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase ) ) , max_queue_size=1_00 ) ):
di.add(__lowerCAmelCase , __lowerCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase_ = get_tokens(__lowerCAmelCase )
lowercase_ = get_tokens(__lowerCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for elementa in cluster:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
for elementa in extremes:
lowercase_ = _shared_dataset[elementa["""base_index"""]]["""content"""]
if jaccard_similarity(__lowerCAmelCase , __lowerCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase_ = 1
extremes.append(__lowerCAmelCase )
return extremes
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
global _shared_dataset
lowercase_ = dataset
lowercase_ = []
lowercase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=__lowerCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__lowerCAmelCase , __lowerCAmelCase , ) , total=len(__lowerCAmelCase ) , ):
extremes_list.append(__lowerCAmelCase )
return extremes_list
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
lowercase_ = make_duplicate_clusters(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
lowercase_ = {}
lowercase_ = find_extremes(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase_ = element
lowercase_ = duplicate_indices - set(extreme_dict.keys() )
lowercase_ = dataset.filter(lambda __lowerCAmelCase , __lowerCAmelCase : idx not in remove_indices , with_indices=__lowerCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase_ = element["""base_index"""] in extreme_dict
if element["is_extreme"]:
lowercase_ = extreme_dict[element["""base_index"""]]["""copies"""]
print(F'''Original dataset size: {len(__lowerCAmelCase )}''' )
print(F'''Number of duplicate clusters: {len(__lowerCAmelCase )}''' )
print(F'''Files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase )}''' )
print(F'''Filtered dataset size: {len(__lowerCAmelCase )}''' )
return ds_filter, duplicate_clusters
| 313
| 0
|
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = 0 , __lowerCAmelCase = 0 ) -> list:
'''simple docstring'''
lowercase_ = end or len(__lowerCAmelCase )
for i in range(__lowerCAmelCase , __lowerCAmelCase ):
lowercase_ = i
lowercase_ = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
lowercase_ = array[temp_index - 1]
temp_index -= 1
lowercase_ = temp_index_value
return array
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> None: # Max Heap
'''simple docstring'''
lowercase_ = index
lowercase_ = 2 * index + 1 # Left Node
lowercase_ = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
lowercase_ = left_index
if right_index < heap_size and array[largest] < array[right_index]:
lowercase_ = right_index
if largest != index:
lowercase_ , lowercase_ = array[largest], array[index]
heapify(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
lowercase_ = len(__lowerCAmelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for i in range(n - 1 , 0 , -1 ):
lowercase_ , lowercase_ = array[0], array[i]
heapify(__lowerCAmelCase , 0 , __lowerCAmelCase )
return array
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = low
lowercase_ = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
lowercase_ , lowercase_ = array[j], array[i]
i += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list:
'''simple docstring'''
if len(__lowerCAmelCase ) == 0:
return array
lowercase_ = 2 * math.ceil(math.loga(len(__lowerCAmelCase ) ) )
lowercase_ = 16
return intro_sort(__lowerCAmelCase , 0 , len(__lowerCAmelCase ) , __lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(__lowerCAmelCase )
max_depth -= 1
lowercase_ = median_of_a(__lowerCAmelCase , __lowerCAmelCase , start + ((end - start) // 2) + 1 , end - 1 )
lowercase_ = partition(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
intro_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase_ = p
return insertion_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[Any] = input("Enter numbers separated by a comma : ").strip()
UpperCAmelCase : int = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| 360
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
UpperCAmelCase : Union[str, Any] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
lowercase_ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> PegasusForConditionalGeneration:
'''simple docstring'''
lowercase_ = DEFAULTS.copy()
cfg_kwargs.update(__lowerCAmelCase )
lowercase_ = PegasusConfig(**__lowerCAmelCase )
lowercase_ = PegasusForConditionalGeneration(__lowerCAmelCase )
lowercase_ = torch_model.model.state_dict()
lowercase_ = {}
for k, v in tf_weights.items():
lowercase_ = rename_state_dict_key(__lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
lowercase_ = v.T
lowercase_ = torch.tensor(__lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowercase_ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowercase_ = mapping["""shared.weight"""]
lowercase_ = mapping["""shared.weight"""]
lowercase_ = {k: torch.zeros_like(__lowerCAmelCase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowerCAmelCase )
lowercase_ , lowercase_ = torch_model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowercase_ = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
lowercase_ = tf.train.list_variables(__lowerCAmelCase )
lowercase_ = {}
lowercase_ = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowercase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase_ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = array
return tf_weights
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = Path(__lowerCAmelCase ).parent.name
lowercase_ = task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
lowercase_ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowerCAmelCase )
# convert model
lowercase_ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowercase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowercase_ = task_specific_params
lowercase_ = convert_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
lowercase_ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowerCAmelCase , Path(__lowerCAmelCase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase : List[Any] = parser.parse_args()
if args.save_dir is None:
UpperCAmelCase : List[str] = Path(args.tf_ckpt_path).parent.name
UpperCAmelCase : int = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 313
| 0
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : float , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : bool = False , ):
"""simple docstring"""
super().__init__()
lowercase_ = nn.Embedding(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = nn.Embedding(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = False
lowercase_ = nn.Dropout(p=lowerCAmelCase_)
lowercase_ = TaConfig(
vocab_size=lowerCAmelCase_ , d_model=lowerCAmelCase_ , num_heads=lowerCAmelCase_ , d_kv=lowerCAmelCase_ , d_ff=lowerCAmelCase_ , dropout_rate=lowerCAmelCase_ , feed_forward_proj=lowerCAmelCase_ , is_decoder=lowerCAmelCase_ , is_encoder_decoder=lowerCAmelCase_ , )
lowercase_ = nn.ModuleList()
for lyr_num in range(lowerCAmelCase_):
lowercase_ = TaBlock(lowerCAmelCase_)
self.encoders.append(lowerCAmelCase_)
lowercase_ = TaLayerNorm(lowerCAmelCase_)
lowercase_ = nn.Dropout(p=lowerCAmelCase_)
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
lowercase_ = self.token_embedder(lowerCAmelCase_)
lowercase_ = encoder_input_tokens.shape[1]
lowercase_ = torch.arange(lowerCAmelCase_ , device=encoder_input_tokens.device)
x += self.position_encoding(lowerCAmelCase_)
lowercase_ = self.dropout_pre(lowerCAmelCase_)
# inverted the attention mask
lowercase_ = encoder_input_tokens.size()
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_)
for lyr in self.encoders:
lowercase_ = lyr(lowerCAmelCase_ , lowerCAmelCase_)[0]
lowercase_ = self.layer_norm(lowerCAmelCase_)
return self.dropout_post(lowerCAmelCase_), encoder_inputs_mask
| 361
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowercase_ = factor + prime
while x in factor_map:
x += factor
lowercase_ = factor
else:
lowercase_ = prime
yield prime
prime += 1
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1E10 ) -> int:
'''simple docstring'''
lowercase_ = sieve()
lowercase_ = 1
while True:
lowercase_ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 313
| 0
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : str = "▁"
UpperCAmelCase : Dict = {"vocab_file": "sentencepiece.bpe.model"}
UpperCAmelCase : List[Any] = {
"vocab_file": {
"facebook/mbart-large-50-one-to-many-mmt": (
"https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
),
}
}
UpperCAmelCase : int = {
"facebook/mbart-large-50-one-to-many-mmt": 1024,
}
# fmt: off
UpperCAmelCase : Tuple = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = ["input_ids", "attention_mask"]
lowercase__ = []
lowercase__ = []
def __init__( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : str=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : Any="</s>" , lowerCAmelCase_ : Optional[int]="<s>" , lowerCAmelCase_ : int="<unk>" , lowerCAmelCase_ : List[Any]="<pad>" , lowerCAmelCase_ : Optional[int]="<mask>" , lowerCAmelCase_ : Optional[Dict[str, Any]] = None , **lowerCAmelCase_ : Dict , ):
"""simple docstring"""
lowercase_ = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_ , lowerCAmelCase_) else mask_token
lowercase_ = {} if sp_model_kwargs is None else sp_model_kwargs
lowercase_ = kwargs.get("""additional_special_tokens""" , [])
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=lowerCAmelCase_ , tgt_lang=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase_ , )
lowercase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(lowerCAmelCase_))
lowercase_ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase_ = {"""<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
lowercase_ = 1
lowercase_ = len(self.sp_model)
lowercase_ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase_)
}
lowercase_ = {v: k for k, v in self.lang_code_to_id.items()}
lowercase_ = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
lowercase_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
lowercase_ = src_lang if src_lang is not None else """en_XX"""
lowercase_ = self.lang_code_to_id[self._src_lang]
lowercase_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def __getstate__( self : List[str]):
"""simple docstring"""
lowercase_ = self.__dict__.copy()
lowercase_ = None
return state
def __setstate__( self : List[Any] , lowerCAmelCase_ : Dict):
"""simple docstring"""
lowercase_ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs"""):
lowercase_ = {}
lowercase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = {self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(lowerCAmelCase_ , out_type=lowerCAmelCase_)
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase_ = self.sp_model.PieceToId(lowerCAmelCase_)
# 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 _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : int):
"""simple docstring"""
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 _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = []
lowercase_ = """"""
lowercase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase_) + token
lowercase_ = True
lowercase_ = []
else:
current_sub_tokens.append(lowerCAmelCase_)
lowercase_ = False
out_string += self.sp_model.decode(lowerCAmelCase_)
return out_string.strip()
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase_):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
return
lowercase_ = os.path.join(
lowerCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowerCAmelCase_)
elif not os.path.isfile(self.vocab_file):
with open(lowerCAmelCase_ , """wb""") as fi:
lowercase_ = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase_)
return (out_vocab_file,)
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase_ , token_ids_a=lowerCAmelCase_ , already_has_special_tokens=lowerCAmelCase_)
lowercase_ = [1] * len(self.prefix_tokens)
lowercase_ = [1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCAmelCase_)) + suffix_ones
return prefix_ones + ([0] * len(lowerCAmelCase_)) + ([0] * len(lowerCAmelCase_)) + suffix_ones
def _UpperCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] , lowerCAmelCase_ : Optional[str] , **lowerCAmelCase_ : Union[str, Any]):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
lowercase_ = src_lang
lowercase_ = self(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_)
lowercase_ = self.convert_tokens_to_ids(lowerCAmelCase_)
lowercase_ = tgt_lang_id
return inputs
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str = "en_XX" , lowerCAmelCase_ : Optional[List[str]] = None , lowerCAmelCase_ : str = "ro_RO" , **lowerCAmelCase_ : int , ):
"""simple docstring"""
lowercase_ = src_lang
lowercase_ = tgt_lang
return super().prepare_seqaseq_batch(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang)
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def _UpperCAmelCase ( self : Any , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = self.lang_code_to_id[src_lang]
lowercase_ = [self.cur_lang_code_id]
lowercase_ = [self.eos_token_id]
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = self.lang_code_to_id[tgt_lang]
lowercase_ = [self.cur_lang_code_id]
lowercase_ = [self.eos_token_id]
| 363
|
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase_ = 0 if allow_empty_subarrays else float("""-inf""" )
lowercase_ = 0.0
for num in arr:
lowercase_ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowercase_ = max(__lowerCAmelCase , __lowerCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : Union[str, Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 313
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|
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]="resnet50" , lowerCAmelCase_ : List[str]=3 , lowerCAmelCase_ : List[Any]=3_2 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : int=True , lowerCAmelCase_ : str=True , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = out_indices if out_indices is not None else [4]
lowercase_ = stage_names
lowercase_ = out_features
lowercase_ = backbone
lowercase_ = batch_size
lowercase_ = image_size
lowercase_ = num_channels
lowercase_ = use_pretrained_backbone
lowercase_ = is_training
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase_ = self.get_config()
return config, pixel_values
def _UpperCAmelCase ( self : int):
"""simple docstring"""
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[int]):
"""simple docstring"""
lowercase_ = TimmBackbone(config=lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
with torch.no_grad():
lowercase_ = model(lowerCAmelCase_)
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 1_4, 1_4) , )
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = (TimmBackbone,) if is_torch_available() else ()
lowercase__ = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = TimmBackboneModelTester(self)
lowercase_ = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """resnet18"""
lowercase_ = """microsoft/resnet-18"""
lowercase_ = AutoBackbone.from_pretrained(lowerCAmelCase_ , use_timm_backbone=lowerCAmelCase_)
lowercase_ = AutoBackbone.from_pretrained(lowerCAmelCase_)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(len(timm_model.stage_names) , len(transformers_model.stage_names))
self.assertEqual(timm_model.channels , transformers_model.channels)
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,))
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names) - 1])
lowercase_ = AutoBackbone.from_pretrained(lowerCAmelCase_ , use_timm_backbone=lowerCAmelCase_ , out_indices=[1, 2, 3])
lowercase_ = AutoBackbone.from_pretrained(lowerCAmelCase_ , out_indices=[1, 2, 3])
self.assertEqual(timm_model.out_indices , transformers_model.out_indices)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(timm_model.channels , transformers_model.channels)
@unittest.skip("""TimmBackbone doesn't support feed forward chunking""")
def _UpperCAmelCase ( self : int):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone initialization is managed on the timm side""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
def _UpperCAmelCase ( self : str):
"""simple docstring"""
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""")
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
pass
@unittest.skip("""TimmBackbone doesn't support output_attentions.""")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
pass
@unittest.skip("""Safetensors is not supported by timm.""")
def _UpperCAmelCase ( self : List[str]):
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
lowercase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ = True
lowercase_ = self.has_attentions
# no need to test all models as different heads yield the same functionality
lowercase_ = self.all_model_classes[0]
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
lowercase_ = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = model(**lowerCAmelCase_)
lowercase_ = outputs[0][-1]
# Encoder-/Decoder-only models
lowercase_ = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
lowercase_ = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowerCAmelCase_)
self.assertIsNotNone(hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(attentions.grad)
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(**lowerCAmelCase_)
self.assertEqual(len(result.feature_maps) , len(config.out_indices))
self.assertEqual(len(model.channels) , len(config.out_indices))
# Check output of last stage is taken if out_features=None, out_indices=None
lowercase_ = copy.deepcopy(lowerCAmelCase_)
lowercase_ = None
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(**lowerCAmelCase_)
self.assertEqual(len(result.feature_maps) , 1)
self.assertEqual(len(model.channels) , 1)
# Check backbone can be initialized with fresh weights
lowercase_ = copy.deepcopy(lowerCAmelCase_)
lowercase_ = False
lowercase_ = model_class(lowerCAmelCase_)
model.to(lowerCAmelCase_)
model.eval()
lowercase_ = model(**lowerCAmelCase_)
| 364
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 313
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