code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=50 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , ):
__lowerCAmelCase : Union[str, Any] = parent
__lowerCAmelCase : Optional[int] = batch_size
__lowerCAmelCase : Optional[Any] = seq_length
__lowerCAmelCase : Union[str, Any] = is_training
__lowerCAmelCase : Dict = use_input_mask
__lowerCAmelCase : Tuple = vocab_size
__lowerCAmelCase : Tuple = hidden_size
__lowerCAmelCase : int = num_hidden_layers
__lowerCAmelCase : Tuple = num_attention_heads
__lowerCAmelCase : Union[str, Any] = intermediate_size
__lowerCAmelCase : Any = hidden_act
__lowerCAmelCase : List[str] = hidden_dropout_prob
__lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
__lowerCAmelCase : List[str] = max_position_embeddings
__lowerCAmelCase : List[Any] = initializer_range
__lowerCAmelCase : int = use_labels
__lowerCAmelCase : List[str] = scope
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
__lowerCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
__lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Any = self.get_config()
return config, input_ids, input_mask, token_labels
def __lowerCamelCase ( self ):
return BertGenerationConfig(
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 , is_decoder=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
def __lowerCamelCase ( self ):
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : str = self.prepare_config_and_inputs()
__lowerCAmelCase : Union[str, Any] = True
__lowerCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Union[str, Any] = BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : str = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : List[Any] = True
__lowerCAmelCase : Union[str, Any] = BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Optional[int] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Dict = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : Dict = True
__lowerCAmelCase : str = BertGenerationDecoder(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
# first forward pass
__lowerCAmelCase : Union[str, Any] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCAmelCase : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCAmelCase : List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowerCAmelCase : Union[str, Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCAmelCase : Optional[int] = torch.cat([input_mask, next_mask] , dim=-1 )
__lowerCAmelCase : List[str] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )['hidden_states'][0]
__lowerCAmelCase : Union[str, Any] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )['hidden_states'][0]
# select random slice
__lowerCAmelCase : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCAmelCase : Tuple = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Tuple = BertGenerationDecoder(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Dict = self.prepare_config_and_inputs()
__lowerCAmelCase : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase):
A_ : str = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
A_ : Any = (BertGenerationDecoder,) if is_torch_available() else ()
A_ : Any = (
{'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
if is_torch_available()
else {}
)
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = BertGenerationEncoderTester(self )
__lowerCAmelCase : List[Any] = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
__lowerCAmelCase : str = 'bert'
self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
# This regression test was failing with PyTorch < 1.3
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
__lowerCAmelCase : Dict = None
self.model_tester.create_and_check_model_as_decoder(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_torch
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
__lowerCAmelCase : Dict = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
__lowerCAmelCase : int = model(_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Union[str, Any] = torch.Size([1, 8, 10_24] )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
@require_torch
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[Any] = BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
__lowerCAmelCase : str = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
__lowerCAmelCase : Tuple = model(_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Optional[int] = torch.Size([1, 8, 5_03_58] )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) ) | 86 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=5_12 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , ):
__lowerCAmelCase : Tuple = parent
__lowerCAmelCase : Optional[int] = 13
__lowerCAmelCase : List[Any] = 7
__lowerCAmelCase : int = True
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : List[Any] = True
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : Optional[Any] = 99
__lowerCAmelCase : int = 3_84
__lowerCAmelCase : Union[str, Any] = 2
__lowerCAmelCase : Tuple = 4
__lowerCAmelCase : str = 37
__lowerCAmelCase : Any = 'gelu'
__lowerCAmelCase : List[str] = 0.1
__lowerCAmelCase : Any = 0.1
__lowerCAmelCase : Union[str, Any] = 5_12
__lowerCAmelCase : int = 16
__lowerCAmelCase : Union[str, Any] = 2
__lowerCAmelCase : int = 0.02
__lowerCAmelCase : Dict = 3
__lowerCAmelCase : Tuple = 4
__lowerCAmelCase : Tuple = 1_28
__lowerCAmelCase : Optional[int] = 2
__lowerCAmelCase : List[str] = 9
__lowerCAmelCase : int = 1
__lowerCAmelCase : int = None
def __lowerCamelCase ( self ):
__lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Optional[int] = None
if self.use_input_mask:
__lowerCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Tuple = None
if self.use_token_type_ids:
__lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase : Optional[Any] = None
__lowerCAmelCase : Dict = None
__lowerCAmelCase : Union[str, Any] = None
if self.use_labels:
__lowerCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase : Union[str, Any] = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=_SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = TFConvBertModel(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__lowerCAmelCase : Tuple = [input_ids, input_mask]
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = TFConvBertForMaskedLM(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = self.num_labels
__lowerCAmelCase : Optional[Any] = TFConvBertForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : int = self.num_choices
__lowerCAmelCase : List[str] = TFConvBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Dict = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Union[str, Any] = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Tuple = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
__lowerCAmelCase : str = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = self.num_labels
__lowerCAmelCase : Any = TFConvBertForTokenClassification(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = TFConvBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : List[str] = config_and_inputs
__lowerCAmelCase : Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class A__ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase):
A_ : List[str] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
A_ : str = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
A_ : List[Any] = False
A_ : str = False
A_ : List[Any] = False
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = TFConvBertModelTester(self )
__lowerCAmelCase : Any = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Any = True
__lowerCAmelCase : Dict = True
if hasattr(_SCREAMING_SNAKE_CASE , 'use_cache' ):
__lowerCAmelCase : int = True
__lowerCAmelCase : List[str] = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : str = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__lowerCAmelCase : str = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = len(model(_SCREAMING_SNAKE_CASE ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE , saved_model=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = os.path.join(_SCREAMING_SNAKE_CASE , 'saved_model' , '1' )
__lowerCAmelCase : int = tf.keras.models.load_model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
__lowerCAmelCase : List[str] = outputs['encoder_hidden_states']
__lowerCAmelCase : Tuple = outputs['encoder_attentions']
else:
__lowerCAmelCase : Optional[int] = outputs['hidden_states']
__lowerCAmelCase : Tuple = outputs['attentions']
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : List[Any] = getattr(self.model_tester , 'decoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : str = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : Tuple = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
def check_decoder_attentions_output(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(out_len % 2 , 0 )
__lowerCAmelCase : Optional[Any] = outputs.decoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__lowerCAmelCase : List[str] = True
__lowerCAmelCase : Optional[int] = False
__lowerCAmelCase : List[Any] = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Tuple = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
__lowerCAmelCase : Dict = True
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : List[Any] = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
@require_tf
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
__lowerCAmelCase : int = tf.constant([[0, 1, 2, 3, 4, 5]] )
__lowerCAmelCase : Tuple = model(_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Tuple = [1, 6, 7_68]
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) | 86 | 1 |
from collections.abc import Sequence
from queue import Queue
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None ) -> Optional[Any]:
UpperCamelCase : int = start
UpperCamelCase : int = end
UpperCamelCase : str = val
UpperCamelCase : Dict = (start + end) // 2
UpperCamelCase : List[str] = left
UpperCamelCase : List[Any] = right
def __repr__( self ) -> List[Any]:
return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> str:
UpperCamelCase : Dict = collection
UpperCamelCase : int = function
if self.collection:
UpperCamelCase : List[str] = self._build_tree(0, len(lowercase_ ) - 1 )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
self._update_tree(self.root, lowercase_, lowercase_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
return self._query_range(self.root, lowercase_, lowercase_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
if start == end:
return SegmentTreeNode(lowercase_, lowercase_, self.collection[start] )
UpperCamelCase : Dict = (start + end) // 2
UpperCamelCase : str = self._build_tree(lowercase_, lowercase_ )
UpperCamelCase : Dict = self._build_tree(mid + 1, lowercase_ )
return SegmentTreeNode(lowercase_, lowercase_, self.fn(left.val, right.val ), lowercase_, lowercase_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Tuple:
if node.start == i and node.end == i:
UpperCamelCase : List[Any] = val
return
if i <= node.mid:
self._update_tree(node.left, lowercase_, lowercase_ )
else:
self._update_tree(node.right, lowercase_, lowercase_ )
UpperCamelCase : int = self.fn(node.left.val, node.right.val )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left, lowercase_, lowercase_ )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left, lowercase_, node.mid ), self._query_range(node.right, node.mid + 1, lowercase_ ), )
else:
# range in right child tree
return self._query_range(node.right, lowercase_, lowercase_ )
def snake_case_ ( self ) -> Any:
if self.root is not None:
UpperCamelCase : int = Queue()
queue.put(self.root )
while not queue.empty():
UpperCamelCase : int = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
__UpperCAmelCase = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 358 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=13, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=224, SCREAMING_SNAKE_CASE_=30, SCREAMING_SNAKE_CASE_=400, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5], SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5], ) -> List[str]:
UpperCamelCase : Optional[int] = size if size is not None else {'height': 18, 'width': 18}
UpperCamelCase : List[Any] = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : int = num_channels
UpperCamelCase : int = image_size
UpperCamelCase : List[Any] = min_resolution
UpperCamelCase : int = max_resolution
UpperCamelCase : Any = do_resize
UpperCamelCase : Optional[int] = size
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : Optional[Any] = image_mean
UpperCamelCase : Tuple = image_std
def snake_case_ ( self ) -> List[Any]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : Optional[Any] = ViTImageProcessor if is_vision_available() else None
def snake_case_ ( self ) -> Any:
UpperCamelCase : Dict = EfficientFormerImageProcessorTester(self )
@property
def snake_case_ ( self ) -> List[Any]:
return self.image_proc_tester.prepare_image_processor_dict()
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'size' ) )
def snake_case_ ( self ) -> Any:
pass
def snake_case_ ( self ) -> int:
# Initialize image_processor
UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : List[str] = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, Image.Image )
# Test not batched input
UpperCamelCase : str = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : Optional[Any] = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
def snake_case_ ( self ) -> str:
# Initialize image_processor
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_, numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, np.ndarray )
# Test not batched input
UpperCamelCase : Dict = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : Dict = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
def snake_case_ ( self ) -> Tuple:
# Initialize image_processor
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : int = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_, torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, torch.Tensor )
# Test not batched input
UpperCamelCase : Optional[int] = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : int = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
| 103 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case__ : List[str] = datasets.load_iris()
snake_case__ : Union[str, Any] = np.array(data['''data'''])
snake_case__ : Optional[Any] = np.array(data['''target'''])
snake_case__ : Optional[Any] = data['''target_names''']
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = train_test_split(X, y)
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : Dict ):
return np.linalg.norm(np.array(_snake_case ) - np.array(_snake_case ) )
def _snake_case ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Dict=5 ):
lowerCAmelCase : Optional[Any] = zip(_snake_case , _snake_case )
# List of distances of all points from the point to be classified
lowerCAmelCase : Union[str, Any] = []
for data_point in data:
lowerCAmelCase : Any = euclidean_distance(data_point[0] , _snake_case )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
lowerCAmelCase : str = [i[1] for i in sorted(_snake_case )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
lowerCAmelCase : Optional[Any] = Counter(_snake_case ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 60 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return x + 2
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Any ) -> Any:
__SCREAMING_SNAKE_CASE = "x = 3"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3} )
__SCREAMING_SNAKE_CASE = "x = y"
__SCREAMING_SNAKE_CASE = {"y": 5}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 5, "y": 5} )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
__SCREAMING_SNAKE_CASE = "y = add_two(x)"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result is None
assert "tried to execute add_two" in out.out
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
__SCREAMING_SNAKE_CASE = "x = 3"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3} )
def UpperCAmelCase_ ( self : str ) -> Any:
__SCREAMING_SNAKE_CASE = "test_dict = {'x': x, 'y': add_two(x)}"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = "x = 3\ny = 5"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
def UpperCAmelCase_ ( self : Any ) -> Any:
__SCREAMING_SNAKE_CASE = "text = f'This is x: {x}.'"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "text": "This is x: 3."} )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = "if x <= 3:\n y = 2\nelse:\n y = 5"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 2} )
__SCREAMING_SNAKE_CASE = {"x": 8}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 8, "y": 5} )
def UpperCAmelCase_ ( self : Tuple ) -> str:
__SCREAMING_SNAKE_CASE = "test_list = [x, add_two(x)]"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , [3, 5] )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_list": [3, 5]} )
def UpperCAmelCase_ ( self : Any ) -> int:
__SCREAMING_SNAKE_CASE = "y = x"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 3} )
def UpperCAmelCase_ ( self : Tuple ) -> int:
__SCREAMING_SNAKE_CASE = "test_list = [x, add_two(x)]\ntest_list[1]"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_list": [3, 5]} )
__SCREAMING_SNAKE_CASE = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
__SCREAMING_SNAKE_CASE = "x = 0\nfor i in range(3):\n x = i"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"range": range} , state=UpperCAmelCase__ )
assert result == 2
self.assertDictEqual(UpperCAmelCase__ , {"x": 2, "i": 2} )
| 54 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowercase__ ( unittest.TestCase ):
def __init__( self : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any]=7 , UpperCAmelCase_ : List[Any]=3 , UpperCAmelCase_ : List[Any]=18 , UpperCAmelCase_ : List[Any]=30 , UpperCAmelCase_ : Any=400 , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=True , ):
SCREAMING_SNAKE_CASE__ = size if size is not None else {'height': 18, 'width': 18}
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = min_resolution
SCREAMING_SNAKE_CASE__ = max_resolution
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = apply_ocr
def A_ ( self : str ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class lowercase__ ( _UpperCAmelCase , unittest.TestCase ):
A__ : str =LayoutLMvaImageProcessor if is_pytesseract_available() else None
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = LayoutLMvaImageProcessingTester(self )
@property
def A_ ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ , 'do_resize' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'size' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'apply_ocr' ) )
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def A_ ( self : Dict ):
pass
def A_ ( self : int ):
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , UpperCAmelCase_ )
self.assertIsInstance(encoding.boxes , UpperCAmelCase_ )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(UpperCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def A_ ( self : Tuple ):
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(UpperCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def A_ ( self : int ):
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(UpperCAmelCase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def A_ ( self : Dict ):
# with apply_OCR = True
SCREAMING_SNAKE_CASE__ = LayoutLMvaImageProcessor()
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
SCREAMING_SNAKE_CASE__ = Image.open(ds[0]['file'] ).convert('RGB' )
SCREAMING_SNAKE_CASE__ = image_processing(UpperCAmelCase_ , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
SCREAMING_SNAKE_CASE__ = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
SCREAMING_SNAKE_CASE__ = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , UpperCAmelCase_ )
self.assertListEqual(encoding.boxes , UpperCAmelCase_ )
# with apply_OCR = False
SCREAMING_SNAKE_CASE__ = LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = image_processing(UpperCAmelCase_ , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 364 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ) -> Optional[Any]:
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
SCREAMING_SNAKE_CASE__ = nn.Parameter(UpperCamelCase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
SCREAMING_SNAKE_CASE__ = nn.Parameter(UpperCamelCase_ )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(UpperCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(UpperCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(UpperCamelCase_ ).view(-1 , UpperCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[2] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(UpperCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCamelCase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(UpperCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(UpperCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(UpperCamelCase_ ).view(-1 , UpperCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = weights[0][0][0]
SCREAMING_SNAKE_CASE__ = np.asarray(layer_norm_a[0] )
SCREAMING_SNAKE_CASE__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(UpperCamelCase_ ) , torch.tensor(UpperCamelCase_ ) , )
# lsh weights + output
SCREAMING_SNAKE_CASE__ = weights[0][1]
if len(UpperCamelCase_ ) < 4:
set_layer_weights_in_torch_lsh(UpperCamelCase_ , torch_block.attention , UpperCamelCase_ )
else:
set_layer_weights_in_torch_local(UpperCamelCase_ , torch_block.attention , UpperCamelCase_ )
# intermediate weighs
SCREAMING_SNAKE_CASE__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(UpperCamelCase_ ) == 4:
SCREAMING_SNAKE_CASE__ = intermediate_weights[2]
# layernorm 2
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[0][0] )
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(UpperCamelCase_ ) , torch.tensor(UpperCamelCase_ ) , )
# intermediate dense
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[1][0] )
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(UpperCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCamelCase_ ) , )
# intermediate out
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[4][0] )
SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(UpperCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCamelCase_ ) , )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = torch_model.reformer
# word embeds
SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(UpperCamelCase_ ) , )
if isinstance(weights[3] , UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
SCREAMING_SNAKE_CASE__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'{position_embeddings[emb_idx]} emb does not match'
SCREAMING_SNAKE_CASE__ = nn.Parameter(torch.tensor(UpperCamelCase_ ) )
SCREAMING_SNAKE_CASE__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
UpperCamelCase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
SCREAMING_SNAKE_CASE__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# output layer norm
SCREAMING_SNAKE_CASE__ = np.asarray(weights[7][0] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(UpperCamelCase_ ) , torch.tensor(UpperCamelCase_ ) , )
# output embeddings
SCREAMING_SNAKE_CASE__ = np.asarray(weights[9][0] )
SCREAMING_SNAKE_CASE__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(UpperCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCamelCase_ ) , )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ReformerConfig.from_json_file(UpperCamelCase_ )
print(F'Building PyTorch model from configuration: {config}' )
SCREAMING_SNAKE_CASE__ = ReformerModelWithLMHead(UpperCamelCase_ )
with open(UpperCamelCase_ , 'rb' ) as f:
SCREAMING_SNAKE_CASE__ = pickle.load(UpperCamelCase_ )['weights']
set_model_weights_in_torch(UpperCamelCase_ , UpperCamelCase_ , config.hidden_size )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , UpperCamelCase_ )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__snake_case = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 169 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase_ ( _lowerCamelCase : Any):
lowercase__ : Any = args.pruning_method
lowercase__ : Union[str, Any] = args.threshold
lowercase__ : Optional[int] = args.model_name_or_path.rstrip("/")
lowercase__ : Union[str, Any] = args.target_model_path
print(f'''Load fine-pruned model from {model_name_or_path}''')
lowercase__ : str = torch.load(os.path.join(lowerCAmelCase_ , "pytorch_model.bin"))
lowercase__ : Optional[int] = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
lowercase__ : Optional[Any] = tensor
print(f'''Copied layer {name}''')
elif "classifier" in name or "qa_output" in name:
lowercase__ : Union[str, Any] = tensor
print(f'''Copied layer {name}''')
elif "bias" in name:
lowercase__ : List[str] = tensor
print(f'''Copied layer {name}''')
else:
if pruning_method == "magnitude":
lowercase__ : Union[str, Any] = MagnitudeBinarizer.apply(inputs=lowerCAmelCase_ , threshold=lowerCAmelCase_)
lowercase__ : Tuple = tensor * mask
print(f'''Pruned layer {name}''')
elif pruning_method == "topK":
if "mask_scores" in name:
continue
lowercase__ : Dict = name[:-6]
lowercase__ : str = model[f'''{prefix_}mask_scores''']
lowercase__ : str = TopKBinarizer.apply(lowerCAmelCase_ , lowerCAmelCase_)
lowercase__ : Any = tensor * mask
print(f'''Pruned layer {name}''')
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
lowercase__ : Optional[int] = name[:-6]
lowercase__ : str = model[f'''{prefix_}mask_scores''']
lowercase__ : Optional[int] = ThresholdBinarizer.apply(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
lowercase__ : str = tensor * mask
print(f'''Pruned layer {name}''')
elif pruning_method == "l0":
if "mask_scores" in name:
continue
lowercase__ : int = name[:-6]
lowercase__ : List[str] = model[f'''{prefix_}mask_scores''']
lowercase__ , lowercase__ : List[Any] = -0.1, 1.1
lowercase__ : Dict = torch.sigmoid(lowerCAmelCase_)
lowercase__ : Union[str, Any] = s * (r - l) + l
lowercase__ : int = s_bar.clamp(min=0.0 , max=1.0)
lowercase__ : Optional[Any] = tensor * mask
print(f'''Pruned layer {name}''')
else:
raise ValueError("Unknown pruning method")
if target_model_path is None:
lowercase__ : List[Any] = os.path.join(
os.path.dirname(lowerCAmelCase_) , f'''bertarized_{os.path.basename(lowerCAmelCase_)}''')
if not os.path.isdir(lowerCAmelCase_):
shutil.copytree(lowerCAmelCase_ , lowerCAmelCase_)
print(f'''\nCreated folder {target_model_path}''')
torch.save(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , "pytorch_model.bin"))
print("\nPruned model saved! See you later!")
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--pruning_method''',
choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
type=str,
required=True,
help=(
'''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
''' sigmoied_threshold = Soft movement pruning)'''
),
)
parser.add_argument(
'''--threshold''',
type=float,
required=False,
help=(
'''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
'''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
'''Not needed for `l0`'''
),
)
parser.add_argument(
'''--model_name_or_path''',
type=str,
required=True,
help='''Folder containing the model that was previously fine-pruned''',
)
parser.add_argument(
'''--target_model_path''',
default=None,
type=str,
required=False,
help='''Folder containing the model that was previously fine-pruned''',
)
UpperCamelCase = parser.parse_args()
main(args)
| 87 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
def _lowerCAmelCase ( lowerCAmelCase_ :List[Any] )->int:
'''simple docstring'''
print("Loading config file..." )
def flatten_yaml_as_dict(lowerCAmelCase_ :List[Any] , lowerCAmelCase_ :Optional[int]="" , lowerCAmelCase_ :int="." ):
snake_case_ = []
for k, v in d.items():
snake_case_ = parent_key + sep + k if parent_key else k
if isinstance(lowerCAmelCase_ , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(lowerCAmelCase_ , lowerCAmelCase_ , sep=lowerCAmelCase_ ).items() )
else:
items.append((new_key, v) )
return dict(lowerCAmelCase_ )
snake_case_ = argparse.Namespace()
with open(lowerCAmelCase_ , "r" ) as yaml_file:
try:
snake_case_ = yaml.load(lowerCAmelCase_ , Loader=yaml.FullLoader )
snake_case_ = flatten_yaml_as_dict(lowerCAmelCase_ )
for k, v in flat_cfg.items():
setattr(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
except yaml.YAMLError as exc:
logger.error("Error while loading config file: {}. Error message: {}".format(lowerCAmelCase_ , str(lowerCAmelCase_ ) ) )
return config
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] , lowerCAmelCase_ :Tuple )->Union[str, Any]:
'''simple docstring'''
snake_case_ = MobileViTVaConfig()
snake_case_ = False
# dataset
if task_name.startswith("imagenet1k_" ):
snake_case_ = 1_000
if int(task_name.strip().split("_" )[-1] ) == 384:
snake_case_ = 384
else:
snake_case_ = 256
snake_case_ = "imagenet-1k-id2label.json"
elif task_name.startswith("imagenet21k_to_1k_" ):
snake_case_ = 21_000
if int(task_name.strip().split("_" )[-1] ) == 384:
snake_case_ = 384
else:
snake_case_ = 256
snake_case_ = "imagenet-22k-id2label.json"
elif task_name.startswith("ade20k_" ):
snake_case_ = 151
snake_case_ = 512
snake_case_ = "ade20k-id2label.json"
snake_case_ = True
elif task_name.startswith("voc_" ):
snake_case_ = 21
snake_case_ = 512
snake_case_ = "pascal-voc-id2label.json"
snake_case_ = True
# orig_config
snake_case_ = load_orig_config_file(lowerCAmelCase_ )
assert getattr(lowerCAmelCase_ , "model.classification.name" , -1 ) == "mobilevit_v2", "Invalid model"
snake_case_ = getattr(lowerCAmelCase_ , "model.classification.mitv2.width_multiplier" , 1.0 )
assert (
getattr(lowerCAmelCase_ , "model.classification.mitv2.attn_norm_layer" , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
snake_case_ = getattr(lowerCAmelCase_ , "model.classification.activation.name" , "swish" )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
snake_case_ = getattr(lowerCAmelCase_ , "model.segmentation.output_stride" , 16 )
if "_deeplabv3" in task_name:
snake_case_ = getattr(lowerCAmelCase_ , "model.segmentation.deeplabv3.aspp_rates" , [12, 24, 36] )
snake_case_ = getattr(lowerCAmelCase_ , "model.segmentation.deeplabv3.aspp_out_channels" , 512 )
snake_case_ = getattr(lowerCAmelCase_ , "model.segmentation.deeplabv3.aspp_dropout" , 0.1 )
# id2label
snake_case_ = "huggingface/label-files"
snake_case_ = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="dataset" ) , "r" ) )
snake_case_ = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowerCAmelCase_ :Any , lowerCAmelCase_ :Optional[Any] , lowerCAmelCase_ :Optional[Any] )->Optional[Any]:
'''simple docstring'''
snake_case_ = dct.pop(lowerCAmelCase_ )
snake_case_ = val
def _lowerCAmelCase ( lowerCAmelCase_ :Dict , lowerCAmelCase_ :int=False )->Dict:
'''simple docstring'''
if base_model:
snake_case_ = ""
else:
snake_case_ = "mobilevitv2."
snake_case_ = []
for k in state_dict.keys():
if k[:8] == "encoder.":
snake_case_ = k[8:]
else:
snake_case_ = k
if ".block." in k:
snake_case_ = k_new.replace(".block." , "." )
if ".conv." in k:
snake_case_ = k_new.replace(".conv." , ".convolution." )
if ".norm." in k:
snake_case_ = k_new.replace(".norm." , ".normalization." )
if "conv_1." in k:
snake_case_ = k_new.replace("conv_1." , F'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if F'''layer_{i}.''' in k:
snake_case_ = k_new.replace(F'''layer_{i}.''' , F'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
snake_case_ = k_new.replace(".exp_1x1." , ".expand_1x1." )
if ".red_1x1." in k:
snake_case_ = k_new.replace(".red_1x1." , ".reduce_1x1." )
for i in [3, 4, 5]:
if F'''layer_{i}.0.''' in k:
snake_case_ = k_new.replace(F'''layer_{i}.0.''' , F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if F'''layer_{i}.1.local_rep.0.''' in k:
snake_case_ = k_new.replace(F'''layer_{i}.1.local_rep.0.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if F'''layer_{i}.1.local_rep.1.''' in k:
snake_case_ = k_new.replace(F'''layer_{i}.1.local_rep.1.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
snake_case_ = [0, 1]
elif i == 4:
snake_case_ = [0, 1, 2, 3]
elif i == 5:
snake_case_ = [0, 1, 2]
for j in j_in:
if F'''layer_{i}.1.global_rep.{j}.''' in k:
snake_case_ = k_new.replace(
F'''layer_{i}.1.global_rep.{j}.''' , F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
snake_case_ = k_new.replace(
F'''layer_{i}.1.global_rep.{j+1}.''' , F'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if F'''layer_{i}.1.conv_proj.''' in k:
snake_case_ = k_new.replace(F'''layer_{i}.1.conv_proj.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
snake_case_ = k_new.replace("pre_norm_attn.0." , "layernorm_before." )
if "pre_norm_attn.1." in k:
snake_case_ = k_new.replace("pre_norm_attn.1." , "attention." )
if "pre_norm_ffn.0." in k:
snake_case_ = k_new.replace("pre_norm_ffn.0." , "layernorm_after." )
if "pre_norm_ffn.1." in k:
snake_case_ = k_new.replace("pre_norm_ffn.1." , "ffn.conv1." )
if "pre_norm_ffn.3." in k:
snake_case_ = k_new.replace("pre_norm_ffn.3." , "ffn.conv2." )
if "classifier.1." in k:
snake_case_ = k_new.replace("classifier.1." , "classifier." )
if "seg_head." in k:
snake_case_ = k_new.replace("seg_head." , "segmentation_head." )
if ".aspp_layer." in k:
snake_case_ = k_new.replace(".aspp_layer." , "." )
if ".aspp_pool." in k:
snake_case_ = k_new.replace(".aspp_pool." , "." )
rename_keys.append((k, k_new) )
return rename_keys
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[Any] )->Optional[int]:
'''simple docstring'''
snake_case_ = []
for k in state_dict.keys():
if k.startswith("seg_head.aux_head." ):
keys_to_ignore.append(lowerCAmelCase_ )
for k in keys_to_ignore:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
def _lowerCAmelCase ( )->List[Any]:
'''simple docstring'''
snake_case_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
snake_case_ = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :Dict , lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :Dict )->Dict:
'''simple docstring'''
snake_case_ = get_mobilevitva_config(lowerCAmelCase_ , lowerCAmelCase_ )
# load original state_dict
snake_case_ = torch.load(lowerCAmelCase_ , map_location="cpu" )
# load huggingface model
if task_name.startswith("ade20k_" ) or task_name.startswith("voc_" ):
snake_case_ = MobileViTVaForSemanticSegmentation(lowerCAmelCase_ ).eval()
snake_case_ = False
else:
snake_case_ = MobileViTVaForImageClassification(lowerCAmelCase_ ).eval()
snake_case_ = False
# remove and rename some keys of load the original model
snake_case_ = checkpoint
remove_unused_keys(lowerCAmelCase_ )
snake_case_ = create_rename_keys(lowerCAmelCase_ , base_model=lowerCAmelCase_ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# load modified state_dict
model.load_state_dict(lowerCAmelCase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
snake_case_ = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
snake_case_ = image_processor(images=prepare_img() , return_tensors="pt" )
snake_case_ = model(**lowerCAmelCase_ )
# verify classification model
if task_name.startswith("imagenet" ):
snake_case_ = outputs.logits
snake_case_ = logits.argmax(-1 ).item()
print("Predicted class:" , model.config.idalabel[predicted_class_idx] )
if task_name.startswith("imagenet1k_256" ) and config.width_multiplier == 1.0:
# expected_logits for base variant
snake_case_ = torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] )
assert torch.allclose(logits[0, :3] , lowerCAmelCase_ , atol=1e-4 )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F'''Saving model {task_name} 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__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''',
default='''imagenet1k_256''',
type=str,
help=(
'''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
'''
Classification (ImageNet-1k)
- MobileViTV2 (256x256) : imagenet1k_256
- MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
- MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
imagenet21k_to_1k_256
- MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
ImageNet-1k 384x384) : imagenet21k_to_1k_384
Segmentation
- ADE20K Dataset : ade20k_deeplabv3
- Pascal VOC 2012 Dataset: voc_deeplabv3
'''
),
choices=[
'''imagenet1k_256''',
'''imagenet1k_384''',
'''imagenet21k_to_1k_256''',
'''imagenet21k_to_1k_384''',
'''ade20k_deeplabv3''',
'''voc_deeplabv3''',
],
)
parser.add_argument(
'''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
SCREAMING_SNAKE_CASE :int = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 159 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 350 |
import warnings
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 UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = ["image_processor", "tokenizer"]
UpperCAmelCase__ : str = "ViltImageProcessor"
UpperCAmelCase__ : Union[str, Any] = ("BertTokenizer", "BertTokenizerFast")
def __init__( self , _a=None , _a=None , **_a ) -> Any:
_a : Union[str, Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _a , )
_a : Dict = kwargs.pop('''feature_extractor''' )
_a : Optional[int] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_a , _a )
_a : int = self.image_processor
def __call__( self , _a , _a = None , _a = True , _a = False , _a = None , _a = None , _a = 0 , _a = None , _a = None , _a = None , _a = False , _a = False , _a = False , _a = False , _a = True , _a = None , **_a , ) -> BatchEncoding:
_a : Tuple = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_token_type_ids=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
# add pixel_values + pixel_mask
_a : str = self.image_processor(_a , return_tensors=_a )
encoding.update(_a )
return encoding
def __lowercase ( self , *_a , **_a ) -> Optional[Any]:
return self.tokenizer.batch_decode(*_a , **_a )
def __lowercase ( self , *_a , **_a ) -> str:
return self.tokenizer.decode(*_a , **_a )
@property
def __lowercase ( self ) -> Optional[int]:
_a : str = self.tokenizer.model_input_names
_a : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowercase ( self ) -> Optional[Any]:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _a , )
return self.image_processor_class
@property
def __lowercase ( self ) -> Any:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _a , )
return self.image_processor
| 15 | 0 |
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=snake_case__ ):
_a : Any = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : int = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : Union[str, Any] = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : int = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : int = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : Tuple = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : List[Any] = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : int = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : Tuple = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : Optional[int] = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : str = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : str = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class a__ ( metaclass=snake_case__ ):
_a : List[str] = ["""flax"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def __SCREAMING_SNAKE_CASE( cls , *_A , **_A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
| 92 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = abs(UpperCamelCase )
lowerCAmelCase__ : List[Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = abs(UpperCamelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return sum(int(UpperCamelCase ) for c in str(abs(UpperCamelCase ) ) )
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(UpperCamelCase , UpperCamelCase ) -> None:
lowerCAmelCase__ : str = f"""{func.__name__}({value})"""
lowerCAmelCase__ : str = timeit(f"""__main__.{call}""" , setup="""import __main__""" )
print(f"""{call:56} = {func(UpperCamelCase )} -- {timing:.4f} seconds""" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(UpperCamelCase , UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 37 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecTextConfig',
'Data2VecTextOnnxConfig',
],
'configuration_data2vec_vision': [
'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecVisionConfig',
'Data2VecVisionOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
UpperCAmelCase_ = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
UpperCAmelCase_ = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
UpperCAmelCase_ = [
'TFData2VecVisionForImageClassification',
'TFData2VecVisionForSemanticSegmentation',
'TFData2VecVisionModel',
'TFData2VecVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 61 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = tempfile.mkdtemp()
UpperCAmelCase__ = BlipImageProcessor()
UpperCAmelCase__ = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
UpperCAmelCase__ = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
UpperCAmelCase__ = InstructBlipProcessor(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **_UpperCAmelCase : Tuple ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).tokenizer
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).image_processor
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **_UpperCAmelCase : Dict ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).qformer_tokenizer
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
UpperCAmelCase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCAmelCase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
UpperCAmelCase__ = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
UpperCAmelCase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = processor(text=_UpperCAmelCase )
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
UpperCAmelCase__ = qformer_tokenizer(_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase__ = processor.batch_decode(_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 61 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_lowerCAmelCase , """embed_dim""" ) )
self.parent.assertTrue(hasattr(_lowerCAmelCase , """num_heads""" ) )
class SCREAMING_SNAKE_CASE_ :
def __init__( self : Optional[Any] , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int]=13 , lowerCamelCase_ : Tuple=64 , lowerCamelCase_ : Optional[int]=3 , lowerCamelCase_ : Any=[16, 48, 96] , lowerCamelCase_ : Optional[Any]=[1, 3, 6] , lowerCamelCase_ : Optional[int]=[1, 2, 10] , lowerCamelCase_ : List[Any]=[7, 3, 3] , lowerCamelCase_ : Union[str, Any]=[4, 2, 2] , lowerCamelCase_ : int=[2, 1, 1] , lowerCamelCase_ : Union[str, Any]=[2, 2, 2] , lowerCamelCase_ : Tuple=[False, False, True] , lowerCamelCase_ : List[Any]=[0.0, 0.0, 0.0] , lowerCamelCase_ : Tuple=0.0_2 , lowerCamelCase_ : Optional[Any]=1E-12 , lowerCamelCase_ : Union[str, Any]=True , lowerCamelCase_ : List[str]=True , lowerCamelCase_ : Optional[Any]=2 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_sizes
UpperCamelCase = patch_stride
UpperCamelCase = patch_padding
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = num_labels
UpperCamelCase = num_channels
UpperCamelCase = embed_dim
UpperCamelCase = num_heads
UpperCamelCase = stride_kv
UpperCamelCase = depth
UpperCamelCase = cls_token
UpperCamelCase = attention_drop_rate
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
# create a random int32 tensor of given shape
UpperCamelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any ):
"""simple docstring"""
UpperCamelCase = TFCvtModel(config=_lowerCAmelCase )
UpperCamelCase = model(_lowerCAmelCase , training=_lowerCAmelCase )
UpperCamelCase = (self.image_size, self.image_size)
UpperCamelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCamelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCamelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : int , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Dict ):
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFCvtForImageClassification(_lowerCAmelCase )
UpperCamelCase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase = config_and_inputs
UpperCamelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
__lowerCAmelCase = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
__lowerCAmelCase = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = TFCvtModelTester(self )
UpperCamelCase = TFCvtConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.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()
@unittest.skip(reason="""Cvt does not output attentions""" )
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(_lowerCAmelCase )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_lowerCAmelCase )
UpperCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
def check_hidden_states_output(lowerCamelCase_ : Tuple , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
UpperCamelCase = model_class(_lowerCAmelCase )
UpperCamelCase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCamelCase = outputs.hidden_states
UpperCamelCase = len(self.model_tester.depth )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = TFCvtModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def lowercase( ) -> str:
'''simple docstring'''
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
UpperCamelCase = model(**_lowerCAmelCase )
# verify the logits
UpperCamelCase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
UpperCamelCase = tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _lowerCAmelCase , atol=1E-4 ) )
| 343 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}
class __lowerCAmelCase ( lowerCAmelCase):
_a = '''ernie_m'''
_a = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self: List[Any] , _lowerCAmelCase: int = 25_00_02 , _lowerCAmelCase: int = 7_68 , _lowerCAmelCase: int = 12 , _lowerCAmelCase: int = 12 , _lowerCAmelCase: int = 30_72 , _lowerCAmelCase: str = "gelu" , _lowerCAmelCase: float = 0.1 , _lowerCAmelCase: float = 0.1 , _lowerCAmelCase: int = 5_14 , _lowerCAmelCase: float = 0.02 , _lowerCAmelCase: int = 1 , _lowerCAmelCase: float = 1e-0_5 , _lowerCAmelCase: Dict=None , _lowerCAmelCase: Optional[int]=False , _lowerCAmelCase: List[str]=0.0 , **_lowerCAmelCase: Tuple , ):
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase :Tuple = vocab_size
lowercase :List[str] = hidden_size
lowercase :Optional[int] = num_hidden_layers
lowercase :Optional[Any] = num_attention_heads
lowercase :Optional[Any] = intermediate_size
lowercase :Optional[Any] = hidden_act
lowercase :Any = hidden_dropout_prob
lowercase :int = attention_probs_dropout_prob
lowercase :Dict = max_position_embeddings
lowercase :Optional[Any] = initializer_range
lowercase :Any = layer_norm_eps
lowercase :Union[str, Any] = classifier_dropout
lowercase :int = is_decoder
lowercase :List[str] = act_dropout
| 236 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a :Dict = True
except (ImportError, ModuleNotFoundError):
a :Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _lowercase ( __lowerCAmelCase ) -> str:
re.sub("""<n>""" , """""" , __lowerCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowerCAmelCase ) )
| 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
} # Priority of each operator
SCREAMING_SNAKE_CASE__ : List[Any] = len(__lowerCAmelCase ) if (len(__lowerCAmelCase ) > 7) else 7
# Print table header for output
print(
"""Symbol""".center(8 ) , """Stack""".center(__lowerCAmelCase ) , """Postfix""".center(__lowerCAmelCase ) , sep=""" | """ , )
print("""-""" * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(__lowerCAmelCase ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(__lowerCAmelCase ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(__lowerCAmelCase ) == 0:
stack.append(__lowerCAmelCase ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(__lowerCAmelCase ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(__lowerCAmelCase ) # push x to stack
print(
x.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format
while len(__lowerCAmelCase ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
""" """.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format
return "".join(__lowerCAmelCase ) # return Postfix as str
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] = list(infix[::-1] ) # reverse the infix equation
for i in range(len(__lowerCAmelCase ) ):
if infix[i] == "(":
SCREAMING_SNAKE_CASE__ : Optional[int] = """)""" # change "(" to ")"
elif infix[i] == ")":
SCREAMING_SNAKE_CASE__ : Optional[Any] = """(""" # change ")" to "("
return (infix_2_postfix("""""".join(__lowerCAmelCase ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
a :Optional[int] = input("\nEnter an Infix Equation = ") # Input an Infix equation
a :Dict = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 56 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_SCREAMING_SNAKE_CASE : Any = HUGGINGFACE_HUB_CACHE
_SCREAMING_SNAKE_CASE : Optional[Any] = "config.json"
_SCREAMING_SNAKE_CASE : Optional[int] = "diffusion_pytorch_model.bin"
_SCREAMING_SNAKE_CASE : int = "diffusion_flax_model.msgpack"
_SCREAMING_SNAKE_CASE : Tuple = "model.onnx"
_SCREAMING_SNAKE_CASE : Dict = "diffusion_pytorch_model.safetensors"
_SCREAMING_SNAKE_CASE : List[Any] = "weights.pb"
_SCREAMING_SNAKE_CASE : Dict = "https://huggingface.co"
_SCREAMING_SNAKE_CASE : int = default_cache_path
_SCREAMING_SNAKE_CASE : List[Any] = "diffusers_modules"
_SCREAMING_SNAKE_CASE : Union[str, Any] = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
_SCREAMING_SNAKE_CASE : int = ["fp16", "non-ema"]
_SCREAMING_SNAKE_CASE : Tuple = ".self_attn"
| 85 | """simple docstring"""
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 289 | 0 |
"""simple docstring"""
from manim import *
class _A ( _a ):
"""simple docstring"""
def __snake_case ( self : Optional[Any]):
a : str = Rectangle(height=0.5 , width=0.5)
a : Optional[Any] = Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
a : List[Any] = Rectangle(height=0.25 , width=0.25)
a : Tuple = [mem.copy() for i in range(6)]
a : str = [mem.copy() for i in range(6)]
a : List[Any] = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : Union[str, Any] = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : List[str] = VGroup(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : str = Text("CPU" , font_size=24)
a : str = Group(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase)
cpu.move_to([-2.5, -0.5, 0])
self.add(__UpperCAmelCase)
a : List[Any] = [mem.copy() for i in range(4)]
a : Union[str, Any] = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : int = Text("GPU" , font_size=24)
a : Any = Group(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase)
gpu.move_to([-1, -1, 0])
self.add(__UpperCAmelCase)
a : Any = [mem.copy() for i in range(6)]
a : Dict = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : Dict = Text("Model" , font_size=24)
a : Optional[int] = Group(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase)
model.move_to([3, -1.0, 0])
self.add(__UpperCAmelCase)
a : Dict = []
a : Optional[int] = []
for i, rect in enumerate(__UpperCAmelCase):
a : Tuple = fill.copy().set_fill(__UpperCAmelCase , opacity=0.8)
target.move_to(__UpperCAmelCase)
model_arr.append(__UpperCAmelCase)
a : Any = Rectangle(height=0.46 , width=0.46).set_stroke(width=0.0).set_fill(__UpperCAmelCase , opacity=0.8)
cpu_target.move_to(cpu_left_col_base[i])
model_cpu_arr.append(__UpperCAmelCase)
self.add(*__UpperCAmelCase , *__UpperCAmelCase)
a : Optional[int] = [meta_mem.copy() for i in range(6)]
a : List[Any] = [meta_mem.copy() for i in range(6)]
a : int = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : Optional[Any] = VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : List[Any] = VGroup(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0)
a : int = Text("Disk" , font_size=24)
a : Union[str, Any] = Group(__UpperCAmelCase , __UpperCAmelCase).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase)
disk.move_to([-4, -1.25, 0])
self.add(__UpperCAmelCase , __UpperCAmelCase)
a : Dict = Square(side_length=2.2)
key.move_to([-5, 2, 0])
a : str = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
self.add(__UpperCAmelCase , __UpperCAmelCase)
a : int = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(__UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left())
self.add(__UpperCAmelCase)
a : Optional[int] = MarkupText(
f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(__UpperCAmelCase))
a : Any = Square(0.3)
input.set_fill(__UpperCAmelCase , opacity=1.0)
input.set_stroke(width=0.0)
input.next_to(model_base[0] , __UpperCAmelCase , buff=0.5)
self.play(Write(__UpperCAmelCase))
input.generate_target()
input.target.next_to(model_arr[0] , direction=__UpperCAmelCase , buff=0.02)
self.play(MoveToTarget(__UpperCAmelCase))
self.play(FadeOut(__UpperCAmelCase))
a : Optional[int] = Arrow(start=__UpperCAmelCase , end=__UpperCAmelCase , color=__UpperCAmelCase , buff=0.5)
a.next_to(model_arr[0].get_left() , __UpperCAmelCase , buff=0.2)
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0])
a : Any = MarkupText(
f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(__UpperCAmelCase , run_time=3))
a : Tuple = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(__UpperCAmelCase) , Circumscribe(model_arr[0] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(model_cpu_arr[0] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase) , )
self.play(MoveToTarget(model_cpu_arr[0]))
a : List[Any] = a.copy()
for i in range(6):
a_c.next_to(model_arr[i].get_right() + 0.02 , __UpperCAmelCase , buff=0.2)
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02)
a : List[str] = AnimationGroup(
FadeOut(__UpperCAmelCase , run_time=0.5) , MoveToTarget(__UpperCAmelCase , run_time=0.5) , FadeIn(__UpperCAmelCase , run_time=0.5) , lag_ratio=0.2)
self.play(__UpperCAmelCase)
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i])
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
if i >= 1:
a : List[Any] = 0.7
self.play(
Circumscribe(model_arr[i] , **__UpperCAmelCase) , Circumscribe(cpu_left_col_base[i] , **__UpperCAmelCase) , Circumscribe(cpu_left_col_base[i + 1] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(model_arr[i + 1] , color=__UpperCAmelCase , **__UpperCAmelCase) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i]) , MoveToTarget(model_cpu_arr[i + 1]) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2)
self.play(
Circumscribe(model_arr[-1] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(cpu_left_col_base[-1] , color=__UpperCAmelCase , **__UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase) , )
self.play(MoveToTarget(model_cpu_arr[i]))
a : str = a_c
a : Dict = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5)
self.play(
FadeOut(__UpperCAmelCase) , FadeOut(__UpperCAmelCase , run_time=0.5) , )
a : List[Any] = MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24)
step_a.move_to([2, 2, 0])
self.play(Write(__UpperCAmelCase , run_time=3) , MoveToTarget(__UpperCAmelCase))
self.wait()
| 226 |
"""simple docstring"""
from __future__ import annotations
class _A :
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : List[Any]=None):
a : int = data
a : Dict = None
def __repr__( self : Dict):
a : List[Any] = []
a : str = self
while temp:
string_rep.append(f'''{temp.data}''')
a : Tuple = temp.next
return "->".join(__UpperCAmelCase)
def lowercase ( A_ )-> Any:
'''simple docstring'''
if not elements_list:
raise Exception("The Elements List is empty" )
a : Any = Node(elements_list[0] )
for i in range(1 , len(A_ ) ):
a : int = Node(elements_list[i] )
a : Optional[Any] = current.next
return head
def lowercase ( A_ )-> None:
'''simple docstring'''
if head_node is not None and isinstance(A_ , A_ ):
print_reverse(head_node.next )
print(head_node.data )
def lowercase ( )-> List[Any]:
'''simple docstring'''
from doctest import testmod
testmod()
a : Union[str, Any] = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(A_ )
print("Elements in Reverse:" )
print_reverse(A_ )
if __name__ == "__main__":
main()
| 226 | 1 |
"""simple docstring"""
import numpy as np
class UpperCamelCase :
def __init__( self ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ) -> str:
'''simple docstring'''
self.set_matricies(red=snake_case_ ,green=snake_case_ ,blue=snake_case_ ,red_edge=snake_case_ ,nir=snake_case_ )
def _UpperCAmelCase ( self ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ) -> Optional[Any]:
'''simple docstring'''
if red is not None:
lowercase_ : Any = red
if green is not None:
lowercase_ : List[Any] = green
if blue is not None:
lowercase_ : Any = blue
if red_edge is not None:
lowercase_ : Dict = red_edge
if nir is not None:
lowercase_ : Tuple = nir
return True
def _UpperCAmelCase ( self ,__UpperCamelCase="" ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ) -> str:
'''simple docstring'''
self.set_matricies(red=snake_case_ ,green=snake_case_ ,blue=snake_case_ ,red_edge=snake_case_ ,nir=snake_case_ )
lowercase_ : Any = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def _UpperCAmelCase ( self ,__UpperCamelCase=0.08 ,__UpperCamelCase=1.22 ,__UpperCamelCase=0.03 ) -> Union[str, Any]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return (self.nir / self.green) - 1
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
return (self.red - self.blue) / self.red
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : List[Any] = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return self.nir - self.green
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : Union[str, Any] = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def _UpperCAmelCase ( self ,__UpperCamelCase=0.16 ) -> Dict:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def _UpperCAmelCase ( self ,__UpperCamelCase=0.5 ) -> List[Any]:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def _UpperCAmelCase ( self ,__UpperCamelCase=None ,__UpperCamelCase=None ) -> List[str]:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
return self.nir / self.red
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
lowercase_ : List[str] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowercase_ : str = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
return self.nir / self.red
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 213 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = """https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def __UpperCamelCase ( _lowerCAmelCase ) -> Dict:
"""simple docstring"""
if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
A : Optional[int] = key.replace(""".model.1.bias""" , """.conv1d_1.bias""" )
elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
A : Any = key.replace(""".model.1.weight""" , """.conv1d_1.weight""" )
elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
A : str = key.replace(""".model.3.bias""" , """.conv1d_2.bias""" )
elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
A : Optional[Any] = key.replace(""".model.3.weight""" , """.conv1d_2.weight""" )
if "conditioner_blocks.0." in key:
A : List[str] = key.replace("""conditioner_blocks.0""" , """conditioner_blocks""" )
if "prime_prior" in key:
A : Tuple = key.replace("""prime_prior""" , """encoder""" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
A : List[str] = key.replace(""".emb.""" , """.""" )
if key.endswith("""k""" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(""".k""" , """.codebook""" )
if "y_emb." in key:
return key.replace("""y_emb.""" , """metadata_embedding.""" )
if "x_emb.emb." in key:
A : Optional[int] = key.replace("""0.x_emb.emb""" , """embed_tokens""" )
if "prime_state_ln" in key:
return key.replace("""prime_state_ln""" , """encoder.final_layer_norm""" )
if ".ln" in key:
return key.replace(""".ln""" , """.layer_norm""" )
if "_ln" in key:
return key.replace("""_ln""" , """_layer_norm""" )
if "prime_state_proj" in key:
return key.replace("""prime_state_proj""" , """encoder.proj_in""" )
if "prime_x_out" in key:
return key.replace("""prime_x_out""" , """encoder.lm_head""" )
if "prior.x_out" in key:
return key.replace("""x_out""" , """fc_proj_out""" )
if "x_emb" in key:
return key.replace("""x_emb""" , """embed_tokens""" )
return key
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
A : List[str] = {}
import re
A : Any = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
A : str = re.compile(
R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : Union[str, Any] = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
A : List[Any] = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
A : Optional[Any] = re.compile(
R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : List[str] = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
A : Optional[Any] = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)""" )
A : Tuple = re.compile(
R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : List[Any] = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)""" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_lowerCAmelCase ):
A : Optional[Any] = re_encoder_block_conv_in.match(_lowerCAmelCase )
A : Tuple = regex_match.groups()
A : str = int(groups[2] ) * 2 + int(groups[3] )
A : int = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
A : Any = re_encoder_block_conv_in.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_encoder_block_resnet.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_encoder_block_resnet.match(_lowerCAmelCase )
A : str = regex_match.groups()
A : Optional[int] = int(groups[2] ) * 2 + int(groups[3] )
A : Any = {"""1""": 1, """3""": 2}[groups[-2]]
A : int = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
A : List[str] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : str = prefix + resnet_block
A : str = re_encoder_block_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_encoder_block_proj_out.fullmatch(_lowerCAmelCase ):
A : List[str] = re_encoder_block_proj_out.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[Any] = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
A : Optional[int] = re_encoder_block_proj_out.sub(_lowerCAmelCase , _lowerCAmelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_lowerCAmelCase ):
A : Union[str, Any] = re_decoder_block_conv_out.match(_lowerCAmelCase )
A : Dict = regex_match.groups()
A : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
A : Optional[int] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
A : int = re_decoder_block_conv_out.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_decoder_block_resnet.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_decoder_block_resnet.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[str] = int(groups[2] ) * 2 + int(groups[3] ) - 2
A : str = {"""1""": 1, """3""": 2}[groups[-2]]
A : Optional[int] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
A : Union[str, Any] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : Tuple = prefix + resnet_block
A : Tuple = re_decoder_block_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_decoder_block_proj_in.fullmatch(_lowerCAmelCase ):
A : Optional[Any] = re_decoder_block_proj_in.match(_lowerCAmelCase )
A : Any = regex_match.groups()
A : Optional[Any] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
A : Dict = re_decoder_block_proj_in.sub(_lowerCAmelCase , _lowerCAmelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_prior_cond_conv_out.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
A : Tuple = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
A : List[str] = re_prior_cond_conv_out.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_prior_cond_resnet.fullmatch(_lowerCAmelCase ):
A : Any = re_prior_cond_resnet.match(_lowerCAmelCase )
A : Any = regex_match.groups()
A : Tuple = int(groups[1] ) * 2 + int(groups[2] ) - 2
A : Optional[Any] = {"""1""": 1, """3""": 2}[groups[-2]]
A : Tuple = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
A : List[str] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : Dict = prefix + resnet_block
A : Union[str, Any] = re_prior_cond_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_prior_cond_proj_in.fullmatch(_lowerCAmelCase ):
A : List[Any] = re_prior_cond_proj_in.match(_lowerCAmelCase )
A : Optional[int] = regex_match.groups()
A : Tuple = f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
A : Optional[int] = re_prior_cond_proj_in.sub(_lowerCAmelCase , _lowerCAmelCase )
# keep original key
else:
A : str = original_key
A : List[str] = replace_key(_lowerCAmelCase )
if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(f'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
A : str = model_state_dict[f'''{key_prefix}.{key}''']
print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
A : Union[str, Any] = original_key
A : Union[str, Any] = original_key
A : List[str] = value
return new_dict
@torch.no_grad()
def __UpperCamelCase ( _lowerCAmelCase=None , _lowerCAmelCase=None ) -> Tuple:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' ):
A : Optional[Any] = requests.get(f'''{PREFIX}{file}''' , allow_redirects=_lowerCAmelCase )
os.makedirs(f'''{pytorch_dump_folder_path}/''' , exist_ok=_lowerCAmelCase )
open(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' , """wb""" ).write(r.content )
A : Optional[int] = MODEL_MAPPING[model_name.split("""/""" )[-1]]
A : List[Any] = JukeboxConfig.from_pretrained(_lowerCAmelCase )
A : Dict = JukeboxModel(_lowerCAmelCase )
A : str = []
A : Optional[int] = {}
for i, dict_name in enumerate(_lowerCAmelCase ):
A : str = torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}''' )["""model"""]
A : Optional[int] = {}
for k in old_dic.keys():
if k.endswith(""".b""" ):
A : Dict = old_dic[k]
elif k.endswith(""".w""" ):
A : Optional[Any] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
A : Union[str, Any] = old_dic[k]
else:
A : Optional[int] = old_dic[k]
A : List[str] = """vqvae""" if i == 0 else f'''priors.{3 - i}'''
A : List[Any] = fix_jukebox_keys(_lowerCAmelCase , model.state_dict() , _lowerCAmelCase , _lowerCAmelCase )
weight_dict.append(_lowerCAmelCase )
A : List[Any] = weight_dict.pop(0 )
model.vqvae.load_state_dict(_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
with open(f'''{pytorch_dump_folder_path}/mapping.json''' , """w""" ) as txtfile:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowerCAmelCase )
return weight_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_:List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
SCREAMING_SNAKE_CASE_:int = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 115 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
A : int = []
for line in lines:
A : int = re.sub(R"""#.*""" , """""" , _lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(_lowerCAmelCase )
A : Tuple = """\n""".join(_lowerCAmelCase )
# Make a hash from all this code
A : Union[str, Any] = full_str.encode("""utf-8""" )
return shaaaa(_lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any] = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int] = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 115 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class A ( __UpperCAmelCase ):
__snake_case = 'data2vec-text'
def __init__( self, UpperCamelCase__=3_0522, UpperCamelCase__=768, UpperCamelCase__=12, UpperCamelCase__=12, UpperCamelCase__=3072, UpperCamelCase__="gelu", UpperCamelCase__=0.1, UpperCamelCase__=0.1, UpperCamelCase__=512, UpperCamelCase__=2, UpperCamelCase__=0.02, UpperCamelCase__=1E-12, UpperCamelCase__=1, UpperCamelCase__=0, UpperCamelCase__=2, UpperCamelCase__="absolute", UpperCamelCase__=True, UpperCamelCase__=None, **UpperCamelCase__, ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__ )
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = position_embedding_type
lowerCAmelCase_ = use_cache
lowerCAmelCase_ = classifier_dropout
class A ( __UpperCAmelCase ):
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for key, value in counts.items() if value > 1]
lowerCAmelCase_ = []
for duplicate_key in duplicates:
lowerCAmelCase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(_A ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(_A , key=lambda _A : s["title"].lower() )
def __UpperCamelCase ( _A=False ):
with open(_A , encoding='''utf-8''' ) as f:
lowerCAmelCase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowerCAmelCase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowerCAmelCase_ = content[api_idx]['''sections''']
# Then to the model doc
lowerCAmelCase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowerCAmelCase_ = api_doc[model_idx]['''sections''']
lowerCAmelCase_ = [(idx, section) for idx, section in enumerate(_A ) if '''sections''' in section]
lowerCAmelCase_ = False
for idx, modality_doc in modalities_docs:
lowerCAmelCase_ = modality_doc['''sections''']
lowerCAmelCase_ = clean_model_doc_toc(_A )
if old_modality_doc != new_modality_doc:
lowerCAmelCase_ = True
if overwrite:
lowerCAmelCase_ = new_modality_doc
if diff:
if overwrite:
lowerCAmelCase_ = model_doc
lowerCAmelCase_ = api_doc
with open(_A , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(_A , allow_unicode=_A ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_A = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 278 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ : Optional[int] = logging.get_logger(__name__)
lowercase__ : List[str] = "▁"
lowercase__ : Optional[Any] = {"vocab_file": "sentencepiece.bpe.model"}
lowercase__ : Tuple = {
"vocab_file": {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
),
}
}
lowercase__ : Any = {
"xlm-roberta-base": 512,
"xlm-roberta-large": 512,
"xlm-roberta-large-finetuned-conll02-dutch": 512,
"xlm-roberta-large-finetuned-conll02-spanish": 512,
"xlm-roberta-large-finetuned-conll03-english": 512,
"xlm-roberta-large-finetuned-conll03-german": 512,
}
class a__ ( UpperCamelCase__ ):
a : Union[str, Any] = VOCAB_FILES_NAMES
a : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
a : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : List[str] = ["""input_ids""", """attention_mask"""]
def __init__( self , A , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , A = None , **A , ) -> None:
'''simple docstring'''
a = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
a = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
a = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
a = 1
a = len(self.sp_model ) + self.fairseq_offset
a = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Optional[Any]:
'''simple docstring'''
a = self.__dict__.copy()
a = None
a = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , A ) -> Any:
'''simple docstring'''
a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowerCAmelCase_ ( self , A , A = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a = [self.cls_token_id]
a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase_ ( self , A , A = None , A = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def lowerCAmelCase_ ( self , A , A = None ) -> List[int]:
'''simple docstring'''
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
a = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCAmelCase_ ( self , A ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(A , out_type=A )
def lowerCAmelCase_ ( self , A ) -> Optional[Any]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
a = self.sp_model.PieceToId(A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCAmelCase_ ( self , A ) -> Dict:
'''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 lowerCAmelCase_ ( self , A ) -> Any:
'''simple docstring'''
a = "".join(A ).replace(A , " " ).strip()
return out_string
def lowerCAmelCase_ ( self , A , A = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , "wb" ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 180 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a__ ( UpperCamelCase__ , unittest.TestCase ):
a : List[Any] = RoFormerTokenizer
a : Tuple = RoFormerTokenizerFast
a : Dict = True
a : Optional[Any] = True
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
def lowerCAmelCase_ ( self , **A ) -> Tuple:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **A )
def lowerCAmelCase_ ( self , **A ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **A )
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
a = "永和服装饰品有限公司,今天天气非常好"
a = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
a = self.get_tokenizer()
a , a = self.get_chinese_input_output_texts()
a = tokenizer.tokenize(A )
self.assertListEqual(A , output_text.split() )
a = tokens + [tokenizer.unk_token]
a = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
a = self.get_rust_tokenizer()
a , a = self.get_chinese_input_output_texts()
a = tokenizer.tokenize(A )
self.assertListEqual(A , output_text.split() )
a = tokens + [tokenizer.unk_token]
a = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 180 | 1 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : list) -> List[str]:
"""simple docstring"""
_validate_point(_lowerCAmelCase)
_validate_point(_lowerCAmelCase)
if len(_lowerCAmelCase) != len(_lowerCAmelCase):
raise ValueError("""Both points must be in the same n-dimensional space""")
return float(sum(abs(a - b) for a, b in zip(_lowerCAmelCase , _lowerCAmelCase)))
def lowerCAmelCase_ ( _lowercase : list[float]) -> Dict:
"""simple docstring"""
if point:
if isinstance(_lowerCAmelCase , _lowerCAmelCase):
for item in point:
if not isinstance(_lowerCAmelCase , (int, float)):
a__ : Dict = (
"""Expected a list of numbers as input, found """
F'''{type(_lowerCAmelCase).__name__}'''
)
raise TypeError(_lowerCAmelCase)
else:
a__ : int = F'''Expected a list of numbers as input, found {type(_lowerCAmelCase).__name__}'''
raise TypeError(_lowerCAmelCase)
else:
raise ValueError("""Missing an input""")
def lowerCAmelCase_ ( _lowercase : list , _lowercase : list) -> List[str]:
"""simple docstring"""
_validate_point(_lowerCAmelCase)
_validate_point(_lowerCAmelCase)
if len(_lowerCAmelCase) != len(_lowerCAmelCase):
raise ValueError("""Both points must be in the same n-dimensional space""")
return float(sum(abs(x - y) for x, y in zip(_lowerCAmelCase , _lowerCAmelCase)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCAmelCase : List[str] = 1_6
_lowerCAmelCase : List[Any] = 3_2
def lowerCAmelCase ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
UpperCAmelCase__ = AutoTokenizer.from_pretrained("bert-base-cased" )
UpperCAmelCase__ = load_dataset("glue" , "mrpc" )
def tokenize_function(_lowerCAmelCase : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCAmelCase__ = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCAmelCase__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCAmelCase__ = 16
elif accelerator.mixed_precision != "no":
UpperCAmelCase__ = 8
else:
UpperCAmelCase__ = None
return tokenizer.pad(
_lowerCAmelCase , padding="longest" , max_length=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_tensors="pt" , )
# Instantiate dataloaders.
UpperCAmelCase__ = DataLoader(
tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
UpperCAmelCase__ = DataLoader(
tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCAmelCase : int = mocked_dataloaders # noqa: F811
def lowerCAmelCase ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , _lowerCAmelCase ) == "1":
UpperCAmelCase__ = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
UpperCAmelCase__ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
UpperCAmelCase__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase__ = config["lr"]
UpperCAmelCase__ = int(config["num_epochs"] )
UpperCAmelCase__ = int(config["seed"] )
UpperCAmelCase__ = int(config["batch_size"] )
set_seed(_lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase__ = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
UpperCAmelCase__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCAmelCase__ = batch_size // MAX_GPU_BATCH_SIZE
UpperCAmelCase__ = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCAmelCase__ = model.to(accelerator.device )
# Instantiate optimizer
UpperCAmelCase__ = AdamW(params=model.parameters() , lr=_lowerCAmelCase )
# Instantiate scheduler
UpperCAmelCase__ = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
UpperCAmelCase__ = os.path.split(_lowerCAmelCase )[-1].split("." )[0]
accelerator.init_trackers(_lowerCAmelCase , _lowerCAmelCase )
# Now we train the model
for epoch in range(_lowerCAmelCase ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
UpperCAmelCase__ = 0
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCAmelCase__ = model(**_lowerCAmelCase )
UpperCAmelCase__ = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
UpperCAmelCase__ = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase__ = model(**_lowerCAmelCase )
UpperCAmelCase__ = outputs.logits.argmax(dim=-1 )
UpperCAmelCase__ , UpperCAmelCase__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=_lowerCAmelCase , references=_lowerCAmelCase , )
UpperCAmelCase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , _lowerCAmelCase )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(_lowerCAmelCase ),
"epoch": epoch,
} , step=_lowerCAmelCase , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=_lowerCAmelCase , default=_lowerCAmelCase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=_lowerCAmelCase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 169 | 0 |
"""simple docstring"""
def _a ( lowerCamelCase: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
assert (
isinstance(a__ , a__ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
__A = 1, 1
for _ in range(number_of_steps - 1 ):
__A = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :Optional[int] , *_UpperCamelCase :int , **_UpperCamelCase :List[str] )-> List[str]:
requires_backends(self , ['''speech'''] )
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :Optional[int] , *_UpperCamelCase :str , **_UpperCamelCase :List[str] )-> Union[str, Any]:
requires_backends(self , ['''speech'''] )
| 250 | 0 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Optional[Any] ) -> None:
UpperCAmelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCAmelCase : str = False
def A ( self : Tuple , __snake_case : list[str] ) -> None:
for word in words:
self.insert(__snake_case )
def A ( self : Union[str, Any] , __snake_case : str ) -> None:
UpperCAmelCase : Any = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase : str = TrieNode()
UpperCAmelCase : str = curr.nodes[char]
UpperCAmelCase : Optional[Any] = True
def A ( self : Optional[Any] , __snake_case : str ) -> bool:
UpperCAmelCase : str = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase : List[str] = curr.nodes[char]
return curr.is_leaf
def A ( self : List[str] , __snake_case : str ) -> None:
def _delete(__snake_case : TrieNode , __snake_case : str , __snake_case : int ) -> bool:
if index == len(__snake_case ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase : Union[str, Any] = False
return len(curr.nodes ) == 0
UpperCAmelCase : Optional[Any] = word[index]
UpperCAmelCase : str = curr.nodes.get(__snake_case )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase : Union[str, Any] = _delete(__snake_case , __snake_case , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , __snake_case , 0 )
def snake_case_ ( _lowerCAmelCase : TrieNode , _lowerCAmelCase : str ) -> None:
if node.is_leaf:
print(_lowerCAmelCase , end=''' ''' )
for key, value in node.nodes.items():
print_words(_lowerCAmelCase , word + key )
def snake_case_ ( ) -> bool:
UpperCAmelCase : List[str] = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase : int = TrieNode()
root.insert_many(_lowerCAmelCase )
# print_words(root, "")
assert all(root.find(_lowerCAmelCase ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : bool ) -> None:
print(str(_lowerCAmelCase ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ( ) -> None:
assert test_trie()
def snake_case_ ( ) -> None:
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 23 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "yolos"
def __init__( self : Any ,A : Optional[Any]=7_68 ,A : Dict=12 ,A : Any=12 ,A : str=30_72 ,A : Any="gelu" ,A : str=0.0 ,A : List[str]=0.0 ,A : Dict=0.02 ,A : int=1E-12 ,A : Tuple=[5_12, 8_64] ,A : List[Any]=16 ,A : str=3 ,A : str=True ,A : Any=1_00 ,A : Dict=True ,A : Dict=False ,A : Tuple=1 ,A : Union[str, Any]=5 ,A : Optional[Any]=2 ,A : Union[str, Any]=5 ,A : int=2 ,A : int=0.1 ,**A : List[str] ,):
super().__init__(**A )
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = qkv_bias
__A = num_detection_tokens
__A = use_mid_position_embeddings
__A = auxiliary_loss
# Hungarian matcher
__A = class_cost
__A = bbox_cost
__A = giou_cost
# Loss coefficients
__A = bbox_loss_coefficient
__A = giou_loss_coefficient
__A = eos_coefficient
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.11" )
@property
def UpperCamelCase_ ( self : str ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase_ ( self : List[Any] ):
return 1E-4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 12
| 15 | 0 |
"""simple docstring"""
import os
def snake_case_() -> Optional[int]:
"""simple docstring"""
_snake_case = os.path.dirname(os.path.realpath(_UpperCamelCase ) )
_snake_case = os.path.join(_UpperCamelCase , '''triangle.txt''' )
with open(_UpperCamelCase ) as f:
_snake_case = f.readlines()
_snake_case = []
for line in triangle:
_snake_case = []
for number in line.strip().split(''' ''' ):
numbers_from_line.append(int(_UpperCamelCase ) )
a.append(_UpperCamelCase )
for i in range(1 , len(_UpperCamelCase ) ):
for j in range(len(a[i] ) ):
_snake_case = a[i - 1][j] if j != len(a[i - 1] ) else 0
_snake_case = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(_UpperCamelCase , _UpperCamelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 368 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case = str(bin(_UpperCamelCase ) )[2:]
_snake_case = max(len(_UpperCamelCase ) , len(_UpperCamelCase ) )
return "0b" + "".join(
str(int('''1''' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase ) , b_binary.zfill(_UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 | 0 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class A_ (lowercase__ ):
'''simple docstring'''
# to overwrite at feature extractactor specific tests
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : List[str] = None
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowercase_ , "feature_size" ) )
self.assertTrue(hasattr(lowercase_ , "sampling_rate" ) )
self.assertTrue(hasattr(lowercase_ , "padding_value" ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Union[str, Any] = feat_extract.model_input_names[0]
UpperCAmelCase_ : Tuple = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(lowercase_ ) == len(lowercase_ ) for x, y in zip(lowercase_ , processed_features[input_name] ) ) )
UpperCAmelCase_ : Any = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_ )
UpperCAmelCase_ : List[Any] = BatchFeature({input_name: speech_inputs} , tensor_type="np" )
UpperCAmelCase_ : int = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase_ : Optional[int] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_ )
UpperCAmelCase_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Tuple = feat_extract.model_input_names[0]
UpperCAmelCase_ : Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type="pt" )
UpperCAmelCase_ : List[str] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase_ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_ )
UpperCAmelCase_ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : int = feat_extract.model_input_names[0]
UpperCAmelCase_ : Tuple = BatchFeature({input_name: speech_inputs} , tensor_type="tf" )
UpperCAmelCase_ : List[str] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase_ : Tuple = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def UpperCamelCase__ ( self , lowercase_=False ):
"""simple docstring"""
def _inputs_have_equal_length(lowercase_ ):
UpperCAmelCase_ : str = len(input[0] )
for input_slice in input[1:]:
if len(lowercase_ ) != length:
return False
return True
def _inputs_are_equal(lowercase_ , lowercase_ ):
if len(lowercase_ ) != len(lowercase_ ):
return False
for input_slice_a, input_slice_a in zip(lowercase_ , lowercase_ ):
if not np.allclose(np.asarray(lowercase_ ) , np.asarray(lowercase_ ) , atol=1E-3 ):
return False
return True
UpperCAmelCase_ : int = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_ )
UpperCAmelCase_ : Union[str, Any] = feat_extract.model_input_names[0]
UpperCAmelCase_ : Optional[Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase_ : Union[str, Any] = self.feat_extract_tester.seq_length_diff
UpperCAmelCase_ : List[str] = self.feat_extract_tester.max_seq_length + pad_diff
UpperCAmelCase_ : Optional[Any] = self.feat_extract_tester.min_seq_length
UpperCAmelCase_ : Optional[Any] = self.feat_extract_tester.batch_size
UpperCAmelCase_ : Dict = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
UpperCAmelCase_ : int = feat_extract.pad(lowercase_ , padding=lowercase_ )
UpperCAmelCase_ : Optional[Any] = input_a[input_name]
UpperCAmelCase_ : List[str] = feat_extract.pad(lowercase_ , padding="longest" )
UpperCAmelCase_ : Dict = input_a[input_name]
UpperCAmelCase_ : Optional[Any] = feat_extract.pad(lowercase_ , padding="max_length" , max_length=len(speech_inputs[-1] ) )
UpperCAmelCase_ : Optional[Any] = input_a[input_name]
UpperCAmelCase_ : Optional[int] = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="np" )
UpperCAmelCase_ : Any = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(lowercase_ ):
feat_extract.pad(lowercase_ , padding="max_length" )[input_name]
UpperCAmelCase_ : List[Any] = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=lowercase_ , return_tensors="np" )
UpperCAmelCase_ : List[str] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(_inputs_are_equal(lowercase_ , lowercase_ ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase_ : List[str] = feat_extract.pad(lowercase_ , pad_to_multiple_of=10 )
UpperCAmelCase_ : str = input_a[input_name]
UpperCAmelCase_ : Union[str, Any] = feat_extract.pad(lowercase_ , padding="longest" , pad_to_multiple_of=10 )
UpperCAmelCase_ : Optional[Any] = input_a[input_name]
UpperCAmelCase_ : Optional[Any] = feat_extract.pad(
lowercase_ , padding="max_length" , pad_to_multiple_of=10 , max_length=lowercase_ )
UpperCAmelCase_ : List[Any] = input_a[input_name]
UpperCAmelCase_ : List[Any] = feat_extract.pad(
lowercase_ , padding="max_length" , pad_to_multiple_of=10 , max_length=lowercase_ , return_tensors="np" , )
UpperCAmelCase_ : str = input_a[input_name]
self.assertTrue(all(len(lowercase_ ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(lowercase_ , lowercase_ ) )
UpperCAmelCase_ : Any = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(lowercase_ ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
UpperCAmelCase_ : str = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def UpperCamelCase__ ( self , lowercase_=False ):
"""simple docstring"""
def _inputs_have_equal_length(lowercase_ ):
UpperCAmelCase_ : Union[str, Any] = len(input[0] )
for input_slice in input[1:]:
if len(lowercase_ ) != length:
return False
return True
def _inputs_are_equal(lowercase_ , lowercase_ ):
if len(lowercase_ ) != len(lowercase_ ):
return False
for input_slice_a, input_slice_a in zip(lowercase_ , lowercase_ ):
if not np.allclose(np.asarray(lowercase_ ) , np.asarray(lowercase_ ) , atol=1E-3 ):
return False
return True
UpperCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_ )
UpperCAmelCase_ : str = feat_extract.model_input_names[0]
UpperCAmelCase_ : Dict = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
UpperCAmelCase_ : Optional[int] = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) , truncation=lowercase_ )
UpperCAmelCase_ : Any = input_a[input_name]
UpperCAmelCase_ : Optional[int] = feat_extract.pad(lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) )
UpperCAmelCase_ : Optional[int] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertFalse(_inputs_have_equal_length(lowercase_ ) )
# truncate to smallest with np
UpperCAmelCase_ : Dict = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) , return_tensors="np" , truncation=lowercase_ , )
UpperCAmelCase_ : int = input_a[input_name]
UpperCAmelCase_ : List[str] = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) , return_tensors="np" )
UpperCAmelCase_ : List[str] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(lowercase_ ) )
# truncate to middle
UpperCAmelCase_ : int = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[1] ) , truncation=lowercase_ , return_tensors="np" , )
UpperCAmelCase_ : Tuple = input_a[input_name]
UpperCAmelCase_ : Optional[Any] = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[1] ) , truncation=lowercase_ )
UpperCAmelCase_ : Dict = input_a[input_name]
UpperCAmelCase_ : str = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[1] ) , return_tensors="np" )
UpperCAmelCase_ : List[str] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(_inputs_are_equal(lowercase_ , lowercase_ ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(lowercase_ ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(lowercase_ ):
feat_extract.pad(lowercase_ , truncation=lowercase_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(lowercase_ ):
feat_extract.pad(lowercase_ , padding="longest" , truncation=lowercase_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(lowercase_ ):
feat_extract.pad(lowercase_ , padding="longest" , truncation=lowercase_ )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(lowercase_ ):
feat_extract.pad(lowercase_ , padding="max_length" , truncation=lowercase_ )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase_ : Optional[int] = 12
UpperCAmelCase_ : Dict = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=lowercase_ , truncation=lowercase_ , )
UpperCAmelCase_ : int = input_a[input_name]
UpperCAmelCase_ : List[str] = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=lowercase_ , )
UpperCAmelCase_ : List[Any] = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
UpperCAmelCase_ : List[str] = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
UpperCAmelCase_ : Optional[int] = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(lowercase_ ) )
self.assertFalse(_inputs_have_equal_length(lowercase_ ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._check_padding(numpify=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._check_padding(numpify=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._check_truncation(numpify=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._check_truncation(numpify=lowercase_ )
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : List[Any] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase_ : Any = feat_extract.model_input_names[0]
UpperCAmelCase_ : Dict = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase_ : List[str] = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="np" )[input_name]
UpperCAmelCase_ : Any = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="pt" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Dict = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase_ : int = feat_extract.model_input_names[0]
UpperCAmelCase_ : List[str] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase_ : List[str] = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="np" )[input_name]
UpperCAmelCase_ : Union[str, Any] = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="tf" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.feat_extract_dict
UpperCAmelCase_ : List[str] = True
UpperCAmelCase_ : Optional[int] = self.feature_extraction_class(**lowercase_ )
UpperCAmelCase_ : Any = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase_ : Optional[int] = [len(lowercase_ ) for x in speech_inputs]
UpperCAmelCase_ : Optional[Any] = feat_extract.model_input_names[0]
UpperCAmelCase_ : str = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase_ : Optional[Any] = feat_extract.pad(lowercase_ , padding="longest" , return_tensors="np" )
self.assertIn("attention_mask" , lowercase_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.feat_extract_dict
UpperCAmelCase_ : int = True
UpperCAmelCase_ : Tuple = self.feature_extraction_class(**lowercase_ )
UpperCAmelCase_ : Dict = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase_ : Optional[int] = [len(lowercase_ ) for x in speech_inputs]
UpperCAmelCase_ : int = feat_extract.model_input_names[0]
UpperCAmelCase_ : List[str] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase_ : Optional[Any] = min(lowercase_ )
UpperCAmelCase_ : Tuple = feat_extract.pad(
lowercase_ , padding="max_length" , max_length=lowercase_ , truncation=lowercase_ , return_tensors="np" )
self.assertIn("attention_mask" , lowercase_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( _snake_case : list[float] ):
"""simple docstring"""
return np.maximum(0 , _snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 354 |
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
a : List[str] = logging.get_logger(__name__)
a : Dict = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a :
snake_case_ = field(
default=_lowerCamelCase , metadata={"help": "Model type selected in the list: " + ", ".join(_lowerCamelCase )} )
snake_case_ = field(
default=_lowerCamelCase , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
snake_case_ = field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
snake_case_ = field(
default=128 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
snake_case_ = field(
default=64 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
snake_case_ = field(
default=30 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
snake_case_ = field(
default=_lowerCamelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
snake_case_ = field(
default=_lowerCamelCase , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
snake_case_ = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
snake_case_ = field(
default=20 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
snake_case_ = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
snake_case_ = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class a ( _lowerCamelCase ):
snake_case_ = "train"
snake_case_ = "dev"
class a ( _lowerCamelCase ):
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
def __init__( self : Any , lowercase_ : SquadDataTrainingArguments , lowercase_ : PreTrainedTokenizer , lowercase_ : Optional[int] = None , lowercase_ : Union[str, Split] = Split.train , lowercase_ : Optional[bool] = False , lowercase_ : Optional[str] = None , lowercase_ : Optional[str] = "pt" , ):
snake_case_ = args
snake_case_ = is_language_sensitive
snake_case_ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(lowercase_ , lowercase_ ):
try:
snake_case_ = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
snake_case_ = mode
# Load data features from cache or dataset file
snake_case_ = '''v2''' if args.version_2_with_negative else '''v1'''
snake_case_ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ = cached_features_file + '''.lock'''
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not args.overwrite_cache:
snake_case_ = time.time()
snake_case_ = torch.load(lowercase_ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case_ = self.old_features['''features''']
snake_case_ = self.old_features.get('''dataset''' , lowercase_ )
snake_case_ = self.old_features.get('''examples''' , lowercase_ )
logger.info(
F"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
''' future run''' )
else:
if mode == Split.dev:
snake_case_ = self.processor.get_dev_examples(args.data_dir )
else:
snake_case_ = self.processor.get_train_examples(args.data_dir )
snake_case_ ,snake_case_ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=lowercase_ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=lowercase_ , )
snake_case_ = time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples} , lowercase_ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__( self : Optional[int] ):
return len(self.features )
def __getitem__( self : Union[str, Any] , lowercase_ : Optional[int] ):
# Convert to Tensors and build dataset
snake_case_ = self.features[i]
snake_case_ = torch.tensor(feature.input_ids , dtype=torch.long )
snake_case_ = torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case_ = torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case_ = torch.tensor(feature.cls_index , dtype=torch.long )
snake_case_ = torch.tensor(feature.p_mask , dtype=torch.float )
snake_case_ = torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case_ = {
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case_ = torch.tensor(feature.start_position , dtype=torch.long )
snake_case_ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 56 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class a :
def __init__( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[Any]=True , lowercase_ : Dict=True , lowercase_ : Tuple=False , lowercase_ : Optional[Any]=True , lowercase_ : Any=99 , lowercase_ : Union[str, Any]=64 , lowercase_ : str=5 , lowercase_ : int=4 , lowercase_ : List[Any]=64 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : str=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def A_ ( self : List[str] ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def A_ ( self : str ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Tuple ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def A_ ( self : Any , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A_ ( self : Tuple , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.num_labels
snake_case_ = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.num_choices
snake_case_ = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.prepare_config_and_inputs()
((snake_case_) ,(snake_case_) ,(snake_case_) ,(snake_case_) ,(snake_case_) ,(snake_case_)) = config_and_inputs
snake_case_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
def A_ ( self : Tuple ):
snake_case_ = MPNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
snake_case_ = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case_ = model(lowercase_ )[0]
snake_case_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
snake_case_ = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 56 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 367 |
def __lowerCamelCase ( __a :int = 3 , __a :int = 7 , __a :int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A__ = 0
A__ = 1
for current_denominator in range(1 , limit + 1 ):
A__ = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
A__ = current_numerator
A__ = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))
| 276 | 0 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __a ( *__lowerCamelCase ):
with open(__lowerCamelCase, "r" ) as fh:
fcntl.flock(__lowerCamelCase, fcntl.LOCK_EX )
try:
print(*__lowerCamelCase )
finally:
fcntl.flock(__lowerCamelCase, fcntl.LOCK_UN )
_a = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
_a = torch.device('cuda', local_rank)
_a = socket.gethostname()
_a = f"""[{hostname}-{local_rank}]"""
try:
# test distributed
dist.init_process_group('nccl')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
_a = dist.get_rank()
_a = dist.get_world_size()
printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")
dist.barrier()
if rank == 0:
printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")
except Exception:
printflock(f"""{gpu} is broken""")
raise
| 61 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a__ ( SCREAMING_SNAKE_CASE__, unittest.TestCase ):
_lowerCamelCase = CLIPTokenizer
_lowerCamelCase = CLIPTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = {}
_lowerCamelCase = False
def lowercase ( self : Tuple ) -> int:
super().setUp()
# fmt: off
lowercase : Dict = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
lowercase : List[Any] = dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowercase : List[str] = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
lowercase : Union[str, Any] = {'unk_token': '<unk>'}
lowercase : Union[str, Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'] )
lowercase : Tuple = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file, 'w', encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '\n' )
with open(self.merges_file, 'w', encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCAmelCase ) )
def lowercase ( self : Dict, **lowerCAmelCase : Optional[Any] ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase ( self : Optional[Any], **lowerCAmelCase : Tuple ) -> str:
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase ( self : Optional[Any], lowerCAmelCase : List[Any] ) -> Optional[Any]:
lowercase : int = 'lower newer'
lowercase : str = 'lower newer'
return input_text, output_text
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
lowercase : str = CLIPTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map )
lowercase : Union[str, Any] = 'lower newer'
lowercase : List[str] = ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
lowercase : List[str] = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
lowercase : int = tokens + [tokenizer.unk_token]
lowercase : Optional[int] = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), lowerCAmelCase )
@require_ftfy
def lowercase ( self : Tuple ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase : List[str] = self.tokenizer_class.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
lowercase : Dict = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
lowercase : Optional[int] = 'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
lowercase : int = tokenizer_s.tokenize(lowerCAmelCase )
lowercase : Any = tokenizer_r.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
lowercase : Optional[int] = 'xa\u0303y' + ' ' + 'x\xe3y'
lowercase : int = tokenizer_s.tokenize(lowerCAmelCase )
lowercase : Optional[Any] = tokenizer_r.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
# Test that the tokenization is identical on unicode of space type
lowercase : Any = [
'\u0009', # (horizontal tab, '\t')
'\u000B', # (vertical tab)
'\u000C', # (form feed)
'\u0020', # (space, ' ')
'\u200E', # (left-to-right mark):w
'\u200F', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
lowercase : Optional[Any] = tokenizer_s.tokenize(lowerCAmelCase )
lowercase : Union[str, Any] = tokenizer_r.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
# Test that the tokenization is identical on unicode of line break type
lowercase : Optional[Any] = [
'\u000A', # (line feed, '\n')
'\r\n', # (carriage return and line feed, '\r\n')
'\u000D', # (carriage return, '\r')
'\r', # (carriage return, '\r')
'\u000D', # (carriage return, '\r')
'\u2028', # (line separator)
'\u2029', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
lowercase : str = tokenizer_s.tokenize(lowerCAmelCase )
lowercase : str = tokenizer_r.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase ( self : Any ) -> List[Any]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase : Optional[int] = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
lowercase : Union[str, Any] = f'''{text_of_1_token} {text_of_1_token}'''
lowercase : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase, use_fast=lowerCAmelCase, )
lowercase : Dict = tokenizer_r(lowerCAmelCase, return_offsets_mapping=lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0], (0, len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1], (len(lowerCAmelCase ) + 1, len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )), )
lowercase : Tuple = f''' {text}'''
lowercase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase, use_fast=lowerCAmelCase, )
lowercase : Dict = tokenizer_r(lowerCAmelCase, return_offsets_mapping=lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1], (1 + len(lowerCAmelCase ) + 1, 1 + len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )), )
def lowercase ( self : Dict ) -> List[Any]:
# Test related to the breaking change introduced in transformers v4.17.0
# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
with self.assertRaises(lowerCAmelCase ) as context:
self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer' )
self.assertTrue(
context.exception.args[0].startswith(
'The `backend_tokenizer` provided does not match the expected format.' ) )
@require_ftfy
def lowercase ( self : List[Any] ) -> str:
super().test_tokenization_python_rust_equals()
def lowercase ( self : Dict ) -> Tuple:
# CLIP always lower cases letters
pass
| 255 | 0 |
def lowerCamelCase__ ( a__ : int = 1000 ) -> int:
UpperCamelCase_ = 3
UpperCamelCase_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 261 |
import re
def lowerCamelCase__ ( a__ : str ) -> bool:
UpperCamelCase_ = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(a__ , a__ ) )
if __name__ == "__main__":
_A = '''0094702343221'''
print(is_sri_lankan_phone_number(phone))
| 261 | 1 |
__snake_case : Any = {str(digit): digit**5 for digit in range(10)}
def _UpperCAmelCase ( a__):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a__))
def _UpperCAmelCase ( ):
'''simple docstring'''
return sum(
number
for number in range(1_0_0_0 , 1_0_0_0_0_0_0)
if number == digits_fifth_powers_sum(a__))
if __name__ == "__main__":
print(solution())
| 248 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case : Tuple = logging.getLogger(__name__)
def _UpperCAmelCase ( ):
'''simple docstring'''
a_ : Dict = argparse.ArgumentParser(
description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).""")
parser.add_argument("""--file_path""" , type=a__ , default="""data/dump.txt""" , help="""The path to the data.""")
parser.add_argument("""--tokenizer_type""" , type=a__ , default="""bert""" , choices=["""bert""", """roberta""", """gpt2"""])
parser.add_argument("""--tokenizer_name""" , type=a__ , default="""bert-base-uncased""" , help="""The tokenizer to use.""")
parser.add_argument("""--dump_file""" , type=a__ , default="""data/dump""" , help="""The dump file prefix.""")
a_ : int = parser.parse_args()
logger.info(f'''Loading Tokenizer ({args.tokenizer_name})''')
if args.tokenizer_type == "bert":
a_ : Any = BertTokenizer.from_pretrained(args.tokenizer_name)
a_ : Tuple = tokenizer.special_tokens_map["""cls_token"""] # `[CLS]`
a_ : Optional[Any] = tokenizer.special_tokens_map["""sep_token"""] # `[SEP]`
elif args.tokenizer_type == "roberta":
a_ : Optional[Any] = RobertaTokenizer.from_pretrained(args.tokenizer_name)
a_ : str = tokenizer.special_tokens_map["""cls_token"""] # `<s>`
a_ : Any = tokenizer.special_tokens_map["""sep_token"""] # `</s>`
elif args.tokenizer_type == "gpt2":
a_ : Union[str, Any] = GPTaTokenizer.from_pretrained(args.tokenizer_name)
a_ : Optional[int] = tokenizer.special_tokens_map["""bos_token"""] # `<|endoftext|>`
a_ : List[Any] = tokenizer.special_tokens_map["""eos_token"""] # `<|endoftext|>`
logger.info(f'''Loading text from {args.file_path}''')
with open(args.file_path , """r""" , encoding="""utf8""") as fp:
a_ : Optional[Any] = fp.readlines()
logger.info("""Start encoding""")
logger.info(f'''{len(a__)} examples to process.''')
a_ : str = []
a_ : Optional[Any] = 0
a_ : str = 1_0_0_0_0
a_ : List[str] = time.time()
for text in data:
a_ : int = f'''{bos} {text.strip()} {sep}'''
a_ : str = tokenizer.encode(a__ , add_special_tokens=a__)
rslt.append(a__)
iter += 1
if iter % interval == 0:
a_ : str = time.time()
logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl''')
a_ : int = time.time()
logger.info("""Finished binarization""")
logger.info(f'''{len(a__)} examples processed.''')
a_ : Union[str, Any] = f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
a_ : int = tokenizer.vocab_size
if vocab_size < (1 << 1_6):
a_ : List[Any] = [np.uintaa(a__) for d in rslt]
else:
a_ : List[str] = [np.intaa(a__) for d in rslt]
random.shuffle(rslt_)
logger.info(f'''Dump to {dp_file}''')
with open(a__ , """wb""") as handle:
pickle.dump(rslt_ , a__ , protocol=pickle.HIGHEST_PROTOCOL)
if __name__ == "__main__":
main()
| 248 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Tuple = {'vocab_file': 'spiece.model'}
A_ : Any = {
'vocab_file': {
'bert_for_seq_generation': (
'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
),
}
}
A_ : Union[str, Any] = {'bert_for_seq_generation': 512}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Any =VOCAB_FILES_NAMES
a : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
a : Optional[int] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : List[int] =[]
a : List[Any] =['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<::::>" , _lowerCamelCase = None , **_lowerCamelCase , ):
UpperCamelCase_: List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , sep_token=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
UpperCamelCase_: int = vocab_file
UpperCamelCase_: List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@property
def _a ( self ):
return self.sp_model.get_piece_size()
def _a ( self ):
UpperCamelCase_: Any = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
UpperCamelCase_: Dict = self.__dict__.copy()
UpperCamelCase_: Optional[Any] = None
return state
def __setstate__( self , _lowerCamelCase ):
UpperCamelCase_: Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_: str = {}
UpperCamelCase_: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCamelCase ):
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self , _lowerCamelCase ):
return self.sp_model.piece_to_id(_lowerCamelCase )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: Union[str, Any] = self.sp_model.IdToPiece(_lowerCamelCase )
return token
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: Optional[Any] = []
UpperCamelCase_: List[Any] = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCamelCase ) + token
UpperCamelCase_: Tuple = []
else:
current_sub_tokens.append(_lowerCamelCase )
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_: Union[str, Any] = 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:
UpperCamelCase_: Tuple = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,) | 358 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _a ( _lowerCamelCase ):
raise NotImplementedError()
@abstractmethod
def _a ( self ):
raise NotImplementedError() | 292 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Tuple:
_A = tempfile.mkdtemp()
_A = BlipImageProcessor()
_A = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel""" )
_A = BlipProcessor(lowerCAmelCase_ , lowerCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> List[str]:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).tokenizer
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> str:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).image_processor
def UpperCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self ) -> int:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(lowerCAmelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase ( self ) -> Any:
_A = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_A = self.get_image_processor(do_normalize=lowerCAmelCase_ , padding_value=1.0 )
_A = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCAmelCase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Optional[int]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = BlipProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_A = self.prepare_image_inputs()
_A = image_processor(lowerCAmelCase_ , return_tensors="""np""" )
_A = processor(images=lowerCAmelCase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase ( self ) -> int:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = BlipProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_A = """lower newer"""
_A = processor(text=lowerCAmelCase_ )
_A = tokenizer(lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase ( self ) -> List[str]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = BlipProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_A = """lower newer"""
_A = self.prepare_image_inputs()
_A = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase_ ):
processor()
def UpperCAmelCase ( self ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = BlipProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(lowerCAmelCase_ )
_A = tokenizer.batch_decode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> List[str]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = BlipProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_A = """lower newer"""
_A = self.prepare_image_inputs()
_A = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 180 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
_A = tempfile.mkdtemp()
_A = BlipImageProcessor()
_A = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
_A = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
_A = InstructBlipProcessor(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> Any:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).tokenizer
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> Dict:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).image_processor
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).qformer_tokenizer
def UpperCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(lowerCAmelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase ( self ) -> str:
_A = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_A = self.get_image_processor(do_normalize=lowerCAmelCase_ , padding_value=1.0 )
_A = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCAmelCase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase_ )
self.assertIsInstance(processor.qformer_tokenizer , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> int:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = self.get_qformer_tokenizer()
_A = InstructBlipProcessor(
tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ , qformer_tokenizer=lowerCAmelCase_ )
_A = self.prepare_image_inputs()
_A = image_processor(lowerCAmelCase_ , return_tensors="""np""" )
_A = processor(images=lowerCAmelCase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase ( self ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = self.get_qformer_tokenizer()
_A = InstructBlipProcessor(
tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ , qformer_tokenizer=lowerCAmelCase_ )
_A = """lower newer"""
_A = processor(text=lowerCAmelCase_ )
_A = tokenizer(lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ )
_A = qformer_tokenizer(lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def UpperCAmelCase ( self ) -> List[str]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = self.get_qformer_tokenizer()
_A = InstructBlipProcessor(
tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ , qformer_tokenizer=lowerCAmelCase_ )
_A = """lower newer"""
_A = self.prepare_image_inputs()
_A = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase_ ):
processor()
def UpperCAmelCase ( self ) -> int:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = self.get_qformer_tokenizer()
_A = InstructBlipProcessor(
tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ , qformer_tokenizer=lowerCAmelCase_ )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(lowerCAmelCase_ )
_A = tokenizer.batch_decode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Optional[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = self.get_qformer_tokenizer()
_A = InstructBlipProcessor(
tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ , qformer_tokenizer=lowerCAmelCase_ )
_A = """lower newer"""
_A = self.prepare_image_inputs()
_A = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 180 | 1 |
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
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = TypeVar("""DatasetType""", Dataset, IterableDataset)
def lowercase( UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = "first_exhausted" , ) -> DatasetType:
'''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(UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase_ , (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(UpperCamelCase_ )}\n"""
f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase_ ) )}']""" )
raise ValueError(
f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_ ).__name__}.""" )
if i == 0:
UpperCamelCase , UpperCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
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(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , stopping_strategy=UpperCamelCase_ )
else:
return _interleave_iterable_datasets(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , stopping_strategy=UpperCamelCase_ )
def lowercase( UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = 0 , ) -> DatasetType:
'''simple docstring'''
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase_ , (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(UpperCamelCase_ )}\n"""
f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase_ ) )}']""" )
raise ValueError(
f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_ ).__name__}.""" )
if i == 0:
UpperCamelCase , UpperCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
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(UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , axis=UpperCamelCase_ )
else:
return _concatenate_iterable_datasets(UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , axis=UpperCamelCase_ )
| 165 | from math import pi
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 165 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class lowerCamelCase__ :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 13 , SCREAMING_SNAKE_CASE = 64 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 3 , SCREAMING_SNAKE_CASE = 3 , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = 128 , SCREAMING_SNAKE_CASE=[16, 32, 64, 128] , SCREAMING_SNAKE_CASE = 7 , SCREAMING_SNAKE_CASE = 4 , SCREAMING_SNAKE_CASE = 37 , SCREAMING_SNAKE_CASE = "gelu" , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 10 , SCREAMING_SNAKE_CASE = 0.02 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 128 , SCREAMING_SNAKE_CASE = [2, 2, 2, 2] , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 2 , ):
"""simple docstring"""
snake_case : int = parent
snake_case : List[Any] = batch_size
snake_case : List[str] = image_size
snake_case : int = patch_size
snake_case : int = num_channels
snake_case : Any = is_training
snake_case : int = use_labels
snake_case : Optional[Any] = hidden_size
snake_case : str = num_hidden_layers
snake_case : Optional[int] = num_attention_heads
snake_case : Union[str, Any] = intermediate_size
snake_case : Dict = hidden_act
snake_case : Any = hidden_dropout_prob
snake_case : Optional[Any] = attention_probs_dropout_prob
snake_case : List[Any] = type_sequence_label_size
snake_case : Optional[Any] = initializer_range
snake_case : Any = encoder_stride
snake_case : Tuple = num_attention_outputs
snake_case : Dict = embed_dim
snake_case : Optional[Any] = embed_dim + 1
snake_case : Any = resolution
snake_case : int = depths
snake_case : int = hidden_sizes
snake_case : int = dim
snake_case : Tuple = mlp_expansion_ratio
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case : Optional[int] = None
if self.use_labels:
snake_case : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case : str = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ):
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case : str = TFEfficientFormerModel(config=SCREAMING_SNAKE_CASE )
snake_case : Optional[Any] = model(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case : Optional[int] = self.type_sequence_label_size
snake_case : Tuple = TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE )
snake_case : List[Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case : Tuple = 1
snake_case : Any = TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE )
snake_case : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case : Union[str, Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Any = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case : Tuple = config_and_inputs
snake_case : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase__ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
a__ : Dict = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
a__ : int = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
a__ : int = False
a__ : List[str] = False
a__ : Union[str, Any] = False
a__ : Optional[Any] = False
a__ : str = False
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : List[Any] = TFEfficientFormerModelTester(self )
snake_case : Dict = ConfigTester(
self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def lowerCamelCase_ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds" )
def lowerCamelCase_ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings" )
def lowerCamelCase_ ( self ):
"""simple docstring"""
pass
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case , snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : str = model_class(SCREAMING_SNAKE_CASE )
snake_case : Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case : Optional[int] = [*signature.parameters.keys()]
snake_case : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self ):
"""simple docstring"""
def check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
snake_case : List[str] = model_class(SCREAMING_SNAKE_CASE )
snake_case : Any = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , training=SCREAMING_SNAKE_CASE )
snake_case : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case : List[Any] = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
if hasattr(self.model_tester , "encoder_seq_length" ):
snake_case : List[Any] = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , "chunk_length" ) and self.model_tester.chunk_length > 1:
snake_case : Optional[int] = seq_length * self.model_tester.chunk_length
else:
snake_case : List[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
snake_case : List[Any] = outputs.decoder_hidden_states
self.asseretIsInstance(SCREAMING_SNAKE_CASE , (list, tuple) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
snake_case : Tuple = getattr(self.model_tester , "seq_length" , SCREAMING_SNAKE_CASE )
snake_case : Tuple = getattr(self.model_tester , "decoder_seq_length" , SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
snake_case , snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : List[Any] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case : Optional[int] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
snake_case : str = super()._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet" )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : str = TFEfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case , snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case : str = True
snake_case : Tuple = getattr(self.model_tester , "seq_length" , SCREAMING_SNAKE_CASE )
snake_case : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , SCREAMING_SNAKE_CASE )
snake_case : Optional[Any] = getattr(self.model_tester , "key_length" , SCREAMING_SNAKE_CASE )
snake_case : Tuple = getattr(self.model_tester , "chunk_length" , SCREAMING_SNAKE_CASE )
if chunk_length is not None and hasattr(self.model_tester , "num_hashes" ):
snake_case : Optional[int] = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
snake_case : Optional[int] = True
snake_case : List[Any] = False
snake_case : Optional[int] = True
snake_case : List[str] = model_class(SCREAMING_SNAKE_CASE )
snake_case : List[str] = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , training=SCREAMING_SNAKE_CASE )
snake_case : int = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case : Tuple = True
snake_case : List[str] = model_class(SCREAMING_SNAKE_CASE )
snake_case : int = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , training=SCREAMING_SNAKE_CASE )
snake_case : Any = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case , snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
snake_case : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
snake_case : Optional[Any] = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=SCREAMING_SNAKE_CASE )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
snake_case : Any = model(SCREAMING_SNAKE_CASE )
self.assertTrue(outputs_dict is not None )
def UpperCamelCase__ ( ):
snake_case : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class lowerCamelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self ):
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300" )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : str = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300" )
snake_case : List[Any] = self.default_image_processor
snake_case : Optional[Any] = prepare_img()
snake_case : int = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="tf" )
# forward pass
snake_case : Union[str, Any] = model(**SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
# verify the logits
snake_case : int = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
snake_case : Dict = tf.constant([-0.05_55, 0.48_25, -0.08_52] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Optional[int] = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300" )
snake_case : int = self.default_image_processor
snake_case : List[Any] = prepare_img()
snake_case : Optional[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="tf" )
# forward pass
snake_case : Any = model(**SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
# verify the logits
snake_case : Any = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
snake_case : Optional[int] = tf.constant([-0.13_12, 0.43_53, -1.04_99] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 148 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def UpperCamelCase__ ( lowercase__ : int ):
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 | 1 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
_snake_case : List[Any] = CLIPConfig
_snake_case : List[Any] = ['CLIPEncoderLayer']
def __init__( self : Any , lowerCAmelCase__ : List[Any] ) -> Tuple:
'''simple docstring'''
super().__init__(_SCREAMING_SNAKE_CASE )
_UpperCamelCase = CLIPVisionModelWithProjection(config.vision_config )
_UpperCamelCase = nn.Linear(config.vision_config.projection_dim , 1 )
_UpperCamelCase = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def snake_case__ ( self : List[str] , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any]=0.5 , lowerCAmelCase__ : Any=0.5 ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.vision_model(_SCREAMING_SNAKE_CASE )[0]
_UpperCamelCase = self.p_head(_SCREAMING_SNAKE_CASE )
_UpperCamelCase = nsfw_detected.flatten()
_UpperCamelCase = nsfw_detected > p_threshold
_UpperCamelCase = nsfw_detected.tolist()
if any(_SCREAMING_SNAKE_CASE ):
logger.warning(
'''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, nsfw_detected_ in enumerate(_SCREAMING_SNAKE_CASE ):
if nsfw_detected_:
_UpperCamelCase = np.zeros(images[idx].shape )
_UpperCamelCase = self.w_head(_SCREAMING_SNAKE_CASE )
_UpperCamelCase = watermark_detected.flatten()
_UpperCamelCase = watermark_detected > w_threshold
_UpperCamelCase = watermark_detected.tolist()
if any(_SCREAMING_SNAKE_CASE ):
logger.warning(
'''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, watermark_detected_ in enumerate(_SCREAMING_SNAKE_CASE ):
if watermark_detected_:
_UpperCamelCase = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 368 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(lowercase ):
for j in range(lowercase ):
_UpperCamelCase = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
lowercase__ : Optional[int] = imread('image_data/lena.jpg', 1)
# convert to its negative
lowercase__ : Union[str, Any] = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 287 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __magic_name__ ( __lowerCAmelCase):
A: Dict = CustomTokenizer
pass
| 146 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __magic_name__ ( datasets.BeamBasedBuilder):
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=lowerCamelCase__ , )
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def UpperCAmelCase__ ( self : str , lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict ) -> str:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCamelCase__ )
class __magic_name__ ( datasets.BeamBasedBuilder):
def UpperCAmelCase__ ( self : List[str] ) -> Any:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=lowerCamelCase__ , )
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : Dict ) -> int:
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCamelCase__ )
def _a ( ):
"""simple docstring"""
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def _a ( ):
"""simple docstring"""
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class __magic_name__ ( __lowerCAmelCase):
@require_beam
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase__ : List[str] = DummyBeamDataset(cache_dir=lowerCamelCase__ , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , F"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
UpperCamelCase__ : Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , lowerCamelCase__ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , lowerCamelCase__ )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
import apache_beam as beam
UpperCamelCase__ : List[Any] = beam.io.parquetio.WriteToParquet
UpperCamelCase__ : str = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase__ : List[Any] = DummyBeamDataset(cache_dir=lowerCamelCase__ , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
UpperCamelCase__ : Any = partial(lowerCamelCase__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , F"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , F"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
UpperCamelCase__ : Tuple = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , lowerCamelCase__ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , lowerCamelCase__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : Any ) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase__ : List[Any] = DummyBeamDataset(cache_dir=lowerCamelCase__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase__ : Tuple = NestedBeamDataset(cache_dir=lowerCamelCase__ , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , F"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
UpperCamelCase__ : List[Any] = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , lowerCamelCase__ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , lowerCamelCase__ )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCamelCase__ , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 146 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """efficientnet"""
def __init__( self : Tuple , UpperCAmelCase : int = 3 , UpperCAmelCase : int = 600 , UpperCAmelCase : float = 2.0 , UpperCAmelCase : float = 3.1 , UpperCAmelCase : int = 8 , UpperCAmelCase : List[int] = [3, 3, 5, 3, 5, 5, 3] , UpperCAmelCase : List[int] = [32, 16, 24, 40, 80, 112, 192] , UpperCAmelCase : List[int] = [16, 24, 40, 80, 112, 192, 320] , UpperCAmelCase : List[int] = [] , UpperCAmelCase : List[int] = [1, 2, 2, 2, 1, 2, 1] , UpperCAmelCase : List[int] = [1, 2, 2, 3, 3, 4, 1] , UpperCAmelCase : List[int] = [1, 6, 6, 6, 6, 6, 6] , UpperCAmelCase : float = 0.2_5 , UpperCAmelCase : str = "swish" , UpperCAmelCase : int = 2560 , UpperCAmelCase : str = "mean" , UpperCAmelCase : float = 0.0_2 , UpperCAmelCase : float = 0.0_0_1 , UpperCAmelCase : float = 0.9_9 , UpperCAmelCase : float = 0.5 , UpperCAmelCase : float = 0.2 , **UpperCAmelCase : int , ) -> Any:
super().__init__(**UpperCAmelCase )
lowerCamelCase__ : List[Any] = num_channels
lowerCamelCase__ : List[str] = image_size
lowerCamelCase__ : Union[str, Any] = width_coefficient
lowerCamelCase__ : Optional[Any] = depth_coefficient
lowerCamelCase__ : Union[str, Any] = depth_divisor
lowerCamelCase__ : Dict = kernel_sizes
lowerCamelCase__ : Union[str, Any] = in_channels
lowerCamelCase__ : Dict = out_channels
lowerCamelCase__ : Dict = depthwise_padding
lowerCamelCase__ : int = strides
lowerCamelCase__ : List[str] = num_block_repeats
lowerCamelCase__ : Optional[Any] = expand_ratios
lowerCamelCase__ : List[str] = squeeze_expansion_ratio
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : int = hidden_dim
lowerCamelCase__ : int = pooling_type
lowerCamelCase__ : Optional[Any] = initializer_range
lowerCamelCase__ : Any = batch_norm_eps
lowerCamelCase__ : List[Any] = batch_norm_momentum
lowerCamelCase__ : int = dropout_rate
lowerCamelCase__ : int = drop_connect_rate
lowerCamelCase__ : List[Any] = sum(UpperCAmelCase ) * 4
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = version.parse("""1.11""" )
@property
def A_ ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def A_ ( self : List[Any] ) -> float:
return 1e-5
| 352 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
}
_UpperCAmelCase : Optional[Any] = {
"""vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
"""merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}
_UpperCAmelCase : Dict = {
"""ctrl""": 2_56,
}
_UpperCAmelCase : str = {
"""Pregnancy""": 16_86_29,
"""Christianity""": 76_75,
"""Explain""": 10_64_23,
"""Fitness""": 6_34_40,
"""Saving""": 6_31_63,
"""Ask""": 2_71_71,
"""Ass""": 9_59_85,
"""Joke""": 16_35_09,
"""Questions""": 4_56_22,
"""Thoughts""": 4_96_05,
"""Retail""": 5_23_42,
"""Feminism""": 16_43_38,
"""Writing""": 1_19_92,
"""Atheism""": 19_22_63,
"""Netflix""": 4_86_16,
"""Computing""": 3_96_39,
"""Opinion""": 4_32_13,
"""Alone""": 4_49_67,
"""Funny""": 5_89_17,
"""Gaming""": 4_03_58,
"""Human""": 40_88,
"""India""": 13_31,
"""Joker""": 7_71_38,
"""Diet""": 3_62_06,
"""Legal""": 1_18_59,
"""Norman""": 49_39,
"""Tip""": 7_26_89,
"""Weight""": 5_23_43,
"""Movies""": 4_62_73,
"""Running""": 2_34_25,
"""Science""": 20_90,
"""Horror""": 3_77_93,
"""Confession""": 6_05_72,
"""Finance""": 1_22_50,
"""Politics""": 1_63_60,
"""Scary""": 19_19_85,
"""Support""": 1_26_54,
"""Technologies""": 3_25_16,
"""Teenage""": 6_61_60,
"""Event""": 3_27_69,
"""Learned""": 6_74_60,
"""Notion""": 18_27_70,
"""Wikipedia""": 3_75_83,
"""Books""": 66_65,
"""Extract""": 7_60_50,
"""Confessions""": 10_27_01,
"""Conspiracy""": 7_59_32,
"""Links""": 6_36_74,
"""Narcissus""": 15_04_25,
"""Relationship""": 5_47_66,
"""Relationships""": 13_47_96,
"""Reviews""": 4_16_71,
"""News""": 42_56,
"""Translation""": 2_68_20,
"""multilingual""": 12_84_06,
}
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase__ : Tuple = set()
lowerCamelCase__ : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase__ : Optional[Any] = char
lowerCamelCase__ : Any = set(_UpperCAmelCase )
return pairs
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = CONTROL_CODES
def __init__( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str]="<unk>" , **UpperCAmelCase : List[Any] ) -> Union[str, Any]:
super().__init__(unk_token=UpperCAmelCase , **UpperCAmelCase )
with open(UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
lowerCamelCase__ : List[Any] = json.load(UpperCAmelCase )
lowerCamelCase__ : Dict = {v: k for k, v in self.encoder.items()}
with open(UpperCAmelCase , encoding='utf-8' ) as merges_handle:
lowerCamelCase__ : Any = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase__ : Any = [tuple(merge.split() ) for merge in merges]
lowerCamelCase__ : List[str] = dict(zip(UpperCAmelCase , range(len(UpperCAmelCase ) ) ) )
lowerCamelCase__ : Any = {}
@property
def A_ ( self : int ) -> Dict:
return len(self.encoder )
def A_ ( self : List[str] ) -> str:
return dict(self.encoder , **self.added_tokens_encoder )
def A_ ( self : Any , UpperCAmelCase : Any ) -> Union[str, Any]:
if token in self.cache:
return self.cache[token]
lowerCamelCase__ : List[str] = tuple(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
lowerCamelCase__ : Optional[Any] = get_pairs(UpperCAmelCase )
if not pairs:
return token
while True:
lowerCamelCase__ : Optional[Any] = min(UpperCAmelCase , key=lambda UpperCAmelCase : self.bpe_ranks.get(UpperCAmelCase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase__ , lowerCamelCase__ : str = bigram
lowerCamelCase__ : List[Any] = []
lowerCamelCase__ : Dict = 0
while i < len(UpperCAmelCase ):
try:
lowerCamelCase__ : Any = word.index(UpperCAmelCase , UpperCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase__ : int = j
if word[i] == first and i < len(UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase__ : Dict = tuple(UpperCAmelCase )
lowerCamelCase__ : str = new_word
if len(UpperCAmelCase ) == 1:
break
else:
lowerCamelCase__ : Any = get_pairs(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = '@@ '.join(UpperCAmelCase )
lowerCamelCase__ : int = word[:-4]
lowerCamelCase__ : str = word
return word
def A_ ( self : Dict , UpperCAmelCase : Optional[int] ) -> Optional[int]:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : Tuple = re.findall(R'\S+\n?' , UpperCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(UpperCAmelCase ).split(' ' ) ) )
return split_tokens
def A_ ( self : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
return self.encoder.get(UpperCAmelCase , self.encoder.get(self.unk_token ) )
def A_ ( self : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[Any]:
return self.decoder.get(UpperCAmelCase , self.unk_token )
def A_ ( self : str , UpperCAmelCase : Tuple ) -> Optional[int]:
lowerCamelCase__ : Tuple = ' '.join(UpperCAmelCase ).replace('@@ ' , '' ).strip()
return out_string
def A_ ( self : Any , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase__ : List[Any] = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase__ : str = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCAmelCase , ensure_ascii=UpperCAmelCase ) + '\n' )
lowerCamelCase__ : str = 0
with open(UpperCAmelCase , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase__ : str = token_index
writer.write(' '.join(UpperCAmelCase ) + '\n' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 45 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 |
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 snake_case( __magic_name__ , __magic_name__=False ) -> List[str]:
'''simple docstring'''
lowercase : List[Any] = []
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 : Union[str, Any] = [(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 snake_case( __magic_name__ , __magic_name__ , __magic_name__=False ) -> Union[str, Any]:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase : Optional[int] = ''''''
else:
lowercase : List[Any] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase : Tuple = state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
lowercase : List[Any] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase : Tuple = in_proj_weight[
: config.hidden_size, :
]
lowercase : str = in_proj_bias[: config.hidden_size]
lowercase : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase : Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase : Any = in_proj_weight[
-config.hidden_size :, :
]
lowercase : Optional[int] = in_proj_bias[-config.hidden_size :]
def snake_case( __magic_name__ ) -> int:
'''simple docstring'''
lowercase : str = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__magic_name__ , __magic_name__ )
def snake_case( __magic_name__ ) -> Tuple:
'''simple docstring'''
lowercase : Any = [
'''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(__magic_name__ , __magic_name__ )
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
lowercase : List[Any] = dct.pop(__magic_name__ )
lowercase : Union[str, Any] = val
def snake_case( __magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Optional[Any] = ViTMSNConfig()
lowercase : str = 10_00
lowercase : List[str] = '''datasets/huggingface/label-files'''
lowercase : List[str] = '''imagenet-1k-id2label.json'''
lowercase : Any = json.load(open(hf_hub_download(__magic_name__ , __magic_name__ ) , '''r''' ) )
lowercase : Union[str, Any] = {int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase : Any = idalabel
lowercase : List[Any] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase : int = 3_84
lowercase : Optional[Any] = 15_36
lowercase : Tuple = 6
elif "l16" in checkpoint_url:
lowercase : Union[str, Any] = 10_24
lowercase : List[str] = 40_96
lowercase : int = 24
lowercase : Union[str, Any] = 16
lowercase : Tuple = 0.1
elif "b4" in checkpoint_url:
lowercase : Union[str, Any] = 4
elif "l7" in checkpoint_url:
lowercase : Dict = 7
lowercase : List[Any] = 10_24
lowercase : str = 40_96
lowercase : int = 24
lowercase : Dict = 16
lowercase : Tuple = 0.1
lowercase : int = ViTMSNModel(__magic_name__ )
lowercase : List[str] = torch.hub.load_state_dict_from_url(__magic_name__ , map_location='''cpu''' )['''target_encoder''']
lowercase : Any = ViTImageProcessor(size=config.image_size )
remove_projection_head(__magic_name__ )
lowercase : List[str] = create_rename_keys(__magic_name__ , base_model=__magic_name__ )
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
read_in_q_k_v(__magic_name__ , __magic_name__ , base_model=__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
lowercase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase : Optional[int] = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
lowercase : Dict = ViTImageProcessor(
size=config.image_size , image_mean=__magic_name__ , image_std=__magic_name__ )
lowercase : List[str] = image_processor(images=__magic_name__ , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
lowercase : int = model(**__magic_name__ )
lowercase : Optional[Any] = 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 : List[str] = torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
lowercase : Any = torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
lowercase : Dict = torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
lowercase : Tuple = torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
lowercase : Optional[int] = torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __magic_name__ , atol=1e-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__magic_name__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__magic_name__ )
if __name__ == "__main__":
lowerCAmelCase_ = 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.'
)
lowerCAmelCase_ = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) | 308 | 0 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
UpperCamelCase_ = [
{"dataset": "wikipedia", "config_name": "20220301.de"},
{"dataset": "wikipedia", "config_name": "20220301.en"},
{"dataset": "wikipedia", "config_name": "20220301.fr"},
{"dataset": "wikipedia", "config_name": "20220301.frr"},
{"dataset": "wikipedia", "config_name": "20220301.it"},
{"dataset": "wikipedia", "config_name": "20220301.simple"},
{"dataset": "snli", "config_name": "plain_text"},
{"dataset": "eli5", "config_name": "LFQA_reddit"},
{"dataset": "wiki40b", "config_name": "en"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
{"dataset": "natural_questions", "config_name": "default"},
]
def lowercase__( __UpperCamelCase: Optional[int]=True ):
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=SCREAMING_SNAKE_CASE ) )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = None
A : Optional[Any] = None
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
with TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_module_factory(A, cache_dir=A )
SCREAMING_SNAKE_CASE : Optional[Any] = import_main_class(dataset_module.module_path, dataset=A )
SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=A, config_name=A, hash=dataset_module.hash, )
SCREAMING_SNAKE_CASE : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A ).replace(os.sep, '/' ),
config.DATASET_INFO_FILENAME,
] )
SCREAMING_SNAKE_CASE : int = cached_path(A, cache_dir=A )
self.assertTrue(os.path.exists(A ) )
@pytest.mark.integration
def lowercase__( __UpperCamelCase: Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
SCREAMING_SNAKE_CASE : Dict = dataset_module_factory('wikipedia' ,cache_dir=__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = import_main_class(dataset_module.module_path )
SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=__UpperCamelCase ,config_name='20220301.frr' ,hash=dataset_module.hash ,)
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
SCREAMING_SNAKE_CASE : int = None
builder_instance.download_and_prepare()
SCREAMING_SNAKE_CASE : int = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowercase__( __UpperCamelCase: List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = dataset_module_factory('wikipedia' ,cache_dir=__UpperCamelCase )
SCREAMING_SNAKE_CASE : int = import_main_class(dataset_module.module_path ,dataset=__UpperCamelCase )
SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=__UpperCamelCase ,config_name='20220301.frr' ,hash=dataset_module.hash ,)
SCREAMING_SNAKE_CASE : List[str] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(__UpperCamelCase ,__UpperCamelCase )
assert "train" in ds
assert isinstance(ds['train'] ,__UpperCamelCase )
assert next(iter(ds['train'] ) )
| 246 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
"uclanlp/visualbert-vqa-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
"uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
"uclanlp/visualbert-vcr-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[int] = '''visual_bert'''
def __init__( self, A=30_522, A=768, A=512, A=12, A=12, A=3_072, A="gelu", A=0.1, A=0.1, A=512, A=2, A=0.02, A=1E-12, A=False, A=True, A=1, A=0, A=2, **A, ):
'''simple docstring'''
super().__init__(pad_token_id=A, bos_token_id=A, eos_token_id=A, **A )
SCREAMING_SNAKE_CASE : Dict = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : int = visual_embedding_dim
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : str = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = bypass_transformer
SCREAMING_SNAKE_CASE : Any = special_visual_initialize
| 246 | 1 |
"""simple docstring"""
import sys
import turtle
def lowercase_ ( _snake_case ,_snake_case ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,):
my_pen.up()
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.goto(vertexa[0] ,vertexa[1] )
if depth == 0:
return
triangle(_snake_case ,get_mid(_snake_case ,_snake_case ) ,get_mid(_snake_case ,_snake_case ) ,depth - 1 )
triangle(_snake_case ,get_mid(_snake_case ,_snake_case ) ,get_mid(_snake_case ,_snake_case ) ,depth - 1 )
triangle(_snake_case ,get_mid(_snake_case ,_snake_case ) ,get_mid(_snake_case ,_snake_case ) ,depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'Correct format for using this script: '
'python fractals.py <int:depth_for_fractal>'
)
UpperCAmelCase__ : List[Any] = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('red')
UpperCAmelCase__ : Union[str, Any] = [(-1_7_5, -1_2_5), (0, 1_7_5), (1_7_5, -1_2_5)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 25 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
a_ = True
except (ImportError, AttributeError):
a_ = object
def __UpperCAmelCase ( *__UpperCamelCase , **__UpperCamelCase ):
pass
a_ = False
a_ = logging.get_logger('transformers-cli/serving')
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Optional[Any] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(__UpperCamelCase , args.host , args.port , args.workers )
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
UpperCamelCase =42
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
class UpperCAmelCase_ ( snake_case ):
@staticmethod
def _lowerCamelCase ( UpperCamelCase_ ) -> Tuple:
__lowercase : Dict = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=UpperCamelCase_ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=UpperCamelCase_ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=UpperCamelCase_ , default=88_88 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=UpperCamelCase_ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=UpperCamelCase_ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=UpperCamelCase_ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=UpperCamelCase_ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=UpperCamelCase_ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=UpperCamelCase_ )
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Any:
__lowercase : List[Any] = pipeline
__lowercase : str = host
__lowercase : List[str] = port
__lowercase : str = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
__lowercase : int = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=UpperCamelCase_ , response_class=UpperCamelCase_ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=UpperCamelCase_ , response_class=UpperCamelCase_ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=UpperCamelCase_ , response_class=UpperCamelCase_ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=UpperCamelCase_ , response_class=UpperCamelCase_ , methods=['''POST'''] , ),
] , timeout=6_00 , )
def _lowerCamelCase ( self ) -> Union[str, Any]:
run(self._app , host=self.host , port=self.port , workers=self.workers )
def _lowerCamelCase ( self ) -> Tuple:
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def _lowerCamelCase ( self , UpperCamelCase_ = Body(UpperCamelCase_ , embed=UpperCamelCase_ ) , UpperCamelCase_ = Body(UpperCamelCase_ , embed=UpperCamelCase_ ) ) -> Optional[int]:
try:
__lowercase : Any = self._pipeline.tokenizer.tokenize(UpperCamelCase_ )
if return_ids:
__lowercase : Dict = self._pipeline.tokenizer.convert_tokens_to_ids(UpperCamelCase_ )
return ServeTokenizeResult(tokens=UpperCamelCase_ , tokens_ids=UpperCamelCase_ )
else:
return ServeTokenizeResult(tokens=UpperCamelCase_ )
except Exception as e:
raise HTTPException(status_code=5_00 , detail={'''model''': '''''', '''error''': str(UpperCamelCase_ )} )
def _lowerCamelCase ( self , UpperCamelCase_ = Body(UpperCamelCase_ , embed=UpperCamelCase_ ) , UpperCamelCase_ = Body(UpperCamelCase_ , embed=UpperCamelCase_ ) , UpperCamelCase_ = Body(UpperCamelCase_ , embed=UpperCamelCase_ ) , ) -> Dict:
try:
__lowercase : Tuple = self._pipeline.tokenizer.decode(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return ServeDeTokenizeResult(model='''''' , text=UpperCamelCase_ )
except Exception as e:
raise HTTPException(status_code=5_00 , detail={'''model''': '''''', '''error''': str(UpperCamelCase_ )} )
async def _lowerCamelCase ( self , UpperCamelCase_=Body(UpperCamelCase_ , embed=UpperCamelCase_ ) ) -> Union[str, Any]:
# Check we don't have empty string
if len(UpperCamelCase_ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
__lowercase : Optional[Any] = self._pipeline(UpperCamelCase_ )
return ServeForwardResult(output=UpperCamelCase_ )
except Exception as e:
raise HTTPException(5_00 , {'''error''': str(UpperCamelCase_ )} )
| 249 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
_a : Union[str, Any] = logging.getLogger(__name__)
_a : Dict = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Union[str, Any] = "bertabs"
def __init__( self , a__=30522 , a__=512 , a__=6 , a__=512 , a__=8 , a__=512 , a__=0.2 , a__=6 , a__=768 , a__=8 , a__=2048 , a__=0.2 , **a__ , ):
super().__init__(**a__ )
_lowerCAmelCase : Any = vocab_size
_lowerCAmelCase : Tuple = max_pos
_lowerCAmelCase : Tuple = enc_layers
_lowerCAmelCase : Optional[Any] = enc_hidden_size
_lowerCAmelCase : Tuple = enc_heads
_lowerCAmelCase : Optional[Any] = enc_ff_size
_lowerCAmelCase : Union[str, Any] = enc_dropout
_lowerCAmelCase : Dict = dec_layers
_lowerCAmelCase : int = dec_hidden_size
_lowerCAmelCase : Optional[Any] = dec_heads
_lowerCAmelCase : Union[str, Any] = dec_ff_size
_lowerCAmelCase : str = dec_dropout
| 368 | """simple docstring"""
_a : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a : List[Any] = [{'type': 'code', 'content': INSTALL_CONTENT}]
_a : Union[str, Any] = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 126 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class A ( A_ ):
UpperCamelCase_ : List[Any] =(DPMSolverSDEScheduler,)
UpperCamelCase_ : Optional[Any] =10
def _A (self , **lowerCAmelCase ):
__lowercase= {
'num_train_timesteps': 1_1_0_0,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
'noise_sampler_seed': 0,
}
config.update(**lowerCAmelCase )
return config
def _A (self ):
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase )
def _A (self ):
for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] , [0.00_02, 0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase , beta_end=lowerCAmelCase )
def _A (self ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase )
def _A (self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase )
def _A (self ):
__lowercase= self.scheduler_classes[0]
__lowercase= self.get_scheduler_config()
__lowercase= scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
__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 )
__lowercase= output.prev_sample
__lowercase= torch.sum(torch.abs(lowerCAmelCase ) )
__lowercase= torch.mean(torch.abs(lowerCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75 ) < 1E-2
assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06 ) < 1E-2
assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52 ) < 1E-3
else:
assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62 ) < 1E-2
assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26 ) < 1E-3
def _A (self ):
__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= 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 )
__lowercase= output.prev_sample
__lowercase= torch.sum(torch.abs(lowerCAmelCase ) )
__lowercase= torch.mean(torch.abs(lowerCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53 ) < 1E-2
assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03 ) < 1E-2
assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97 ) < 1E-3
else:
assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25 ) < 1E-2
assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21 ) < 1E-3
def _A (self ):
__lowercase= self.scheduler_classes[0]
__lowercase= self.get_scheduler_config()
__lowercase= scheduler_class(**lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase )
__lowercase= self.dummy_model()
__lowercase= self.dummy_sample_deter.to(lowerCAmelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowercase= scheduler.scale_model_input(lowerCAmelCase , lowerCAmelCase )
__lowercase= model(lowerCAmelCase , lowerCAmelCase )
__lowercase= scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
__lowercase= output.prev_sample
__lowercase= torch.sum(torch.abs(lowerCAmelCase ) )
__lowercase= torch.mean(torch.abs(lowerCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38 ) < 1E-2
assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12 ) < 1E-2
assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71 ) < 1E-3
else:
assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62 ) < 1E-2
assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26 ) < 1E-3
def _A (self ):
__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= self.dummy_model()
__lowercase= self.dummy_sample_deter.to(lowerCAmelCase ) * scheduler.init_noise_sigma
__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 )
__lowercase= output.prev_sample
__lowercase= torch.sum(torch.abs(lowerCAmelCase ) )
__lowercase= torch.mean(torch.abs(lowerCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2
else:
assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72 ) < 1E-2
assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11 ) < 1E-2
| 295 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A :
def __init__(self , lowerCAmelCase , lowerCAmelCase=1_3 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=9_9 , lowerCAmelCase=3_2 , lowerCAmelCase=5 , lowerCAmelCase=4 , lowerCAmelCase=3_7 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_1_2 , lowerCAmelCase=1_6 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=None , ):
__lowercase= parent
__lowercase= batch_size
__lowercase= seq_length
__lowercase= is_training
__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
__lowercase= self.vocab_size - 1
def _A (self ):
__lowercase= ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__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= OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
__lowercase= ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ):
__lowercase= OpenAIGPTModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowercase= model(lowerCAmelCase , token_type_ids=lowerCAmelCase , head_mask=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) )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ):
__lowercase= OpenAIGPTLMHeadModel(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowercase= model(lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ):
__lowercase= OpenAIGPTDoubleHeadsModel(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowercase= model(lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ):
__lowercase= self.num_labels
__lowercase= OpenAIGPTForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowercase= ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase= model(lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A (self ):
__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,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A ( A_ , A_ , A_ , unittest.TestCase ):
UpperCamelCase_ : Optional[Any] =(
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
UpperCamelCase_ : Tuple =(
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
UpperCamelCase_ : List[str] =(
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False ):
__lowercase= super()._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
__lowercase= torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase , )
__lowercase= inputs_dict['labels']
__lowercase= inputs_dict['labels']
__lowercase= torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowerCAmelCase , )
__lowercase= torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase )
return inputs_dict
def _A (self ):
__lowercase= OpenAIGPTModelTester(self )
__lowercase= ConfigTester(self , config_class=lowerCAmelCase , n_embd=3_7 )
def _A (self ):
self.config_tester.run_common_tests()
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase )
@slow
def _A (self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase= OpenAIGPTModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@require_torch
class A ( unittest.TestCase ):
@slow
def _A (self ):
__lowercase= OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(lowerCAmelCase )
__lowercase= torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]] , dtype=torch.long , device=lowerCAmelCase ) # the president is
__lowercase= [
4_8_1,
4_7_3_5,
5_4_4,
2_4_6,
9_6_3,
8_7_0,
7_6_2,
2_3_9,
2_4_4,
4_0_4_7_7,
2_4_4,
2_4_9,
7_1_9,
8_8_1,
4_8_7,
5_4_4,
2_4_0,
2_4_4,
6_0_3,
4_8_1,
] # the president is a very good man. " \n " i\'m sure he is, " said the
__lowercase= model.generate(lowerCAmelCase , do_sample=lowerCAmelCase )
self.assertListEqual(output_ids[0].tolist() , lowerCAmelCase )
| 295 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
__a = ["model.decoder.embed_positions.weights"]
def __snake_case( _lowerCAmelCase ) -> Optional[Any]:
if "emb" in name:
snake_case__ : Dict = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
snake_case__ : Tuple = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
snake_case__ : Tuple = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
snake_case__ : Optional[int] = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
snake_case__ : Any = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
snake_case__ : Any = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
snake_case__ : Dict = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
snake_case__ : Optional[Any] = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
snake_case__ : Dict = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
snake_case__ : List[Any] = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
snake_case__ : Union[str, Any] = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple[Dict, Dict]:
snake_case__ : Dict = list(state_dict.keys() )
snake_case__ : Union[str, Any] = {}
for key in keys:
snake_case__ : List[Any] = state_dict.pop(__UpperCAmelCase )
snake_case__ : List[Any] = rename_keys(__UpperCAmelCase )
if "in_proj_weight" in key:
# split fused qkv proj
snake_case__ : Union[str, Any] = val[:hidden_size, :]
snake_case__ : List[str] = val[hidden_size : 2 * hidden_size, :]
snake_case__ : Dict = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
snake_case__ : Optional[Any] = val
else:
snake_case__ : Dict = val
return state_dict, enc_dec_proj_state_dict
def __snake_case( _lowerCAmelCase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
snake_case__ : Dict = 1_024
snake_case__ : str = 24
snake_case__ : Optional[int] = 16
elif checkpoint == "medium":
snake_case__ : int = 1_536
snake_case__ : List[Any] = 48
snake_case__ : Optional[Any] = 24
elif checkpoint == "large":
snake_case__ : Optional[Any] = 2_048
snake_case__ : Optional[Any] = 48
snake_case__ : Union[str, Any] = 32
else:
raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
snake_case__ : Optional[int] = MusicgenDecoderConfig(
hidden_size=__UpperCAmelCase , ffn_dim=hidden_size * 4 , num_hidden_layers=__UpperCAmelCase , num_attention_heads=__UpperCAmelCase , )
return config
@torch.no_grad()
def __snake_case( _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase="cpu" ) -> Union[str, Any]:
snake_case__ : Dict = MusicGen.get_pretrained(__UpperCAmelCase , device=__UpperCAmelCase )
snake_case__ : Tuple = decoder_config_from_checkpoint(__UpperCAmelCase )
snake_case__ : Dict = fairseq_model.lm.state_dict()
snake_case__ : Tuple = rename_state_dict(
__UpperCAmelCase , hidden_size=decoder_config.hidden_size )
snake_case__ : Optional[Any] = TaEncoderModel.from_pretrained("""t5-base""" )
snake_case__ : Any = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
snake_case__ : Optional[int] = MusicgenForCausalLM(__UpperCAmelCase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
snake_case__ : Tuple = decoder.load_state_dict(__UpperCAmelCase , strict=__UpperCAmelCase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
raise ValueError(f"Missing key(s) in state_dict: {missing_keys}" )
if len(__UpperCAmelCase ) > 0:
raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}" )
# init the composite model
snake_case__ : int = MusicgenForConditionalGeneration(text_encoder=__UpperCAmelCase , audio_encoder=__UpperCAmelCase , decoder=__UpperCAmelCase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__UpperCAmelCase )
# check we can do a forward pass
snake_case__ : Any = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
snake_case__ : int = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
snake_case__ : Union[str, Any] = model(input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase ).logits
if logits.shape != (8, 1, 2_048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
snake_case__ : Any = AutoTokenizer.from_pretrained("""t5-base""" )
snake_case__ : Optional[Any] = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
snake_case__ : Optional[Any] = MusicgenProcessor(feature_extractor=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
# set the appropriate bos/pad token ids
snake_case__ : int = 2_048
snake_case__ : Tuple = 2_048
# set other default generation config params
snake_case__ : Any = int(30 * audio_encoder.config.frame_rate )
snake_case__ : str = True
snake_case__ : Optional[int] = 3.0
if pytorch_dump_folder is not None:
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}" )
model.save_pretrained(__UpperCAmelCase )
processor.save_pretrained(__UpperCAmelCase )
if repo_id:
logger.info(f"Pushing model {checkpoint} to {repo_id}" )
model.push_to_hub(__UpperCAmelCase )
processor.push_to_hub(__UpperCAmelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__a = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 352 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 0 |
from __future__ import annotations
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!" )
SCREAMING_SNAKE_CASE_: Optional[Any] = number_of_bytes // partitions
SCREAMING_SNAKE_CASE_: Optional[int] = []
for i in range(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = i * bytes_per_partition + 1
SCREAMING_SNAKE_CASE_: Any = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f"{start_bytes}-{end_bytes}" )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 |
lowerCAmelCase : str = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 253 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A__ = logging.get_logger(__name__)
A__ = {
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
# See all BART models at https://huggingface.co/models?filter=bart
}
class a ( __lowerCamelCase ):
__lowerCAmelCase : List[str] = """bart"""
__lowerCAmelCase : Any = ["""past_key_values"""]
__lowerCAmelCase : List[Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self :int ,__lowercase :Union[str, Any]=5_0_2_6_5 ,__lowercase :Optional[int]=1_0_2_4 ,__lowercase :int=1_2 ,__lowercase :Tuple=4_0_9_6 ,__lowercase :str=1_6 ,__lowercase :List[Any]=1_2 ,__lowercase :str=4_0_9_6 ,__lowercase :List[str]=1_6 ,__lowercase :Optional[int]=0.0 ,__lowercase :List[str]=0.0 ,__lowercase :int="gelu" ,__lowercase :int=1_0_2_4 ,__lowercase :Any=0.1 ,__lowercase :Optional[Any]=0.0 ,__lowercase :List[Any]=0.0 ,__lowercase :Tuple=0.02 ,__lowercase :List[str]=0.0 ,__lowercase :int=False ,__lowercase :Any=True ,__lowercase :List[str]=3 ,__lowercase :List[Any]=1 ,__lowercase :List[str]=0 ,__lowercase :List[str]=2 ,__lowercase :Union[str, Any]=True ,__lowercase :List[Any]=2 ,__lowercase :Dict=2 ,**__lowercase :List[str] ,):
snake_case__ : Union[str, Any] = vocab_size
snake_case__ : Tuple = max_position_embeddings
snake_case__ : List[Any] = d_model
snake_case__ : Any = encoder_ffn_dim
snake_case__ : int = encoder_layers
snake_case__ : Union[str, Any] = encoder_attention_heads
snake_case__ : List[str] = decoder_ffn_dim
snake_case__ : Any = decoder_layers
snake_case__ : Union[str, Any] = decoder_attention_heads
snake_case__ : int = dropout
snake_case__ : Optional[int] = attention_dropout
snake_case__ : str = activation_dropout
snake_case__ : List[Any] = activation_function
snake_case__ : Any = init_std
snake_case__ : Union[str, Any] = encoder_layerdrop
snake_case__ : Optional[int] = decoder_layerdrop
snake_case__ : List[Any] = classifier_dropout
snake_case__ : Tuple = use_cache
snake_case__ : List[str] = encoder_layers
snake_case__ : Dict = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=__lowercase ,pad_token_id=__lowercase ,bos_token_id=__lowercase ,eos_token_id=__lowercase ,is_encoder_decoder=__lowercase ,decoder_start_token_id=__lowercase ,forced_eos_token_id=__lowercase ,**__lowercase ,)
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' ,__lowercase ):
snake_case__ : int = self.bos_token_id
warnings.warn(
F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
'''The config can simply be saved and uploaded again to be fixed.''' )
class a ( __lowerCamelCase ):
@property
def __lowerCamelCase ( self :Optional[int] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : Optional[Any] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case__ : Union[str, Any] = {0: '''batch'''}
snake_case__ : Any = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case__ : Any = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case__ : Dict = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__lowercase ,direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case__ : List[str] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case__ , snake_case__ : Dict = self.num_layers
for i in range(__lowercase ):
snake_case__ : int = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case__ : Tuple = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case__ : Optional[Any] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def __lowerCamelCase ( self :Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : List[str] = super().outputs
else:
snake_case__ : List[str] = super(__lowercase ,self ).outputs
if self.use_past:
snake_case__ , snake_case__ : Any = self.num_layers
for i in range(__lowercase ):
snake_case__ : Optional[int] = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case__ : List[Any] = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __lowerCamelCase ( self :Optional[Any] ,__lowercase :PreTrainedTokenizer ,__lowercase :int = -1 ,__lowercase :int = -1 ,__lowercase :bool = False ,__lowercase :Optional[TensorType] = None ,):
snake_case__ : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
# Generate decoder inputs
snake_case__ : List[Any] = seq_length if not self.use_past else 1
snake_case__ : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
snake_case__ : Any = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
snake_case__ : List[str] = dict(**__lowercase ,**__lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case__ , snake_case__ : Union[str, Any] = common_inputs['''input_ids'''].shape
snake_case__ : List[str] = common_inputs['''decoder_input_ids'''].shape[1]
snake_case__ , snake_case__ : Dict = self.num_attention_heads
snake_case__ : List[str] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case__ : Optional[int] = decoder_seq_length + 3
snake_case__ : Dict = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case__ : List[Any] = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase ,__lowercase )] ,dim=1 )
snake_case__ : Any = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case__ , snake_case__ : List[Any] = self.num_layers
snake_case__ : List[Any] = min(__lowercase ,__lowercase )
snake_case__ : Dict = max(__lowercase ,__lowercase ) - min_num_layers
snake_case__ : Union[str, Any] = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
) )
# TODO: test this.
snake_case__ : Optional[Any] = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__lowercase ,__lowercase ):
common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
return common_inputs
def __lowerCamelCase ( self :List[Any] ,__lowercase :PreTrainedTokenizer ,__lowercase :int = -1 ,__lowercase :int = -1 ,__lowercase :bool = False ,__lowercase :Optional[TensorType] = None ,):
snake_case__ : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case__ , snake_case__ : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case__ : Dict = seqlen + 2
snake_case__ , snake_case__ : Tuple = self.num_layers
snake_case__ , snake_case__ : List[str] = self.num_attention_heads
snake_case__ : Optional[Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case__ : int = common_inputs['''attention_mask'''].dtype
snake_case__ : int = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__lowercase ,__lowercase ,dtype=__lowercase )] ,dim=1 )
snake_case__ : Union[str, Any] = [
(torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
]
return common_inputs
def __lowerCamelCase ( self :str ,__lowercase :PreTrainedTokenizer ,__lowercase :int = -1 ,__lowercase :int = -1 ,__lowercase :bool = False ,__lowercase :Optional[TensorType] = None ,):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case__ : Optional[int] = compute_effective_axis_dimension(
__lowercase ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case__ : str = tokenizer.num_special_tokens_to_add(__lowercase )
snake_case__ : int = compute_effective_axis_dimension(
__lowercase ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=__lowercase )
# Generate dummy inputs according to compute batch and sequence
snake_case__ : Union[str, Any] = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case__ : Optional[Any] = dict(tokenizer(__lowercase ,return_tensors=__lowercase ) )
return common_inputs
def __lowerCamelCase ( self :int ,__lowercase :PreTrainedTokenizer ,__lowercase :int = -1 ,__lowercase :int = -1 ,__lowercase :bool = False ,__lowercase :Optional[TensorType] = None ,):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__lowercase ,batch_size=__lowercase ,seq_length=__lowercase ,is_pair=__lowercase ,framework=__lowercase )
elif self.task == "causal-lm":
snake_case__ : int = self._generate_dummy_inputs_for_causal_lm(
__lowercase ,batch_size=__lowercase ,seq_length=__lowercase ,is_pair=__lowercase ,framework=__lowercase )
else:
snake_case__ : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__lowercase ,batch_size=__lowercase ,seq_length=__lowercase ,is_pair=__lowercase ,framework=__lowercase )
return common_inputs
def __lowerCamelCase ( self :Tuple ,__lowercase :Optional[int] ,__lowercase :List[str] ,__lowercase :Optional[int] ,__lowercase :Tuple ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : Optional[Any] = super()._flatten_past_key_values_(__lowercase ,__lowercase ,__lowercase ,__lowercase )
else:
snake_case__ : int = super(__lowercase ,self )._flatten_past_key_values_(
__lowercase ,__lowercase ,__lowercase ,__lowercase )
| 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
snake_case__ : str = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
snake_case__ : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
snake_case__ : Dict = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
snake_case__ : str = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
snake_case__ : Dict = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
snake_case__ : Union[str, Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
snake_case__ : List[str] = dp[i - 1][j]
else:
snake_case__ : Union[str, Any] = 0
else:
snake_case__ : Tuple = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A__ = '''aab'''
A__ = '''c*a*b'''
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : List[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[Any] = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
lowerCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 2 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( snake_case__ ):
'''simple docstring'''
return (data["data"], data["target"])
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = XGBClassifier()
classifier.fit(snake_case__ , snake_case__ )
return classifier
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = load_iris()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = data_handling(snake_case__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = train_test_split(
snake_case__ , snake_case__ , test_size=0.25 )
SCREAMING_SNAKE_CASE__ = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
SCREAMING_SNAKE_CASE__ = xgboost(snake_case__ , snake_case__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
snake_case__ , snake_case__ , snake_case__ , display_labels=snake_case__ , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 165 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return number | (1 << position)
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return number & ~(1 << position)
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return number ^ (1 << position)
def __a ( UpperCAmelCase , UpperCAmelCase ) ->bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : Any = OrderedDict(
[
# Base model mapping
('albert', 'FlaxAlbertModel'),
('bart', 'FlaxBartModel'),
('beit', 'FlaxBeitModel'),
('bert', 'FlaxBertModel'),
('big_bird', 'FlaxBigBirdModel'),
('blenderbot', 'FlaxBlenderbotModel'),
('blenderbot-small', 'FlaxBlenderbotSmallModel'),
('clip', 'FlaxCLIPModel'),
('distilbert', 'FlaxDistilBertModel'),
('electra', 'FlaxElectraModel'),
('gpt-sw3', 'FlaxGPT2Model'),
('gpt2', 'FlaxGPT2Model'),
('gpt_neo', 'FlaxGPTNeoModel'),
('gptj', 'FlaxGPTJModel'),
('longt5', 'FlaxLongT5Model'),
('marian', 'FlaxMarianModel'),
('mbart', 'FlaxMBartModel'),
('mt5', 'FlaxMT5Model'),
('opt', 'FlaxOPTModel'),
('pegasus', 'FlaxPegasusModel'),
('regnet', 'FlaxRegNetModel'),
('resnet', 'FlaxResNetModel'),
('roberta', 'FlaxRobertaModel'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
('roformer', 'FlaxRoFormerModel'),
('t5', 'FlaxT5Model'),
('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
('vit', 'FlaxViTModel'),
('wav2vec2', 'FlaxWav2Vec2Model'),
('whisper', 'FlaxWhisperModel'),
('xglm', 'FlaxXGLMModel'),
('xlm-roberta', 'FlaxXLMRobertaModel'),
]
)
_lowerCamelCase : Optional[Any] = OrderedDict(
[
# Model for pre-training mapping
('albert', 'FlaxAlbertForPreTraining'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForPreTraining'),
('big_bird', 'FlaxBigBirdForPreTraining'),
('electra', 'FlaxElectraForPreTraining'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('t5', 'FlaxT5ForConditionalGeneration'),
('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
_lowerCamelCase : int = OrderedDict(
[
# Model for Masked LM mapping
('albert', 'FlaxAlbertForMaskedLM'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForMaskedLM'),
('big_bird', 'FlaxBigBirdForMaskedLM'),
('distilbert', 'FlaxDistilBertForMaskedLM'),
('electra', 'FlaxElectraForMaskedLM'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
_lowerCamelCase : List[str] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('bart', 'FlaxBartForConditionalGeneration'),
('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
('encoder-decoder', 'FlaxEncoderDecoderModel'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('marian', 'FlaxMarianMTModel'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('pegasus', 'FlaxPegasusForConditionalGeneration'),
('t5', 'FlaxT5ForConditionalGeneration'),
]
)
_lowerCamelCase : int = OrderedDict(
[
# Model for Image-classsification
('beit', 'FlaxBeitForImageClassification'),
('regnet', 'FlaxRegNetForImageClassification'),
('resnet', 'FlaxResNetForImageClassification'),
('vit', 'FlaxViTForImageClassification'),
]
)
_lowerCamelCase : int = OrderedDict(
[
('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
]
)
_lowerCamelCase : Optional[int] = OrderedDict(
[
# Model for Causal LM mapping
('bart', 'FlaxBartForCausalLM'),
('bert', 'FlaxBertForCausalLM'),
('big_bird', 'FlaxBigBirdForCausalLM'),
('electra', 'FlaxElectraForCausalLM'),
('gpt-sw3', 'FlaxGPT2LMHeadModel'),
('gpt2', 'FlaxGPT2LMHeadModel'),
('gpt_neo', 'FlaxGPTNeoForCausalLM'),
('gptj', 'FlaxGPTJForCausalLM'),
('opt', 'FlaxOPTForCausalLM'),
('roberta', 'FlaxRobertaForCausalLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
('xglm', 'FlaxXGLMForCausalLM'),
('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
]
)
_lowerCamelCase : Optional[Any] = OrderedDict(
[
# Model for Sequence Classification mapping
('albert', 'FlaxAlbertForSequenceClassification'),
('bart', 'FlaxBartForSequenceClassification'),
('bert', 'FlaxBertForSequenceClassification'),
('big_bird', 'FlaxBigBirdForSequenceClassification'),
('distilbert', 'FlaxDistilBertForSequenceClassification'),
('electra', 'FlaxElectraForSequenceClassification'),
('mbart', 'FlaxMBartForSequenceClassification'),
('roberta', 'FlaxRobertaForSequenceClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
('roformer', 'FlaxRoFormerForSequenceClassification'),
('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
]
)
_lowerCamelCase : Any = OrderedDict(
[
# Model for Question Answering mapping
('albert', 'FlaxAlbertForQuestionAnswering'),
('bart', 'FlaxBartForQuestionAnswering'),
('bert', 'FlaxBertForQuestionAnswering'),
('big_bird', 'FlaxBigBirdForQuestionAnswering'),
('distilbert', 'FlaxDistilBertForQuestionAnswering'),
('electra', 'FlaxElectraForQuestionAnswering'),
('mbart', 'FlaxMBartForQuestionAnswering'),
('roberta', 'FlaxRobertaForQuestionAnswering'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
('roformer', 'FlaxRoFormerForQuestionAnswering'),
('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
]
)
_lowerCamelCase : Union[str, Any] = OrderedDict(
[
# Model for Token Classification mapping
('albert', 'FlaxAlbertForTokenClassification'),
('bert', 'FlaxBertForTokenClassification'),
('big_bird', 'FlaxBigBirdForTokenClassification'),
('distilbert', 'FlaxDistilBertForTokenClassification'),
('electra', 'FlaxElectraForTokenClassification'),
('roberta', 'FlaxRobertaForTokenClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
('roformer', 'FlaxRoFormerForTokenClassification'),
('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
]
)
_lowerCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
('albert', 'FlaxAlbertForMultipleChoice'),
('bert', 'FlaxBertForMultipleChoice'),
('big_bird', 'FlaxBigBirdForMultipleChoice'),
('distilbert', 'FlaxDistilBertForMultipleChoice'),
('electra', 'FlaxElectraForMultipleChoice'),
('roberta', 'FlaxRobertaForMultipleChoice'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
('roformer', 'FlaxRoFormerForMultipleChoice'),
('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
]
)
_lowerCamelCase : int = OrderedDict(
[
('bert', 'FlaxBertForNextSentencePrediction'),
]
)
_lowerCamelCase : Union[str, Any] = OrderedDict(
[
('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
]
)
_lowerCamelCase : Any = OrderedDict(
[
('whisper', 'FlaxWhisperForAudioClassification'),
]
)
_lowerCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_lowerCamelCase : Dict = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_lowerCamelCase : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_lowerCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_lowerCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_lowerCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_lowerCamelCase : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_lowerCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_lowerCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_lowerCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_lowerCamelCase : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_MAPPING
_lowerCamelCase : Optional[Any] = auto_class_update(FlaxAutoModel)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
_lowerCamelCase : List[str] = auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_lowerCamelCase : List[Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
_lowerCamelCase : List[str] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_lowerCamelCase : Tuple = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_lowerCamelCase : Tuple = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_lowerCamelCase : Any = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_lowerCamelCase : str = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_lowerCamelCase : Tuple = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_lowerCamelCase : List[Any] = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_lowerCamelCase : Union[str, Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='image classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_lowerCamelCase : Optional[int] = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_lowerCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)
| 337 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_A = logging.get_logger(__name__)
_A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_A = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_A = {
'''allenai/led-base-16384''': 16_384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowerCamelCase__ ( ) -> int:
UpperCamelCase_ = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
UpperCamelCase_ = bs[:]
UpperCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(a__ )
cs.append(2**8 + n )
n += 1
UpperCamelCase_ = [chr(a__ ) for n in cs]
return dict(zip(a__ , a__ ) )
def lowerCamelCase__ ( a__ : List[Any] ) -> Union[str, Any]:
UpperCamelCase_ = set()
UpperCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCamelCase_ = char
return pairs
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : str = VOCAB_FILES_NAMES
A__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Any = ["""input_ids""", """attention_mask"""]
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="replace" , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase="</s>" , __UpperCamelCase="<s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<mask>" , __UpperCamelCase=False , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else bos_token
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else eos_token
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else sep_token
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else cls_token
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else unk_token
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , **__UpperCamelCase , )
with open(__UpperCamelCase , encoding="""utf-8""" ) as vocab_handle:
UpperCamelCase_ = json.load(__UpperCamelCase )
UpperCamelCase_ = {v: k for k, v in self.encoder.items()}
UpperCamelCase_ = errors # how to handle errors in decoding
UpperCamelCase_ = bytes_to_unicode()
UpperCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(__UpperCamelCase , encoding="""utf-8""" ) as merges_handle:
UpperCamelCase_ = merges_handle.read().split("""\n""" )[1:-1]
UpperCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
UpperCamelCase_ = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
UpperCamelCase_ = {}
UpperCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCamelCase_ = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def lowerCamelCase_ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowerCamelCase_ ( self ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , __UpperCamelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
UpperCamelCase_ = tuple(__UpperCamelCase )
UpperCamelCase_ = get_pairs(__UpperCamelCase )
if not pairs:
return token
while True:
UpperCamelCase_ = min(__UpperCamelCase , key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCamelCase_ , UpperCamelCase_ = bigram
UpperCamelCase_ = []
UpperCamelCase_ = 0
while i < len(__UpperCamelCase ):
try:
UpperCamelCase_ = word.index(__UpperCamelCase , __UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCamelCase_ = j
if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCamelCase_ = tuple(__UpperCamelCase )
UpperCamelCase_ = new_word
if len(__UpperCamelCase ) == 1:
break
else:
UpperCamelCase_ = get_pairs(__UpperCamelCase )
UpperCamelCase_ = """ """.join(__UpperCamelCase )
UpperCamelCase_ = word
return word
def lowerCamelCase_ ( self , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = []
for token in re.findall(self.pat , __UpperCamelCase ):
UpperCamelCase_ = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase ).split(""" """ ) )
return bpe_tokens
def lowerCamelCase_ ( self , __UpperCamelCase ):
"""simple docstring"""
return self.encoder.get(__UpperCamelCase , self.encoder.get(self.unk_token ) )
def lowerCamelCase_ ( self , __UpperCamelCase ):
"""simple docstring"""
return self.decoder.get(__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = """""".join(__UpperCamelCase )
UpperCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
if not os.path.isdir(__UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
__UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCamelCase_ = os.path.join(
__UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCamelCase , ensure_ascii=__UpperCamelCase ) + """\n""" )
UpperCamelCase_ = 0
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
""" Please check that the tokenizer is not corrupted!""" )
UpperCamelCase_ = token_index
writer.write(""" """.join(__UpperCamelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase_ = [self.cls_token_id]
UpperCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase ) > 0 and not text[0].isspace()):
UpperCamelCase_ = """ """ + text
return (text, kwargs)
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = PaddingStrategy.DO_NOT_PAD , __UpperCamelCase = None , __UpperCamelCase = None , ):
"""simple docstring"""
UpperCamelCase_ = super()._pad(
encoded_inputs=__UpperCamelCase , max_length=__UpperCamelCase , padding_strategy=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
# Load from model defaults
if return_attention_mask is None:
UpperCamelCase_ = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
UpperCamelCase_ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
UpperCamelCase_ = len(encoded_inputs["""global_attention_mask"""] ) != len(__UpperCamelCase )
if needs_to_be_padded:
UpperCamelCase_ = len(__UpperCamelCase ) - 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`
UpperCamelCase_ = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
UpperCamelCase_ = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 122 |
_A = [0, 2, 4, 6, 8]
_A = [1, 3, 5, 7, 9]
def lowerCamelCase__ ( a__ : int , a__ : int , a__ : list[int] , a__ : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCamelCase_ = 0
for digit in range(10 ):
UpperCamelCase_ = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a__ , a__ )
return result
UpperCamelCase_ = 0
for digita in range(10 ):
UpperCamelCase_ = digita
if (remainder + digita) % 2 == 0:
UpperCamelCase_ = ODD_DIGITS
else:
UpperCamelCase_ = EVEN_DIGITS
for digita in other_parity_digits:
UpperCamelCase_ = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a__ , a__ , )
return result
def lowerCamelCase__ ( a__ : int = 9 ) -> int:
UpperCamelCase_ = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a__ , 0 , [0] * length , a__ )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 122 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowerCAmelCase__ :List[str] = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
('''beta''', '''bias'''),
('''gamma''', '''weight'''),
('''pegasus''', '''model'''),
]
lowerCAmelCase__ :List[Any] = [
('''.output.dense''', '''.fc2'''),
('''intermediate.LayerNorm''', '''final_layer_norm'''),
('''intermediate.dense''', '''fc1'''),
]
lowerCAmelCase__ :str = (
INIT_COMMON
+ [
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.out_proj'''),
('''attention.self''', '''self_attn'''),
('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
('''attention.encdec''', '''encoder_attn'''),
('''key''', '''k_proj'''),
('''value''', '''v_proj'''),
('''query''', '''q_proj'''),
('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
]
+ END_COMMON
)
lowerCAmelCase__ :Optional[int] = (
INIT_COMMON
+ [
('''embeddings.word_embeddings''', '''shared.weight'''),
('''embeddings.position_embeddings''', '''embed_positions.weight'''),
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.output'''),
('''attention.self''', '''self_attn.self'''),
('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
]
+ END_COMMON
)
lowerCAmelCase__ :Tuple = [
'''encdec/key/bias''',
'''encdec/query/bias''',
'''encdec/value/bias''',
'''self/key/bias''',
'''self/query/bias''',
'''self/value/bias''',
'''encdec_output/dense/bias''',
'''attention/output/dense/bias''',
]
def lowerCAmelCase__ ( a__: Any , a__: Optional[Any] ) -> List[str]:
'''simple docstring'''
for tf_name, hf_name in patterns:
_UpperCAmelCase = k.replace(a__ , a__ )
return k
def lowerCAmelCase__ ( a__: dict , a__: dict ) -> BigBirdPegasusForConditionalGeneration:
'''simple docstring'''
_UpperCAmelCase = BigBirdPegasusConfig(**a__ )
_UpperCAmelCase = BigBirdPegasusForConditionalGeneration(a__ )
_UpperCAmelCase = torch_model.state_dict()
_UpperCAmelCase = {}
# separating decoder weights
_UpperCAmelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
_UpperCAmelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
_UpperCAmelCase = [k.endswith(a__ ) for ending in KEYS_TO_IGNORE]
if any(a__ ):
continue
_UpperCAmelCase = DECODER_PATTERNS
_UpperCAmelCase = rename_state_dict_key(a__ , a__ )
if new_k not in state_dict:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
_UpperCAmelCase = v.T
_UpperCAmelCase = torch.from_numpy(a__ )
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
_UpperCAmelCase = [k.endswith(a__ ) for ending in KEYS_TO_IGNORE]
if any(a__ ):
continue
_UpperCAmelCase = REMAINING_PATTERNS
_UpperCAmelCase = rename_state_dict_key(a__ , a__ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
_UpperCAmelCase = v.T
_UpperCAmelCase = torch.from_numpy(a__ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
_UpperCAmelCase = mapping['model.embed_positions.weight']
_UpperCAmelCase = mapping.pop('model.embed_positions.weight' )
_UpperCAmelCase , _UpperCAmelCase = torch_model.load_state_dict(a__ , strict=a__ )
_UpperCAmelCase = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.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 lowerCAmelCase__ ( a__: Tuple ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = tf.train.list_variables(a__ )
_UpperCAmelCase = {}
_UpperCAmelCase = ['global_step']
for name, shape in tqdm(a__ , desc='converting tf checkpoint to dict' ):
_UpperCAmelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
_UpperCAmelCase = tf.train.load_variable(a__ , a__ )
_UpperCAmelCase = array
return tf_weights
def lowerCAmelCase__ ( a__: str , a__: str , a__: dict ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = get_tf_weights_as_numpy(a__ )
_UpperCAmelCase = convert_bigbird_pegasus(a__ , a__ )
torch_model.save_pretrained(a__ )
if __name__ == "__main__":
lowerCAmelCase__ :Union[str, Any] = argparse.ArgumentParser()
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.''')
lowerCAmelCase__ :Optional[Any] = parser.parse_args()
lowerCAmelCase__ :str = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 185 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCAmelCase__ ( a__: Dict , a__: Dict , a__: Any , a__: Optional[int]=None , a__: str=None , a__: List[Any]=None , a__: Optional[int]=None , a__: Union[str, Any]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=a__ )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = self.eos_token_id # Eos Token
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return MaMaaaConfig(
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 , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , 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 , )
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = inputs_dict['input_ids']
_UpperCAmelCase = inputs_dict['attention_mask']
_UpperCAmelCase = inputs_dict['head_mask']
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )['last_hidden_state']
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[
'last_hidden_state'
]
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-2 ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaEncoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : List[Any] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_a : List[str] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_a : int = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_a : str = True
_a : Union[str, Any] = True
_a : Optional[int] = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE )
self.assertEqual(info['missing_keys'] , [] )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if not self.is_encoder_decoder:
_UpperCAmelCase = inputs['input_ids']
del inputs["input_ids"]
else:
_UpperCAmelCase = inputs['input_ids']
_UpperCAmelCase = inputs.get('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.get_input_embeddings()
if not self.is_encoder_decoder:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
model(**_SCREAMING_SNAKE_CASE )[0]
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = input_dict['input_ids']
_UpperCAmelCase = input_ids.ne(1 ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval().to(_SCREAMING_SNAKE_CASE )
if torch_device == "cuda":
model.half()
model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
model.generate(num_beams=4 , do_sample=_SCREAMING_SNAKE_CASE , early_stopping=_SCREAMING_SNAKE_CASE , num_return_sequences=3 )
def lowerCAmelCase__ ( a__: Tuple ) -> Optional[int]:
'''simple docstring'''
return torch.tensor(a__ , dtype=torch.long , device=a__ )
lowerCAmelCase__ :str = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __a ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
# change to intended input
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
_UpperCAmelCase = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
_UpperCAmelCase = model.generate(
input_ids=dct['input_ids'].to(_SCREAMING_SNAKE_CASE ) , attention_mask=dct['attention_mask'].to(_SCREAMING_SNAKE_CASE ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
_UpperCAmelCase = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
_UpperCAmelCase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
assert generated == expected_en
| 185 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> bool:
'''simple docstring'''
A__ = [int(lowerCAmelCase__ ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(lowerCAmelCase__ ) == 4 and all(0 <= int(lowerCAmelCase__ ) <= 2_5_4 for octet in octets )
if __name__ == "__main__":
lowerCAmelCase__ = input().strip()
lowerCAmelCase__ = """valid""" if is_ip_va_address_valid(ip) else """invalid"""
print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 68 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> List[Any]:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
if n == 0:
return 0
__a = float('''-inf''' )
for i in range(1 , n + 1 ):
__a = max(
lowerCAmelCase__ , prices[i - 1] + naive_cut_rod_recursive(n - i , lowerCAmelCase__ ) )
return max_revue
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> List[str]:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
__a = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list , lowerCAmelCase__ : list ) -> Union[str, Any]:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
__a = float('''-inf''' )
for i in range(1 , n + 1 ):
__a = max(
lowerCAmelCase__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , lowerCAmelCase__ , lowerCAmelCase__ ) , )
__a = max_revenue
return max_rev[n]
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> Dict:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
__a = [float('''-inf''' ) for _ in range(n + 1 )]
__a = 0
for i in range(1 , n + 1 ):
__a = max_rev[i]
for j in range(1 , i + 1 ):
__a = max(lowerCAmelCase__ , prices[j - 1] + max_rev[i - j] )
__a = max_revenue_i
return max_rev[n]
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> str:
if n < 0:
__a = f'''n must be greater than or equal to 0. Got n = {n}'''
raise ValueError(lowerCAmelCase__ )
if n > len(lowerCAmelCase__ ):
__a = (
'''Each integral piece of rod must have a corresponding price. '''
f'''Got n = {n} but length of prices = {len(lowerCAmelCase__ )}'''
)
raise ValueError(lowerCAmelCase__ )
def lowercase ( ) -> int:
__a = [6, 10, 12, 15, 20, 23]
__a = len(lowerCAmelCase__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
__a = 36
__a = top_down_cut_rod(lowerCAmelCase__ , lowerCAmelCase__ )
__a = bottom_up_cut_rod(lowerCAmelCase__ , lowerCAmelCase__ )
__a = naive_cut_rod_recursive(lowerCAmelCase__ , lowerCAmelCase__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 45 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class A_ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : Optional[int] = TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small')
__lowerCamelCase : List[str] = AutoTokenizer.from_pretrained('google/mt5-small')
__lowerCamelCase : str = tokenizer('Hello there' ,return_tensors='tf').input_ids
__lowerCamelCase : Dict = tokenizer('Hi I am' ,return_tensors='tf').input_ids
__lowerCamelCase : Tuple = model(_A ,labels=_A).loss
__lowerCamelCase : List[Any] = -tf.math.reduce_mean(_A).numpy()
__lowerCamelCase : Dict = -2_1.2_2_8_1_6_8
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2E-4)
| 350 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class A_ ( unittest.TestCase ):
def lowerCAmelCase ( self : Optional[Any]):
# A mock response for an HTTP head request to emulate server down
__lowerCamelCase : List[Any] = mock.Mock()
__lowerCamelCase : Tuple = 5_0_0
__lowerCamelCase : Dict = {}
__lowerCamelCase : List[Any] = HTTPError
__lowerCamelCase : int = {}
# Download this model to make sure it's in the cache.
__lowerCamelCase : Optional[int] = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=SCREAMING_SNAKE_CASE__) as mock_head:
__lowerCamelCase : Tuple = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase ( self : Any):
# A mock response for an HTTP head request to emulate server down
__lowerCamelCase : int = mock.Mock()
__lowerCamelCase : List[str] = 5_0_0
__lowerCamelCase : Tuple = {}
__lowerCamelCase : List[str] = HTTPError
__lowerCamelCase : int = {}
# Download this model to make sure it's in the cache.
__lowerCamelCase : Dict = GPTaTokenizerFast.from_pretrained('gpt2')
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=SCREAMING_SNAKE_CASE__) as mock_head:
__lowerCamelCase : Optional[int] = GPTaTokenizerFast.from_pretrained('gpt2')
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase ( self : str):
# This test is for deprecated behavior and can be removed in v5
try:
__lowerCamelCase : Tuple = tempfile.mktemp()
with open(SCREAMING_SNAKE_CASE__ ,'wb') as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = AlbertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
finally:
os.remove(SCREAMING_SNAKE_CASE__)
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json'):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb') as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_0_0_0)
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json')
def lowerCAmelCase ( self : Optional[Any]):
# This test is for deprecated behavior and can be removed in v5
__lowerCamelCase : str = AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model')
@is_staging_test
class A_ ( unittest.TestCase ):
_UpperCAmelCase : Any = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def lowerCAmelCase ( cls : Optional[int]):
__lowerCamelCase : Optional[int] = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE__)
@classmethod
def lowerCAmelCase ( cls : str):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer')
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org')
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer')
except HTTPError:
pass
def lowerCAmelCase ( self : List[str]):
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase : Dict = os.path.join(SCREAMING_SNAKE_CASE__ ,'vocab.txt')
with open(SCREAMING_SNAKE_CASE__ ,'w' ,encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
__lowerCamelCase : Union[str, Any] = BertTokenizer(SCREAMING_SNAKE_CASE__)
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token)
__lowerCamelCase : List[str] = BertTokenizer.from_pretrained(F"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab)
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ ,repo_id='test-tokenizer' ,push_to_hub=SCREAMING_SNAKE_CASE__ ,use_auth_token=self._token)
__lowerCamelCase : str = BertTokenizer.from_pretrained(F"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab)
def lowerCAmelCase ( self : Any):
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase : Dict = os.path.join(SCREAMING_SNAKE_CASE__ ,'vocab.txt')
with open(SCREAMING_SNAKE_CASE__ ,'w' ,encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
__lowerCamelCase : Optional[Any] = BertTokenizer(SCREAMING_SNAKE_CASE__)
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token)
__lowerCamelCase : List[Any] = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org')
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab)
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
SCREAMING_SNAKE_CASE__ ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=SCREAMING_SNAKE_CASE__ ,use_auth_token=self._token)
__lowerCamelCase : List[Any] = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org')
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab)
@require_tokenizers
def lowerCAmelCase ( self : int):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase : Dict = os.path.join(SCREAMING_SNAKE_CASE__ ,'vocab.txt')
with open(SCREAMING_SNAKE_CASE__ ,'w' ,encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
__lowerCamelCase : Any = CustomTokenizer(SCREAMING_SNAKE_CASE__)
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token)
__lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" ,trust_remote_code=SCREAMING_SNAKE_CASE__)
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer')
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase : List[str] = os.path.join(SCREAMING_SNAKE_CASE__ ,'vocab.txt')
with open(SCREAMING_SNAKE_CASE__ ,'w' ,encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
__lowerCamelCase : Dict = BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__)
bert_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = CustomTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__)
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token)
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" ,trust_remote_code=SCREAMING_SNAKE_CASE__)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast')
__lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(
F"{USER}/test-dynamic-tokenizer" ,use_fast=SCREAMING_SNAKE_CASE__ ,trust_remote_code=SCREAMING_SNAKE_CASE__)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer')
class A_ ( unittest.TestCase ):
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : List[str] = Trie()
trie.add('Hello 友達')
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}})
trie.add('Hello')
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}})
def lowerCAmelCase ( self : Tuple):
__lowerCamelCase : str = Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100') ,['[CLS] This is a extra_id_100'])
trie.add('[CLS]')
trie.add('extra_id_1')
trie.add('extra_id_100')
self.assertEqual(trie.split('[CLS] This is a extra_id_100') ,['[CLS]', ' This is a ', 'extra_id_100'])
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : Dict = Trie()
trie.add('A')
self.assertEqual(trie.split('ABC') ,['A', 'BC'])
self.assertEqual(trie.split('BCA') ,['BC', 'A'])
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : Any = Trie()
trie.add('TOKEN]')
trie.add('[SPECIAL_TOKEN]')
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]') ,['This is something ', '[SPECIAL_TOKEN]'])
def lowerCAmelCase ( self : int):
__lowerCamelCase : List[Any] = Trie()
trie.add('A')
trie.add('P')
trie.add('[SPECIAL_TOKEN]')
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]') ,['This is something ', '[SPECIAL_TOKEN]'])
def lowerCAmelCase ( self : Optional[int]):
__lowerCamelCase : Any = Trie()
trie.add('AB')
trie.add('B')
trie.add('C')
self.assertEqual(trie.split('ABC') ,['AB', 'C'])
def lowerCAmelCase ( self : Optional[int]):
__lowerCamelCase : Dict = Trie()
trie.add('ABC')
trie.add('B')
trie.add('CD')
self.assertEqual(trie.split('ABCD') ,['ABC', 'D'])
def lowerCAmelCase ( self : List[Any]):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
__lowerCamelCase : List[Any] = Trie()
__lowerCamelCase : Any = trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3])
self.assertEqual(SCREAMING_SNAKE_CASE__ ,['AB', 'C'])
| 113 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : Any = logging.get_logger(__name__)
lowerCamelCase__ : Optional[Any] = {'''vocab_file''': '''spiece.model'''}
lowerCamelCase__ : Optional[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
}
}
lowerCamelCase__ : List[Any] = {
'''albert-base-v1''': 5_12,
'''albert-large-v1''': 5_12,
'''albert-xlarge-v1''': 5_12,
'''albert-xxlarge-v1''': 5_12,
'''albert-base-v2''': 5_12,
'''albert-large-v2''': 5_12,
'''albert-xlarge-v2''': 5_12,
'''albert-xxlarge-v2''': 5_12,
}
lowerCamelCase__ : int = '''▁'''
class _UpperCAmelCase ( __a):
__a : str = VOCAB_FILES_NAMES
__a : Dict = PRETRAINED_VOCAB_FILES_MAP
__a : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _A , _A=True , _A=True , _A=False , _A="[CLS]" , _A="[SEP]" , _A="<unk>" , _A="[SEP]" , _A="<pad>" , _A="[CLS]" , _A="[MASK]" , _A = None , **_A , ) -> None:
'''simple docstring'''
_UpperCAmelCase : List[Any] = (
AddedToken(_A , lstrip=_A , rstrip=_A , normalized=_A )
if isinstance(_A , _A )
else mask_token
)
_UpperCAmelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_A , remove_space=_A , keep_accents=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
_UpperCAmelCase : str = do_lower_case
_UpperCAmelCase : Optional[Any] = remove_space
_UpperCAmelCase : str = keep_accents
_UpperCAmelCase : int = vocab_file
_UpperCAmelCase : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_A )
@property
def __snake_case ( self ) -> Optional[int]:
'''simple docstring'''
return len(self.sp_model )
def __snake_case ( self ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = self.__dict__.copy()
_UpperCAmelCase : Union[str, Any] = None
return state
def __setstate__( self , _A ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __snake_case ( self , _A ) -> Union[str, Any]:
'''simple docstring'''
if self.remove_space:
_UpperCAmelCase : Tuple = """ """.join(inputs.strip().split() )
else:
_UpperCAmelCase : int = inputs
_UpperCAmelCase : List[str] = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
_UpperCAmelCase : Any = unicodedata.normalize("""NFKD""" , _A )
_UpperCAmelCase : str = """""".join([c for c in outputs if not unicodedata.combining(_A )] )
if self.do_lower_case:
_UpperCAmelCase : Tuple = outputs.lower()
return outputs
def __snake_case ( self , _A ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Dict = self.preprocess_text(_A )
_UpperCAmelCase : List[Any] = self.sp_model.encode(_A , out_type=_A )
_UpperCAmelCase : Optional[Any] = []
for piece in pieces:
if len(_A ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
_UpperCAmelCase : List[str] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_A , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_UpperCAmelCase : Optional[Any] = cur_pieces[1:]
else:
_UpperCAmelCase : Dict = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_A )
else:
new_pieces.append(_A )
return new_pieces
def __snake_case ( self , _A ) -> str:
'''simple docstring'''
return self.sp_model.PieceToId(_A )
def __snake_case ( self , _A ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.IdToPiece(_A )
def __snake_case ( self , _A ) -> Any:
'''simple docstring'''
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[int] = """"""
_UpperCAmelCase : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_A ) + token
_UpperCAmelCase : Dict = True
_UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(_A )
_UpperCAmelCase : Tuple = False
out_string += self.sp_model.decode(_A )
return out_string.strip()
def __snake_case ( self , _A , _A = None ) -> List[int]:
'''simple docstring'''
_UpperCAmelCase : int = [self.sep_token_id]
_UpperCAmelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __snake_case ( self , _A , _A = None , _A = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is not None:
return [1] + ([0] * len(_A )) + [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1]
def __snake_case ( self , _A , _A = None ) -> List[int]:
'''simple docstring'''
_UpperCAmelCase : Dict = [self.sep_token_id]
_UpperCAmelCase : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __snake_case ( self , _A , _A = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase : str = os.path.join(
_A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , """wb""" ) as fi:
_UpperCAmelCase : List[Any] = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 246 |
"""simple docstring"""
import math
lowerCamelCase__ : List[Any] = 10
lowerCamelCase__ : Optional[int] = 7
lowerCamelCase__ : Dict = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( _lowerCAmelCase : int = 20 ) -> str:
_UpperCAmelCase : List[str] = math.comb(_lowerCAmelCase, _lowerCAmelCase )
_UpperCAmelCase : Optional[int] = math.comb(NUM_BALLS - BALLS_PER_COLOUR, _lowerCAmelCase )
_UpperCAmelCase : List[str] = NUM_COLOURS * (1 - missing_colour / total)
return f'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 246 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict = logging.get_logger(__name__)
_A : Tuple = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : str = """megatron-bert"""
def __init__( self : Any , A : Union[str, Any]=2_9_0_5_6 , A : Dict=1_0_2_4 , A : Dict=2_4 , A : Optional[Any]=1_6 , A : Optional[Any]=4_0_9_6 , A : Union[str, Any]="gelu" , A : List[Any]=0.1 , A : Optional[Any]=0.1 , A : Tuple=5_1_2 , A : Any=2 , A : Optional[int]=0.02 , A : List[Any]=1e-12 , A : Union[str, Any]=0 , A : List[str]="absolute" , A : int=True , **A : Any , ) ->List[str]:
super().__init__(pad_token_id=_A , **_A )
lowerCamelCase__ : List[Any] = vocab_size
lowerCamelCase__ : List[str] = hidden_size
lowerCamelCase__ : List[str] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : List[Any] = hidden_act
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : Optional[Any] = hidden_dropout_prob
lowerCamelCase__ : List[str] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : str = type_vocab_size
lowerCamelCase__ : Optional[Any] = initializer_range
lowerCamelCase__ : Dict = layer_norm_eps
lowerCamelCase__ : Optional[Any] = position_embedding_type
lowerCamelCase__ : Dict = use_cache
| 357 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : Tuple = logging.get_logger(__name__)
_A : List[str] = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,lowerCAmelCase_ ):
_UpperCAmelCase : Dict = "nat"
_UpperCAmelCase : Tuple = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : List[Any] , A : Union[str, Any]=4 , A : str=3 , A : List[Any]=6_4 , A : Optional[Any]=[3, 4, 6, 5] , A : int=[2, 4, 8, 1_6] , A : Optional[int]=7 , A : List[Any]=3.0 , A : str=True , A : str=0.0 , A : Any=0.0 , A : int=0.1 , A : Tuple="gelu" , A : List[Any]=0.02 , A : str=1e-5 , A : Optional[int]=0.0 , A : Optional[Any]=None , A : Dict=None , **A : str , ) ->Union[str, Any]:
super().__init__(**A )
lowerCamelCase__ : Any = patch_size
lowerCamelCase__ : Optional[Any] = num_channels
lowerCamelCase__ : Any = embed_dim
lowerCamelCase__ : str = depths
lowerCamelCase__ : Union[str, Any] = len(A )
lowerCamelCase__ : int = num_heads
lowerCamelCase__ : Optional[int] = kernel_size
lowerCamelCase__ : Optional[int] = mlp_ratio
lowerCamelCase__ : List[Any] = qkv_bias
lowerCamelCase__ : Tuple = hidden_dropout_prob
lowerCamelCase__ : List[str] = attention_probs_dropout_prob
lowerCamelCase__ : List[Any] = drop_path_rate
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : Union[str, Any] = layer_norm_eps
lowerCamelCase__ : List[Any] = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase__ : str = int(embed_dim * 2 ** (len(A ) - 1) )
lowerCamelCase__ : Dict = layer_scale_init_value
lowerCamelCase__ : Dict = ['''stem'''] + [F"stage{idx}" for idx in range(1 , len(A ) + 1 )]
lowerCamelCase__ , lowerCamelCase__ : str = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
| 265 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : int = MgpstrTokenizer
lowerCAmelCase_ : List[str] = False
lowerCAmelCase_ : Optional[int] = {}
lowerCAmelCase_ : Any = False
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
super().setUp()
# fmt: off
UpperCAmelCase__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
UpperCAmelCase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) + """\n""" )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = """tester"""
UpperCAmelCase__ = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCAmelCase__ = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
UpperCAmelCase__ = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) , 1 )
UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
self.assertTrue(special_token not in decoded )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCAmelCase__ , UpperCAmelCase__ = self.get_input_output_texts(_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = tokenizer.convert_ids_to_tokens(_UpperCAmelCase )
self.assertNotEqual(len(_UpperCAmelCase ) , 0 )
UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(text_a.replace(""" """ , """""" ) , _UpperCAmelCase )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
pass
| 346 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n'
UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'
UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/BLEU""",
"""https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
] , )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ):
"""simple docstring"""
UpperCAmelCase__ = compute_bleu(
reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 346 | 1 |
UpperCamelCase__ = [0, 2, 4, 6, 8]
UpperCamelCase__ = [1, 3, 5, 7, 9]
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCamelCase__ = 0
for digit in range(10 ):
UpperCamelCase__ = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a__ , a__ )
return result
UpperCamelCase__ = 0
for digita in range(10 ):
UpperCamelCase__ = digita
if (remainder + digita) % 2 == 0:
UpperCamelCase__ = ODD_DIGITS
else:
UpperCamelCase__ = EVEN_DIGITS
for digita in other_parity_digits:
UpperCamelCase__ = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a__ , a__ , )
return result
def _UpperCamelCase (a__ :int = 9 ):
"""simple docstring"""
UpperCamelCase__ = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a__ , 0 , [0] * length , a__ )
return result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 87 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
UpperCamelCase__ = [
"openmmlab/upernet-convnext-tiny",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
UpperCamelCase__ = "UperNetConfig"
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0 , __lowerCAmelCase = False , __lowerCAmelCase = 1 , ):
super().__init__()
UpperCamelCase__ = nn.Convad(
in_channels=__lowerCAmelCase , out_channels=__lowerCAmelCase , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , bias=__lowerCAmelCase , dilation=__lowerCAmelCase , )
UpperCamelCase__ = nn.BatchNormad(__lowerCAmelCase )
UpperCamelCase__ = nn.ReLU()
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = self.conv(__lowerCAmelCase )
UpperCamelCase__ = self.batch_norm(__lowerCAmelCase )
UpperCamelCase__ = self.activation(__lowerCAmelCase )
return output
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
super().__init__()
UpperCamelCase__ = [
nn.AdaptiveAvgPoolad(__lowerCAmelCase ),
UperNetConvModule(__lowerCAmelCase , __lowerCAmelCase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(__lowerCAmelCase ) , __lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = input
for layer in self.layers:
UpperCamelCase__ = layer(__lowerCAmelCase )
return hidden_state
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
super().__init__()
UpperCamelCase__ = pool_scales
UpperCamelCase__ = align_corners
UpperCamelCase__ = in_channels
UpperCamelCase__ = channels
UpperCamelCase__ = []
for i, pool_scale in enumerate(__lowerCAmelCase ):
UpperCamelCase__ = UperNetPyramidPoolingBlock(pool_scale=__lowerCAmelCase , in_channels=__lowerCAmelCase , channels=__lowerCAmelCase )
self.blocks.append(__lowerCAmelCase )
self.add_module(str(__lowerCAmelCase ) , __lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = []
for ppm in self.blocks:
UpperCamelCase__ = ppm(__lowerCAmelCase )
UpperCamelCase__ = nn.functional.interpolate(
__lowerCAmelCase , size=x.size()[2:] , mode="""bilinear""" , align_corners=self.align_corners )
ppm_outs.append(__lowerCAmelCase )
return ppm_outs
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
super().__init__()
UpperCamelCase__ = config
UpperCamelCase__ = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCamelCase__ = in_channels
UpperCamelCase__ = config.hidden_size
UpperCamelCase__ = False
UpperCamelCase__ = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
UpperCamelCase__ = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCamelCase__ = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCamelCase__ = nn.ModuleList()
UpperCamelCase__ = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCamelCase__ = UperNetConvModule(__lowerCAmelCase , self.channels , kernel_size=1 )
UpperCamelCase__ = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(__lowerCAmelCase )
self.fpn_convs.append(__lowerCAmelCase )
UpperCamelCase__ = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def _lowerCamelCase ( self ):
self.apply(self._init_weights )
def _lowerCamelCase ( self , __lowerCAmelCase ):
if isinstance(__lowerCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = inputs[-1]
UpperCamelCase__ = [x]
psp_outs.extend(self.psp_modules(__lowerCAmelCase ) )
UpperCamelCase__ = torch.cat(__lowerCAmelCase , dim=1 )
UpperCamelCase__ = self.bottleneck(__lowerCAmelCase )
return output
def _lowerCamelCase ( self , __lowerCAmelCase ):
# build laterals
UpperCamelCase__ = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(__lowerCAmelCase ) )
# build top-down path
UpperCamelCase__ = len(__lowerCAmelCase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCamelCase__ = laterals[i - 1].shape[2:]
UpperCamelCase__ = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=__lowerCAmelCase , mode="""bilinear""" , align_corners=self.align_corners )
# build outputs
UpperCamelCase__ = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCamelCase__ = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode="""bilinear""" , align_corners=self.align_corners )
UpperCamelCase__ = torch.cat(__lowerCAmelCase , dim=1 )
UpperCamelCase__ = self.fpn_bottleneck(__lowerCAmelCase )
UpperCamelCase__ = self.classifier(__lowerCAmelCase )
return output
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase = 2 , __lowerCAmelCase = 3 , __lowerCAmelCase = 1 ):
super().__init__()
UpperCamelCase__ = config
UpperCamelCase__ = config.auxiliary_in_channels
UpperCamelCase__ = config.auxiliary_channels
UpperCamelCase__ = config.auxiliary_num_convs
UpperCamelCase__ = config.auxiliary_concat_input
UpperCamelCase__ = in_index
UpperCamelCase__ = (kernel_size // 2) * dilation
UpperCamelCase__ = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , dilation=__lowerCAmelCase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , dilation=__lowerCAmelCase ) )
if self.num_convs == 0:
UpperCamelCase__ = nn.Identity()
else:
UpperCamelCase__ = nn.Sequential(*__lowerCAmelCase )
if self.concat_input:
UpperCamelCase__ = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=__lowerCAmelCase , padding=kernel_size // 2 )
UpperCamelCase__ = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def _lowerCamelCase ( self ):
self.apply(self._init_weights )
def _lowerCamelCase ( self , __lowerCAmelCase ):
if isinstance(__lowerCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _lowerCamelCase ( self , __lowerCAmelCase ):
# just take the relevant feature maps
UpperCamelCase__ = encoder_hidden_states[self.in_index]
UpperCamelCase__ = self.convs(__lowerCAmelCase )
if self.concat_input:
UpperCamelCase__ = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
UpperCamelCase__ = self.classifier(__lowerCAmelCase )
return output
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Any = UperNetConfig
snake_case : List[Any] = """pixel_values"""
snake_case : Optional[Any] = True
def _lowerCamelCase ( self , __lowerCAmelCase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def _lowerCamelCase ( self ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=False ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = value
UpperCamelCase__ = r"\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): 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__ = r"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for 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(
"""UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""" , _a , )
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , __lowerCAmelCase ):
super().__init__(__lowerCAmelCase )
UpperCamelCase__ = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
UpperCamelCase__ = UperNetHead(__lowerCAmelCase , in_channels=self.backbone.channels )
UpperCamelCase__ = UperNetFCNHead(__lowerCAmelCase ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("""batch_size, sequence_length""" ) )
@replace_return_docstrings(output_type=__lowerCAmelCase , config_class=_CONFIG_FOR_DOC )
def _lowerCamelCase ( self , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , ):
UpperCamelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase__ = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCamelCase__ = self.backbone.forward_with_filtered_kwargs(
__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , output_attentions=__lowerCAmelCase )
UpperCamelCase__ = outputs.feature_maps
UpperCamelCase__ = self.decode_head(__lowerCAmelCase )
UpperCamelCase__ = nn.functional.interpolate(__lowerCAmelCase , size=pixel_values.shape[2:] , mode="""bilinear""" , align_corners=__lowerCAmelCase )
UpperCamelCase__ = None
if self.auxiliary_head is not None:
UpperCamelCase__ = self.auxiliary_head(__lowerCAmelCase )
UpperCamelCase__ = nn.functional.interpolate(
__lowerCAmelCase , size=pixel_values.shape[2:] , mode="""bilinear""" , align_corners=__lowerCAmelCase )
UpperCamelCase__ = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("""The number of labels should be greater than one""" )
else:
# compute weighted loss
UpperCamelCase__ = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
UpperCamelCase__ = loss_fct(__lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase__ = loss_fct(__lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase__ = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCamelCase__ = (logits,) + outputs[1:]
else:
UpperCamelCase__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=__lowerCAmelCase , logits=__lowerCAmelCase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 87 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A ( UpperCamelCase__ , unittest.TestCase ):
a__ : str = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def _lowercase (self : List[str] , __a : str=0 ):
UpperCAmelCase_ = np.random.RandomState(__a )
UpperCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _lowercase (self : Dict ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Any ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__a )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Tuple ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Tuple ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Dict ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase (self : Dict ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = 3 * [inputs["prompt"]]
# forward
UpperCAmelCase_ = pipe(**__a )
UpperCAmelCase_ = output.images[0, -3:, -3:, -1]
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = 3 * [inputs.pop("prompt" )]
UpperCAmelCase_ = pipe.tokenizer(
__a , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=__a , return_tensors="np" , )
UpperCAmelCase_ = text_inputs["input_ids"]
UpperCAmelCase_ = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
UpperCAmelCase_ = prompt_embeds
# forward
UpperCAmelCase_ = pipe(**__a )
UpperCAmelCase_ = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
def _lowercase (self : Any ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = 3 * ["this is a negative prompt"]
UpperCAmelCase_ = negative_prompt
UpperCAmelCase_ = 3 * [inputs["prompt"]]
# forward
UpperCAmelCase_ = pipe(**__a )
UpperCAmelCase_ = output.images[0, -3:, -3:, -1]
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = 3 * [inputs.pop("prompt" )]
UpperCAmelCase_ = []
for p in [prompt, negative_prompt]:
UpperCAmelCase_ = pipe.tokenizer(
__a , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=__a , return_tensors="np" , )
UpperCAmelCase_ = text_inputs["input_ids"]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
UpperCAmelCase_ , UpperCAmelCase_ = embeds
# forward
UpperCAmelCase_ = pipe(**__a )
UpperCAmelCase_ = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __A ( unittest.TestCase ):
@property
def _lowercase (self : List[str] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _lowercase (self : int ):
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = False
return options
def _lowercase (self : str ):
# using the PNDM scheduler by default
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = "A painting of a squirrel eating a burger"
np.random.seed(0 )
UpperCAmelCase_ = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="np" )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _lowercase (self : Tuple ):
UpperCAmelCase_ = DDIMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=__a , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = "open neural network exchange"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=__a , output_type="np" )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _lowercase (self : List[Any] ):
UpperCAmelCase_ = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=__a , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = "open neural network exchange"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=__a , output_type="np" )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _lowercase (self : Optional[int] ):
UpperCAmelCase_ = 0
def test_callback_fn(__a : int , __a : int , __a : np.ndarray ) -> None:
UpperCAmelCase_ = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
UpperCAmelCase_ = latents[0, -3:, -3:, -1]
UpperCAmelCase_ = np.array(
[-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
UpperCAmelCase_ = latents[0, -3:, -3:, -1]
UpperCAmelCase_ = np.array(
[-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
UpperCAmelCase_ = False
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = "Andromeda galaxy in a bottle"
UpperCAmelCase_ = np.random.RandomState(0 )
pipe(
prompt=__a , num_inference_steps=5 , guidance_scale=7.5 , generator=__a , callback=__a , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _lowercase (self : List[Any] ):
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(__a , __a )
assert pipe.safety_checker is None
UpperCAmelCase_ = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__a )
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(__a )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
UpperCAmelCase_ = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
| 1 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = [0 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase :str = 0, 0
for i in range(1 , len(SCREAMING_SNAKE_CASE ) ):
# case when current index is inside the interval
if i <= right_pointer:
__UpperCamelCase :Union[str, Any] = min(right_pointer - i + 1 , z_result[i - left_pointer] )
__UpperCamelCase :Tuple = min_edge
while go_next(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = i, i + z_result[i] - 1
return z_result
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return i + z_result[i] < len(SCREAMING_SNAKE_CASE ) and s[z_result[i]] == s[i + z_result[i]]
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[Any] = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
__UpperCamelCase :Tuple = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(SCREAMING_SNAKE_CASE ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 0 |
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 lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE_ = 'Pix2StructImageProcessor'
SCREAMING_SNAKE_CASE_ = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self : Tuple ,__lowerCamelCase : Any ,__lowerCamelCase : Dict ):
'''simple docstring'''
a = False
super().__init__(__lowerCamelCase ,__lowerCamelCase )
def __call__( self : Any ,__lowerCamelCase : str=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 : Optional[int] = 20_48 ,__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 : Union[str, Any] ,):
'''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 and not self.image_processor.is_vqa:
a = self.tokenizer
a = self.tokenizer(
text=__lowerCamelCase ,add_special_tokens=__lowerCamelCase ,padding=__lowerCamelCase ,truncation=__lowerCamelCase ,max_length=__lowerCamelCase ,stride=__lowerCamelCase ,pad_to_multiple_of=__lowerCamelCase ,return_attention_mask=__lowerCamelCase ,return_overflowing_tokens=__lowerCamelCase ,return_special_tokens_mask=__lowerCamelCase ,return_offsets_mapping=__lowerCamelCase ,return_token_type_ids=__lowerCamelCase ,return_length=__lowerCamelCase ,verbose=__lowerCamelCase ,return_tensors=__lowerCamelCase ,**__lowerCamelCase ,)
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
a = self.image_processor(
__lowerCamelCase ,return_tensors=__lowerCamelCase ,max_patches=__lowerCamelCase ,**__lowerCamelCase )
else:
# add pixel_values and bbox
a = self.image_processor(
__lowerCamelCase ,return_tensors=__lowerCamelCase ,max_patches=__lowerCamelCase ,header_text=__lowerCamelCase ,**__lowerCamelCase )
if text is not None and not self.image_processor.is_vqa:
a = self.tokenizer(
text=__lowerCamelCase ,add_special_tokens=__lowerCamelCase ,padding=__lowerCamelCase ,truncation=__lowerCamelCase ,max_length=__lowerCamelCase ,stride=__lowerCamelCase ,pad_to_multiple_of=__lowerCamelCase ,return_attention_mask=__lowerCamelCase ,return_overflowing_tokens=__lowerCamelCase ,return_special_tokens_mask=__lowerCamelCase ,return_offsets_mapping=__lowerCamelCase ,return_token_type_ids=__lowerCamelCase ,return_length=__lowerCamelCase ,verbose=__lowerCamelCase ,return_tensors=__lowerCamelCase ,**__lowerCamelCase ,)
if "attention_mask" in text_encoding:
a = text_encoding.pop('''attention_mask''' )
if "input_ids" in text_encoding:
a = text_encoding.pop('''input_ids''' )
else:
a = None
if text_encoding is not None:
encoding_image_processor.update(__lowerCamelCase )
return encoding_image_processor
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ,*__lowerCamelCase : List[str] ,**__lowerCamelCase : Tuple ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__lowerCamelCase ,**__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,*__lowerCamelCase : List[Any] ,**__lowerCamelCase : Any ):
'''simple docstring'''
return self.tokenizer.decode(*__lowerCamelCase ,**__lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 356 |
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__ : Optional[int] = pd.read_csv("""sample_data.csv""", header=None)
UpperCamelCase__ : Tuple = df.shape[:1][0]
# If you're using some other dataset input the target column
UpperCamelCase__ : List[Any] = df.iloc[:, 1:2]
UpperCamelCase__ : Union[str, Any] = actual_data.values.reshape(len_data, 1)
UpperCamelCase__ : List[Any] = MinMaxScaler().fit_transform(actual_data)
UpperCamelCase__ : Optional[Any] = 10
UpperCamelCase__ : int = 5
UpperCamelCase__ : List[str] = 20
UpperCamelCase__ : Optional[int] = len_data - periods * look_back
UpperCamelCase__ : Union[str, Any] = actual_data[:division]
UpperCamelCase__ : str = actual_data[division - look_back :]
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = [], []
UpperCamelCase__ , UpperCamelCase__ : str = [], []
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__ : List[str] = np.array(train_x)
UpperCamelCase__ : Optional[Any] = np.array(test_x)
UpperCamelCase__ : Tuple = np.array([list(i.ravel()) for i in train_y])
UpperCamelCase__ : Optional[Any] = np.array([list(i.ravel()) for i in test_y])
UpperCamelCase__ : Union[str, 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__ : Tuple = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
UpperCamelCase__ : Tuple = model.predict(x_test)
| 330 | 0 |
"""simple docstring"""
_a : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_a : Optional[Any] = {
'm': 0,
'km': 3,
'Mm': 6,
'Gm': 9,
'Tm': 12,
'Pm': 15,
'Em': 18,
'Zm': 21,
'Ym': 24,
}
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : float ,_lowerCamelCase : str ,_lowerCamelCase : str ) -> float:
_lowerCAmelCase : Tuple = from_type.lower().strip("""s""" )
_lowerCAmelCase : str = to_type.lower().strip("""s""" )
_lowerCAmelCase : int = UNIT_SYMBOL.get(_lowerCamelCase ,_lowerCamelCase )
_lowerCAmelCase : Any = UNIT_SYMBOL.get(_lowerCamelCase ,_lowerCamelCase )
if from_sanitized not in METRIC_CONVERSION:
_lowerCAmelCase : Optional[int] = (
f"Invalid 'from_type' value: {from_type!r}.\n"
f"Conversion abbreviations are: {', '.join(_lowerCamelCase )}"
)
raise ValueError(_lowerCamelCase )
if to_sanitized not in METRIC_CONVERSION:
_lowerCAmelCase : int = (
f"Invalid 'to_type' value: {to_type!r}.\n"
f"Conversion abbreviations are: {', '.join(_lowerCamelCase )}"
)
raise ValueError(_lowerCamelCase )
_lowerCAmelCase : Tuple = METRIC_CONVERSION[from_sanitized]
_lowerCAmelCase : Optional[int] = METRIC_CONVERSION[to_sanitized]
_lowerCAmelCase : List[str] = 1
if from_exponent > to_exponent:
_lowerCAmelCase : str = from_exponent - to_exponent
else:
_lowerCAmelCase : Dict = -(to_exponent - from_exponent)
return value * pow(10 ,_lowerCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 44 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ) -> Dict:
_lowerCAmelCase : List[Any] = torch.exp(_lowerCamelCase )
_lowerCAmelCase : List[Any] = torch.sum(_lowerCamelCase ,dim=1 ) # sum of exp(x_i)
_lowerCAmelCase : Dict = torch.sum(x * exp_x ,dim=1 ) # sum of x_i * exp(x_i)
return torch.log(_lowerCamelCase ) - B / A
class __A ( nn.Module ):
def __init__( self , a__ ):
super().__init__()
_lowerCAmelCase : int = config.output_attentions
_lowerCAmelCase : Any = config.output_hidden_states
_lowerCAmelCase : List[Any] = nn.ModuleList([BertLayer(a__ ) for _ in range(config.num_hidden_layers )] )
_lowerCAmelCase : Any = nn.ModuleList([BertHighway(a__ ) for _ in range(config.num_hidden_layers )] )
_lowerCAmelCase : str = [-1 for _ in range(config.num_hidden_layers )]
def __A ( self , a__ ):
if (type(a__ ) is float) or (type(a__ ) is int):
for i in range(len(self.early_exit_entropy ) ):
_lowerCAmelCase : Tuple = x
else:
_lowerCAmelCase : Optional[int] = x
def __A ( self , a__ ):
_lowerCAmelCase : Optional[int] = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def __A ( self , a__ , a__=None , a__=None , a__=None , a__=None , ):
_lowerCAmelCase : Any = ()
_lowerCAmelCase : Optional[int] = ()
_lowerCAmelCase : List[Any] = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
_lowerCAmelCase : str = all_hidden_states + (hidden_states,)
_lowerCAmelCase : List[str] = layer_module(
a__ , a__ , head_mask[i] , a__ , a__ )
_lowerCAmelCase : Union[str, Any] = layer_outputs[0]
if self.output_attentions:
_lowerCAmelCase : Dict = all_attentions + (layer_outputs[1],)
_lowerCAmelCase : Optional[int] = (hidden_states,)
if self.output_hidden_states:
_lowerCAmelCase : Union[str, Any] = current_outputs + (all_hidden_states,)
if self.output_attentions:
_lowerCAmelCase : Optional[int] = current_outputs + (all_attentions,)
_lowerCAmelCase : Optional[Any] = self.highway[i](a__ )
# logits, pooled_output
if not self.training:
_lowerCAmelCase : Tuple = highway_exit[0]
_lowerCAmelCase : Any = entropy(a__ )
_lowerCAmelCase : Optional[Any] = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
_lowerCAmelCase : Union[str, Any] = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
_lowerCAmelCase : List[str] = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(a__ , i + 1 )
else:
_lowerCAmelCase : Dict = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
_lowerCAmelCase : List[Any] = all_hidden_states + (hidden_states,)
_lowerCAmelCase : List[Any] = (hidden_states,)
if self.output_hidden_states:
_lowerCAmelCase : List[str] = outputs + (all_hidden_states,)
if self.output_attentions:
_lowerCAmelCase : Any = outputs + (all_attentions,)
_lowerCAmelCase : Optional[int] = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"The Bert Model transformer with early exiting (DeeBERT). " , SCREAMING_SNAKE_CASE_ , )
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , a__ ):
super().__init__(a__ )
_lowerCAmelCase : Any = config
_lowerCAmelCase : Tuple = BertEmbeddings(a__ )
_lowerCAmelCase : Tuple = DeeBertEncoder(a__ )
_lowerCAmelCase : List[str] = BertPooler(a__ )
self.init_weights()
def __A ( self ):
self.encoder.init_highway_pooler(self.pooler )
def __A ( self ):
return self.embeddings.word_embeddings
def __A ( self , a__ ):
_lowerCAmelCase : Dict = value
def __A ( self , a__ ):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(a__ )
@add_start_docstrings_to_model_forward(a__ )
def __A ( self , a__=None , a__=None , a__=None , a__=None , a__=None , a__=None , a__=None , a__=None , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
_lowerCAmelCase : Any = input_ids.size()
elif inputs_embeds is not None:
_lowerCAmelCase : List[str] = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
_lowerCAmelCase : str = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
_lowerCAmelCase : List[Any] = torch.ones(a__ , device=a__ )
if encoder_attention_mask is None:
_lowerCAmelCase : Optional[Any] = torch.ones(a__ , device=a__ )
if token_type_ids is None:
_lowerCAmelCase : Dict = torch.zeros(a__ , dtype=torch.long , device=a__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
_lowerCAmelCase : torch.Tensor = self.get_extended_attention_mask(a__ , a__ , a__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
_lowerCAmelCase : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
_lowerCAmelCase : Tuple = encoder_attention_mask[:, None, None, :]
_lowerCAmelCase : Union[str, Any] = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
_lowerCAmelCase : Optional[Any] = (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
_lowerCAmelCase : Optional[int] = self.get_head_mask(a__ , self.config.num_hidden_layers )
_lowerCAmelCase : Dict = self.embeddings(
input_ids=a__ , position_ids=a__ , token_type_ids=a__ , inputs_embeds=a__ )
_lowerCAmelCase : Union[str, Any] = self.encoder(
a__ , attention_mask=a__ , head_mask=a__ , encoder_hidden_states=a__ , encoder_attention_mask=a__ , )
_lowerCAmelCase : Dict = encoder_outputs[0]
_lowerCAmelCase : Union[str, Any] = self.pooler(a__ )
_lowerCAmelCase : Dict = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , a__ , a__ ):
_lowerCAmelCase : str = message
_lowerCAmelCase : str = exit_layer # start from 1!
class __A ( nn.Module ):
def __init__( self , a__ ):
super().__init__()
_lowerCAmelCase : Any = BertPooler(a__ )
_lowerCAmelCase : str = nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase : Union[str, Any] = nn.Linear(config.hidden_size , config.num_labels )
def __A ( self , a__ ):
# Pooler
_lowerCAmelCase : Tuple = encoder_outputs[0]
_lowerCAmelCase : int = self.pooler(a__ )
# "return" pooler_output
# BertModel
_lowerCAmelCase : Union[str, Any] = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
_lowerCAmelCase : Optional[int] = bmodel_output[1]
_lowerCAmelCase : Tuple = self.dropout(a__ )
_lowerCAmelCase : Dict = self.classifier(a__ )
return logits, pooled_output
@add_start_docstrings(
"Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. " , SCREAMING_SNAKE_CASE_ , )
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , a__ ):
super().__init__(a__ )
_lowerCAmelCase : List[str] = config.num_labels
_lowerCAmelCase : Optional[Any] = config.num_hidden_layers
_lowerCAmelCase : str = DeeBertModel(a__ )
_lowerCAmelCase : Tuple = nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase : List[Any] = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(a__ )
def __A ( self , a__=None , a__=None , a__=None , a__=None , a__=None , a__=None , a__=None , a__=-1 , a__=False , ):
_lowerCAmelCase : Dict = self.num_layers
try:
_lowerCAmelCase : str = self.bert(
a__ , attention_mask=a__ , token_type_ids=a__ , position_ids=a__ , head_mask=a__ , inputs_embeds=a__ , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
_lowerCAmelCase : Any = outputs[1]
_lowerCAmelCase : Optional[int] = self.dropout(a__ )
_lowerCAmelCase : List[str] = self.classifier(a__ )
_lowerCAmelCase : Union[str, Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : int = e.exit_layer
_lowerCAmelCase : Union[str, Any] = outputs[0]
if not self.training:
_lowerCAmelCase : Tuple = entropy(a__ )
_lowerCAmelCase : Optional[int] = []
_lowerCAmelCase : Optional[Any] = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Tuple = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase : Any = CrossEntropyLoss()
_lowerCAmelCase : Optional[int] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_lowerCAmelCase : Optional[Any] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Dict = highway_exit[0]
if not self.training:
highway_logits_all.append(a__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase : Optional[int] = CrossEntropyLoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(a__ )
if train_highway:
_lowerCAmelCase : List[Any] = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Dict = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Dict = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 44 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def a_ ( _UpperCAmelCase : str = "laptop" ) -> DataFrame:
__snake_case : List[Any] = f'''https://www.amazon.in/laptop/s?k={product}'''
__snake_case : Optional[Any] = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
__snake_case : List[str] = BeautifulSoup(requests.get(_UpperCAmelCase ,headers=_UpperCAmelCase ).text )
# Initialize a Pandas dataframe with the column titles
__snake_case : Optional[int] = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' ,attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} ,) ,soup.find_all('div' ,attrs={'class': 'a-row a-size-base a-color-base'} ) ,):
try:
__snake_case : List[Any] = item.ha.text
__snake_case : Optional[int] = 'https://www.amazon.in/' + item.ha.a['href']
__snake_case : Optional[Any] = item.find('span' ,attrs={'class': 'a-offscreen'} ).text
try:
__snake_case : Any = item.find('span' ,attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
__snake_case : Optional[Any] = 'Not available'
try:
__snake_case : Any = (
'₹'
+ item.find(
'span' ,attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
__snake_case : Optional[Any] = ''
try:
__snake_case : List[str] = float(
(
(
float(product_mrp.strip('₹' ).replace(',' ,'' ) )
- float(product_price.strip('₹' ).replace(',' ,'' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' ,'' ) )
)
* 1_00 )
except ValueError:
__snake_case : Optional[Any] = float('nan' )
except AttributeError:
pass
__snake_case : Tuple = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__snake_case : Any = ' '
__snake_case : Tuple = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
A__ : List[Any] = '''headphones'''
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = 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 A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(__a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = BasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : Any = WordpieceTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a = random.Random()
def _snake_case ( _snake_case : Optional[int] , _snake_case : Union[str, Any]=1.0 , _snake_case : Any=None , _snake_case : Optional[Any]=None ) -> Optional[Any]:
'''simple docstring'''
if rng is None:
_A = global_rng
_A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : str=400 , _UpperCAmelCase : Optional[Any]=2_000 , _UpperCAmelCase : int=24 , _UpperCAmelCase : List[str]=24 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : str=16_000 , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Dict=True , ):
_A = parent
_A = batch_size
_A = min_seq_length
_A = max_seq_length
_A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_A = feature_size
_A = num_mel_bins
_A = padding_value
_A = sampling_rate
_A = return_attention_mask
_A = do_normalize
def lowerCAmelCase_ ( self : Any ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict=False , _UpperCAmelCase : str=False ):
def _flatten(_UpperCAmelCase : List[Any] ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
_A = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_A = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_A = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = SpeechaTextFeatureExtractor if is_speech_available() else None
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = SpeechaTextFeatureExtractionTester(self )
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Any ):
self.assertTrue(np.all(np.mean(_UpperCAmelCase , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase , axis=0 ) - 1 ) < 1E-3 ) )
def lowerCAmelCase_ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test feature size
_A = feature_extractor(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
_A = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_features
_A = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_features
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test batched
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_A = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_A = np.asarray(_UpperCAmelCase )
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = ['longest', 'max_length', 'do_not_pad']
_A = [None, 16, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
_A = feature_extractor(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase )
_A = inputs.input_features
_A = inputs.attention_mask
_A = [np.sum(_UpperCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowerCAmelCase_ ( self : Any ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = ['longest', 'max_length', 'do_not_pad']
_A = [None, 16, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
_A = feature_extractor(
_UpperCAmelCase , max_length=_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase )
_A = inputs.input_features
_A = inputs.attention_mask
_A = [np.sum(_UpperCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowerCAmelCase_ ( self : int ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='max_length' , max_length=4 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowerCAmelCase_ ( self : Dict ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='longest' , max_length=4 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='longest' , max_length=16 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def lowerCAmelCase_ ( self : List[str] ):
import torch
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = np.random.rand(100 , 32 ).astype(np.floataa )
_A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Dict ):
from datasets import load_dataset
_A = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
_A = ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowerCAmelCase_ ( self : Dict ):
# fmt: off
_A = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
_A = self._load_datasamples(1 )
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = feature_extractor(_UpperCAmelCase , return_tensors='pt' ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GraphormerForGraphClassification''',
'''GraphormerModel''',
'''GraphormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 354 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A = datasets.logging.get_logger(__name__)
A = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
A = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
A = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
'''simple docstring'''
def _lowerCamelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def _lowerCamelCase ( self , _UpperCAmelCase ):
if self.config_name == "default":
__a : List[str] = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
__a : List[str] = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=False ):
if gpus is None:
__a : str = 1 if torch.cuda.is_available() else 0
__a : Optional[Any] = {'''src''': sources, '''mt''': predictions, '''ref''': references}
__a : Dict = [dict(zip(_UpperCAmelCase , _UpperCAmelCase ) ) for t in zip(*data.values() )]
__a , __a : int = self.scorer.predict(_UpperCAmelCase , gpus=_UpperCAmelCase , progress_bar=_UpperCAmelCase )
return {"mean_score": mean_score, "scores": scores} | 188 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self : Union[str, Any] , *,
snake_case_ : int = 4 , snake_case_ : int = 7_6_8 , snake_case_ : int , snake_case_ : List[Any] , ):
super().__init__()
_UpperCAmelCase = nn.Parameter(torch.zeros(snake_case_ ) )
# parameters for additional clip time embeddings
_UpperCAmelCase = nn.Linear(snake_case_ , snake_case_ )
_UpperCAmelCase = nn.Linear(snake_case_ , snake_case_ )
# parameters for encoder hidden states
_UpperCAmelCase = clip_extra_context_tokens
_UpperCAmelCase = nn.Linear(
snake_case_ , self.clip_extra_context_tokens * cross_attention_dim )
_UpperCAmelCase = nn.Linear(snake_case_ , snake_case_ )
_UpperCAmelCase = nn.LayerNorm(snake_case_ )
def lowercase ( self : Union[str, Any] , *, snake_case_ : Dict , snake_case_ : int , snake_case_ : List[Any] , snake_case_ : Tuple ):
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
_UpperCAmelCase = image_embeddings.shape[0]
_UpperCAmelCase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
_UpperCAmelCase = classifier_free_guidance_embeddings.expand(
snake_case_ , -1 )
_UpperCAmelCase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
_UpperCAmelCase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
_UpperCAmelCase = self.embedding_proj(snake_case_ )
_UpperCAmelCase = self.clip_image_embeddings_project_to_time_embeddings(snake_case_ )
_UpperCAmelCase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
_UpperCAmelCase = self.clip_extra_context_tokens_proj(snake_case_ )
_UpperCAmelCase = clip_extra_context_tokens.reshape(snake_case_ , -1 , self.clip_extra_context_tokens )
_UpperCAmelCase = clip_extra_context_tokens.permute(0 , 2 , 1 )
_UpperCAmelCase = self.encoder_hidden_states_proj(snake_case_ )
_UpperCAmelCase = self.text_encoder_hidden_states_norm(snake_case_ )
_UpperCAmelCase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 22 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _lowercase (a_ , a_ , a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = AltDiffusionPipeline
lowercase__ = TEXT_TO_IMAGE_PARAMS
lowercase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
def _lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
UpperCamelCase_ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=snake_case__ , set_alpha_to_one=snake_case__ , )
torch.manual_seed(0 )
UpperCamelCase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
UpperCamelCase_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
UpperCamelCase_ = CLIPTextModel(snake_case__ )
UpperCamelCase_ = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
UpperCamelCase_ = 77
UpperCamelCase_ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _lowerCamelCase ( self , snake_case__ , snake_case__=0 ):
'''simple docstring'''
if str(snake_case__ ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(snake_case__ )
else:
UpperCamelCase_ = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _lowerCamelCase ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def _lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_ = self.get_dummy_components()
torch.manual_seed(0 )
UpperCamelCase_ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
UpperCamelCase_ = RobertaSeriesModelWithTransformation(snake_case__ )
UpperCamelCase_ = text_encoder
UpperCamelCase_ = AltDiffusionPipeline(**snake_case__ )
UpperCamelCase_ = alt_pipe.to(snake_case__ )
alt_pipe.set_progress_bar_config(disable=snake_case__ )
UpperCamelCase_ = self.get_dummy_inputs(snake_case__ )
UpperCamelCase_ = "A photo of an astronaut"
UpperCamelCase_ = alt_pipe(**snake_case__ )
UpperCamelCase_ = output.images
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase_ = np.array(
[0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = PNDMScheduler(skip_prk_steps=snake_case__ )
torch.manual_seed(0 )
UpperCamelCase_ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
UpperCamelCase_ = RobertaSeriesModelWithTransformation(snake_case__ )
UpperCamelCase_ = text_encoder
UpperCamelCase_ = AltDiffusionPipeline(**snake_case__ )
UpperCamelCase_ = alt_pipe.to(snake_case__ )
alt_pipe.set_progress_bar_config(disable=snake_case__ )
UpperCamelCase_ = self.get_dummy_inputs(snake_case__ )
UpperCamelCase_ = alt_pipe(**snake_case__ )
UpperCamelCase_ = output.images
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase_ = np.array(
[0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class _lowercase (unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , safety_checker=snake_case__ )
UpperCamelCase_ = alt_pipe.to(snake_case__ )
alt_pipe.set_progress_bar_config(disable=snake_case__ )
UpperCamelCase_ = "A painting of a squirrel eating a burger"
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = alt_pipe([prompt] , generator=snake_case__ , guidance_scale=6.0 , num_inference_steps=20 , output_type="np" )
UpperCamelCase_ = output.images
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase_ = np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = DDIMScheduler.from_pretrained("BAAI/AltDiffusion" , subfolder="scheduler" )
UpperCamelCase_ = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , scheduler=snake_case__ , safety_checker=snake_case__ )
UpperCamelCase_ = alt_pipe.to(snake_case__ )
alt_pipe.set_progress_bar_config(disable=snake_case__ )
UpperCamelCase_ = "A painting of a squirrel eating a burger"
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = alt_pipe([prompt] , generator=snake_case__ , num_inference_steps=2 , output_type="numpy" )
UpperCamelCase_ = output.images
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase_ = np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 128 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@property
def UpperCamelCase ( self ):
torch.manual_seed(0 )
A__ = UNetaDModel(
block_out_channels=(32, 64),layers_per_block=2,sample_size=32,in_channels=3,out_channels=3,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),)
return model
def UpperCamelCase ( self ):
A__ = self.dummy_uncond_unet
A__ = ScoreSdeVeScheduler()
A__ = ScoreSdeVePipeline(unet=__lowerCamelCase,scheduler=__lowerCamelCase )
sde_ve.to(__lowerCamelCase )
sde_ve.set_progress_bar_config(disable=__lowerCamelCase )
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=2,output_type='''numpy''',generator=__lowerCamelCase ).images
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=2,output_type='''numpy''',generator=__lowerCamelCase,return_dict=__lowerCamelCase )[
0
]
A__ = image[0, -3:, -3:, -1]
A__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
A__ = '''google/ncsnpp-church-256'''
A__ = UNetaDModel.from_pretrained(__lowerCamelCase )
A__ = ScoreSdeVeScheduler.from_pretrained(__lowerCamelCase )
A__ = ScoreSdeVePipeline(unet=__lowerCamelCase,scheduler=__lowerCamelCase )
sde_ve.to(__lowerCamelCase )
sde_ve.set_progress_bar_config(disable=__lowerCamelCase )
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=10,output_type='''numpy''',generator=__lowerCamelCase ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A__ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 350 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ):
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = '''A painting of a squirrel eating a burger '''
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__lowerCamelCase )
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = generator.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def UpperCamelCase ( self ):
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(
'''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = '''A painting of a squirrel eating a burger '''
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images
A__ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A__ = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 39 | 0 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase = "cpu" , _UpperCamelCase = None ):
__lowerCAmelCase : Any = torch.load(snake_case_ , map_location=snake_case_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(snake_case_ , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
__lowerCAmelCase : Any = v.half()
if save_path is None: # overwrite src_path
__lowerCAmelCase : int = src_path
torch.save(snake_case_ , snake_case_ )
if __name__ == "__main__":
fire.Fire(convert) | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
def lowercase__ ( snake_case_ :Union[tf.Tensor, np.ndarray] ):
if isinstance(snake_case_ , np.ndarray ):
return list(tensor.shape )
__UpperCAmelCase = tf.shape(snake_case_ )
if tensor.shape == tf.TensorShape(snake_case_ ):
return dynamic
__UpperCAmelCase = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(snake_case_ )]
def lowercase__ ( snake_case_ :tf.Tensor , snake_case_ :Optional[int] = None , snake_case_ :Optional[str] = None ):
return tf.nn.softmax(logits=logits + 1E-9 , axis=snake_case_ , name=snake_case_ )
def lowercase__ ( snake_case_ :int , snake_case_ :Union[str, Any] , snake_case_ :str , snake_case_ :Union[str, Any]=1E-5 , snake_case_ :List[str]=-1 ):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case_ , snake_case_ ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
__UpperCAmelCase , __UpperCAmelCase = tf.nn.moments(snake_case_ , axes=[axis] , keepdims=snake_case_ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__UpperCAmelCase = [1] * inputs.shape.rank
__UpperCAmelCase = shape_list(snake_case_ )[axis]
__UpperCAmelCase = tf.reshape(snake_case_ , snake_case_ )
__UpperCAmelCase = tf.reshape(snake_case_ , snake_case_ )
# Compute layer normalization using the batch_normalization
# function.
__UpperCAmelCase = tf.nn.batch_normalization(
snake_case_ , snake_case_ , snake_case_ , offset=snake_case_ , scale=snake_case_ , variance_epsilon=snake_case_ , )
return outputs
def lowercase__ ( snake_case_ :Optional[int] , snake_case_ :List[str]=0 , snake_case_ :Optional[Any]=-1 ):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__UpperCAmelCase = tf.shape(snake_case_ )
__UpperCAmelCase = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__UpperCAmelCase = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(snake_case_ , snake_case_ )
def lowercase__ ( snake_case_ :tf.Tensor ):
if not isinstance(snake_case_ , tf.Tensor ):
__UpperCAmelCase = tf.convert_to_tensor(snake_case_ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__UpperCAmelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__UpperCAmelCase = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__UpperCAmelCase = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowercase__ ( snake_case_ :tf.Tensor , snake_case_ :int , snake_case_ :str = "input_ids" ):
tf.debugging.assert_less(
snake_case_ , tf.cast(snake_case_ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case_ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowercase__ ( snake_case_ :List[Any] , snake_case_ :List[Any] , snake_case_ :List[str] ):
__UpperCAmelCase = 64_512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__UpperCAmelCase = [x for x in data if len(snake_case_ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
__UpperCAmelCase = np.asarray(snake_case_ )
__UpperCAmelCase = 1
__UpperCAmelCase = np.array_split(snake_case_ , snake_case_ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__UpperCAmelCase = np.array_split(snake_case_ , snake_case_ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(snake_case_ ):
__UpperCAmelCase = chunk_data
else:
__UpperCAmelCase = data
def lowercase__ ( snake_case_ :str , snake_case_ :List[str] ):
if name in group.attrs:
__UpperCAmelCase = [n.decode('''utf8''' ) if hasattr(snake_case_ , '''decode''' ) else n for n in group.attrs[name]]
else:
__UpperCAmelCase = []
__UpperCAmelCase = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(snake_case_ , '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def lowercase__ ( snake_case_ :Tuple ):
def _expand_single_ad_tensor(snake_case_ :Optional[int] ):
if isinstance(snake_case_ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(snake_case_ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , snake_case_ )
| 332 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
UpperCamelCase = ["""image_processor""", """tokenizer"""]
UpperCamelCase = """CLIPImageProcessor"""
UpperCamelCase = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self : int , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : str ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __lowercase , )
UpperCAmelCase_ = kwargs.pop("feature_extractor" )
UpperCAmelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__lowercase , __lowercase )
def __call__( self : Tuple , _UpperCAmelCase : Any=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Optional[int]=None , **_UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
UpperCAmelCase_ = self.tokenizer(__lowercase , return_tensors=__lowercase , **__lowercase )
if images is not None:
UpperCAmelCase_ = self.image_processor(__lowercase , return_tensors=__lowercase , **__lowercase )
if text is not None and images is not None:
UpperCAmelCase_ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__lowercase ) , tensor_type=__lowercase )
def lowercase__ ( self : Dict , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__lowercase , **__lowercase )
def lowercase__ ( self : Dict , *_UpperCAmelCase : Dict , **_UpperCAmelCase : List[Any] ) -> str:
'''simple docstring'''
return self.tokenizer.decode(*__lowercase , **__lowercase )
@property
def lowercase__ ( self : Any ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = self.tokenizer.model_input_names
UpperCAmelCase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowercase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __lowercase , )
return self.image_processor_class
@property
def lowercase__ ( self : Any ) -> Dict:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __lowercase , )
return self.image_processor
| 350 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
"""roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''roberta'''
def __init__( self : int , _UpperCAmelCase : List[Any]=50265 , _UpperCAmelCase : str=768 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : int=12 , _UpperCAmelCase : Tuple=3072 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Optional[int]=512 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : Optional[Any]=1e-12 , _UpperCAmelCase : Dict=1 , _UpperCAmelCase : str=0 , _UpperCAmelCase : int=2 , _UpperCAmelCase : Tuple="absolute" , _UpperCAmelCase : Any=True , _UpperCAmelCase : int=None , **_UpperCAmelCase : List[str] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = type_vocab_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = position_embedding_type
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = classifier_dropout
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def lowercase__ ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 241 | 0 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def _snake_case ( UpperCamelCase : Any ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] = module
UpperCAmelCase : Union[str, Any] = nn.Sequential(
nn.Linear(module.in_features , _snake_case , bias=_snake_case ) , nn.Linear(_snake_case , module.out_features , bias=_snake_case ) , )
UpperCAmelCase : Any = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=_snake_case )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
return self.module(_snake_case , *_snake_case , **_snake_case ) + self.adapter(_snake_case )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
__lowerCAmelCase : Tuple = '''bigscience/bloom-1b7'''
# Constant values
__lowerCAmelCase : Union[str, Any] = 2.109_6595_5269_2574
__lowerCAmelCase : Optional[int] = '''Hello my name is'''
__lowerCAmelCase : List[str] = set()
EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I' )
EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n' )
EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University' )
__lowerCAmelCase : Union[str, Any] = 10
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : int = AutoTokenizer.from_pretrained(self.model_name )
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# Models and tokenizer
UpperCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""" )
UpperCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = self.model_abit.config
self.assertTrue(hasattr(_snake_case , """quantization_config""" ) )
UpperCAmelCase : Any = config.to_dict()
UpperCAmelCase : Any = config.to_diff_dict()
UpperCAmelCase : Union[str, Any] = config.to_json_string()
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
UpperCAmelCase : int = self.model_fpaa.get_memory_footprint()
UpperCAmelCase : Optional[Any] = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
UpperCAmelCase : Optional[int] = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(_snake_case , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self.tokenizer(self.input_text , return_tensors="""pt""" )
UpperCAmelCase : List[Any] = self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_snake_case ) , self.EXPECTED_OUTPUTS )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] = BitsAndBytesConfig()
UpperCAmelCase : int = True
UpperCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_snake_case , device_map="""auto""" )
UpperCAmelCase : str = self.tokenizer(self.input_text , return_tensors="""pt""" )
UpperCAmelCase : int = model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_snake_case ) , self.EXPECTED_OUTPUTS )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
with self.assertRaises(_snake_case ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(_snake_case )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = BitsAndBytesConfig()
with self.assertRaises(_snake_case ):
UpperCAmelCase : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_snake_case , load_in_abit=_snake_case , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
with self.assertRaises(_snake_case ):
# Tries with `str`
self.model_abit.to("""cpu""" )
with self.assertRaises(_snake_case ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(_snake_case ):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0""" ) )
with self.assertRaises(_snake_case ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(_snake_case ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
UpperCAmelCase : Any = self.tokenizer(self.input_text , return_tensors="""pt""" )
UpperCAmelCase : Any = self.model_fpaa.to(torch.floataa )
UpperCAmelCase : List[str] = self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
UpperCAmelCase : Optional[int] = self.model_fpaa.to("""cpu""" )
# Check this does not throw an error
UpperCAmelCase : List[str] = self.model_fpaa.half()
# Check this does not throw an error
UpperCAmelCase : List[Any] = self.model_fpaa.float()
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[str] = AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=_snake_case , device_map="""auto""" )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@classmethod
def SCREAMING_SNAKE_CASE ( cls ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = '''t5-small'''
UpperCAmelCase : List[str] = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
UpperCAmelCase : str = AutoTokenizer.from_pretrained(cls.model_name )
UpperCAmelCase : str = '''Translate in German: Hello, my dog is cute'''
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
from transformers import TaForConditionalGeneration
UpperCAmelCase : List[Any] = TaForConditionalGeneration._keep_in_fpaa_modules
UpperCAmelCase : List[str] = None
# test with `t5-small`
UpperCAmelCase : Optional[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
UpperCAmelCase : Optional[Any] = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
UpperCAmelCase : Optional[Any] = model.generate(**_snake_case )
# test with `flan-t5-small`
UpperCAmelCase : Optional[int] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_snake_case , device_map="""auto""" )
UpperCAmelCase : Optional[int] = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
UpperCAmelCase : Union[str, Any] = model.generate(**_snake_case )
UpperCAmelCase : Optional[int] = modules
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
UpperCAmelCase : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
UpperCAmelCase : str = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
UpperCAmelCase : Optional[Any] = model.generate(**_snake_case )
# test with `flan-t5-small`
UpperCAmelCase : Dict = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_snake_case , device_map="""auto""" )
UpperCAmelCase : int = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
UpperCAmelCase : Optional[int] = model.generate(**_snake_case )
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
# model_name
UpperCAmelCase : Tuple = '''bigscience/bloom-560m'''
UpperCAmelCase : Any = '''t5-small'''
# Different types of model
UpperCAmelCase : Dict = AutoModel.from_pretrained(self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
# Sequence classification model
UpperCAmelCase : List[Any] = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
# CausalLM model
UpperCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_snake_case , device_map="""auto""" )
# Seq2seq model
UpperCAmelCase : str = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=_snake_case , device_map="""auto""" )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
UpperCAmelCase : Optional[int] = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=_snake_case , device_map="""balanced""" )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
UpperCAmelCase : str = self.tokenizer(self.input_text , return_tensors="""pt""" )
# Second real batch
UpperCAmelCase : int = model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=_snake_case ) , self.EXPECTED_OUTPUTS )
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Any = '''facebook/opt-350m'''
super().setUp()
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
if version.parse(importlib.metadata.version("""bitsandbytes""" ) ) < version.parse("""0.37.0""" ):
return
# Step 1: freeze all parameters
UpperCAmelCase : Dict = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_snake_case )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
UpperCAmelCase : str = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
UpperCAmelCase : List[str] = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(_snake_case ) ):
UpperCAmelCase : Any = LoRALayer(module.q_proj , rank=16 )
UpperCAmelCase : Any = LoRALayer(module.k_proj , rank=16 )
UpperCAmelCase : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
UpperCAmelCase : Optional[int] = self.tokenizer("""Test batch """ , return_tensors="""pt""" ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
UpperCAmelCase : str = model.forward(**_snake_case )
out.logits.norm().backward()
for module in model.modules():
if isinstance(_snake_case , _snake_case ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(_snake_case , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
__lowerCAmelCase : str = '''gpt2-xl'''
__lowerCAmelCase : str = 3.3191_8548_5415_2187
| 109 |
from __future__ import annotations
from PIL import Image
# Define glider example
__lowerCAmelCase : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
__lowerCAmelCase : Union[str, Any] = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def UpperCAmelCase_ ( __lowerCAmelCase ) -> list[list[int]]:
__lowercase : int = []
for i in range(len(__lowerCAmelCase ) ):
__lowercase : Optional[int] = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__lowercase : Union[str, Any] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(__lowerCAmelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(__lowerCAmelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(__lowerCAmelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__lowercase : List[Any] = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(__lowerCAmelCase )
return next_generation
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> list[Image.Image]:
__lowercase : Tuple = []
for _ in range(__lowerCAmelCase ):
# Create output image
__lowercase : Tuple = Image.new('''RGB''' , (len(cells[0] ), len(__lowerCAmelCase )) )
__lowercase : Dict = img.load()
# Save cells to image
for x in range(len(__lowerCAmelCase ) ):
for y in range(len(cells[0] ) ):
__lowercase : int = 255 - cells[y][x] * 255
__lowercase : Tuple = (colour, colour, colour)
# Save image
images.append(__lowerCAmelCase )
__lowercase : Tuple = new_generation(__lowerCAmelCase )
return images
if __name__ == "__main__":
__lowerCAmelCase : Any = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| 156 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase :
def __init__( self : Tuple , __snake_case : List[str] , __snake_case : int=13 , __snake_case : Optional[Any]=7 , __snake_case : int=True , __snake_case : Optional[Any]=True , __snake_case : Optional[Any]=True , __snake_case : List[Any]=True , __snake_case : int=True , __snake_case : int=False , __snake_case : Optional[Any]=False , __snake_case : Any=False , __snake_case : Optional[Any]=2 , __snake_case : Tuple=99 , __snake_case : int=0 , __snake_case : int=32 , __snake_case : Any=5 , __snake_case : List[Any]=4 , __snake_case : List[str]=0.1 , __snake_case : List[Any]=0.1 , __snake_case : Dict=5_12 , __snake_case : List[str]=2 , __snake_case : Union[str, Any]=0.02 , __snake_case : Dict=2 , __snake_case : List[str]=4 , __snake_case : Union[str, Any]="last" , __snake_case : Dict=True , __snake_case : Any=None , __snake_case : List[str]=0 , ) -> List[str]:
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_lengths
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = gelu_activation
_lowerCAmelCase = sinusoidal_embeddings
_lowerCAmelCase = causal
_lowerCAmelCase = asm
_lowerCAmelCase = n_langs
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_special
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = summary_type
_lowerCAmelCase = use_proj
_lowerCAmelCase = scope
_lowerCAmelCase = bos_token_id
def lowercase__ ( self : List[Any] ) -> Union[str, Any]:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_input_lengths:
_lowerCAmelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , 2 ).float()
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowercase__ ( self : Union[str, Any] ) -> Optional[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def lowercase__ ( self : Optional[int] , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : str , __snake_case : str , __snake_case : Dict , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : Optional[int] , ) -> List[Any]:
_lowerCAmelCase = XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case , lengths=__snake_case , langs=__snake_case )
_lowerCAmelCase = model(__snake_case , langs=__snake_case )
_lowerCAmelCase = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Tuple , __snake_case : Any , __snake_case : str , __snake_case : Optional[Any] , __snake_case : int , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : Optional[int] , ) -> Optional[int]:
_lowerCAmelCase = XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : int , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : int , __snake_case : Dict , ) -> List[Any]:
_lowerCAmelCase = XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case )
_lowerCAmelCase = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
_lowerCAmelCase = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase__ ( self : Union[str, Any] , __snake_case : List[str] , __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : Dict , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : int , ) -> str:
_lowerCAmelCase = XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case )
_lowerCAmelCase = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
_lowerCAmelCase = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((_lowerCAmelCase ) , ) = result_with_labels.to_tuple()
_lowerCAmelCase = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((_lowerCAmelCase ) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def lowercase__ ( self : str , __snake_case : Any , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : Tuple , __snake_case : str , ) -> Any:
_lowerCAmelCase = XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case )
_lowerCAmelCase = model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase__ ( self : Optional[int] , __snake_case : Dict , __snake_case : Tuple , __snake_case : List[str] , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , ) -> int:
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : Dict , __snake_case : str , __snake_case : Tuple , __snake_case : str , __snake_case : str , __snake_case : Dict , __snake_case : str , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , ) -> Union[str, Any]:
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : Tuple ) -> List[Any]:
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
_lowercase: Optional[int] = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase: List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase: List[str] = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowercase__ ( self : str , __snake_case : Dict , __snake_case : Tuple , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Optional[int] ) -> Optional[Any]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def lowercase__ ( self : Tuple , __snake_case : List[Any] , __snake_case : int , __snake_case : Any=False ) -> Tuple:
_lowerCAmelCase = super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
_lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def lowercase__ ( self : Union[str, Any] ) -> Union[str, Any]:
_lowerCAmelCase = XLMModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def lowercase__ ( self : Union[str, Any] ) -> int:
self.config_tester.run_common_tests()
def lowercase__ ( self : Tuple ) -> Dict:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def lowercase__ ( self : List[str] ) -> Tuple:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def lowercase__ ( self : int ) -> Dict:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def lowercase__ ( self : Optional[int] ) -> str:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def lowercase__ ( self : str ) -> Dict:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def lowercase__ ( self : Optional[int] ) -> List[str]:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def lowercase__ ( self : int ) -> Union[str, Any]:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def lowercase__ ( self : Any , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Tuple , __snake_case : Any=False , __snake_case : Any=1 ) -> List[Any]:
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
_lowerCAmelCase = min_length + idx + 1
_lowerCAmelCase = min_length + idx + 1
_lowerCAmelCase = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def lowercase__ ( self : List[Any] , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : int , __snake_case : List[Any]=False , __snake_case : Dict=1 ) -> Dict:
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
_lowerCAmelCase = min_length + idx + 1
_lowerCAmelCase = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def lowercase__ ( self : Union[str, Any] ) -> str:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self : Dict ) -> Optional[Any]:
_lowerCAmelCase = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(__snake_case )
_lowerCAmelCase = torch.tensor([[14, 4_47]] , dtype=torch.long , device=__snake_case ) # the president
_lowerCAmelCase = [
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_lowerCAmelCase = model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 367 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A__ : str =5_00_00
A__ : Optional[int] =50_00
A__ , A__ : Optional[int] =os.path.split(__file__)
A__ : Tuple =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for i in range(lowerCAmelCase ):
_lowerCAmelCase = dataset[i]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for i in range(0 , len(lowerCAmelCase ) , lowerCAmelCase ):
_lowerCAmelCase = dataset[i : i + batch_size]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with dataset.formatted_as(type=lowerCAmelCase ):
for i in range(lowerCAmelCase ):
_lowerCAmelCase = dataset[i]
@get_duration
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with dataset.formatted_as(type=lowerCAmelCase ):
for i in range(0 , lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = dataset[i : i + batch_size]
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
_lowerCAmelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
]
_lowerCAmelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("""generating dataset""" )
_lowerCAmelCase = datasets.Features(
{"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
_lowerCAmelCase = generate_example_dataset(
os.path.join(lowerCAmelCase , """dataset.arrow""" ) , lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes={"""list""": (1_00,)} , )
print("""first set of iterations""" )
for func, kwargs in functions:
print(func.__name__ , str(lowerCAmelCase ) )
_lowerCAmelCase = func(lowerCAmelCase , **lowerCAmelCase )
print("""shuffling dataset""" )
_lowerCAmelCase = dataset.shuffle()
print("""Second set of iterations (after shuffling""" )
for func, kwargs in functions_shuffled:
print("""shuffled """ , func.__name__ , str(lowerCAmelCase ) )
_lowerCAmelCase = func(
lowerCAmelCase , **lowerCAmelCase )
with open(lowerCAmelCase , """wb""" ) as f:
f.write(json.dumps(lowerCAmelCase ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 220 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : list[list[float]] ) ->list[list[float]]:
"""simple docstring"""
__snake_case : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_snake_case ):
if len(_snake_case ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_snake_case ) )
return data_lists
def lowercase ( _snake_case : list[list[float]] , _snake_case : list[int] ) ->list[list[float]]:
"""simple docstring"""
__snake_case : list[list[float]] = []
for dlist, weight in zip(_snake_case , _snake_case ):
__snake_case : Optional[Any] = min(_snake_case )
__snake_case : Union[str, Any] = max(_snake_case )
__snake_case : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__snake_case : Any = f"""Invalid weight of {weight:f} provided"""
raise ValueError(_snake_case )
score_lists.append(_snake_case )
return score_lists
def lowercase ( _snake_case : list[list[float]] ) ->list[float]:
"""simple docstring"""
__snake_case : list[float] = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(_snake_case ):
__snake_case : Dict = final_scores[j] + ele
return final_scores
def lowercase ( _snake_case : list[list[float]] , _snake_case : list[int] ) ->list[list[float]]:
"""simple docstring"""
__snake_case : str = get_data(_snake_case )
__snake_case : Optional[int] = calculate_each_score(_snake_case , _snake_case )
__snake_case : Optional[int] = generate_final_scores(_snake_case )
# append scores to source data
for i, ele in enumerate(_snake_case ):
source_data[i].append(_snake_case )
return source_data
| 102 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
SCREAMING_SNAKE_CASE : List[Any] = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def lowercase ( _snake_case : Optional[int] , _snake_case : Optional[int] ) ->Tuple:
"""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 lowercase ( _snake_case : List[str] ) ->Optional[int]:
"""simple docstring"""
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_snake_case )
def lowercase ( _snake_case : Optional[Any] , _snake_case : Dict ) ->Any:
"""simple docstring"""
__snake_case : List[Any] = tmp_path_factory.getbasetemp() / '''cache'''
__snake_case : int = test_hf_cache_home / '''datasets'''
__snake_case : Tuple = test_hf_cache_home / '''metrics'''
__snake_case : List[str] = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_snake_case ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_snake_case ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_snake_case ) )
__snake_case : Optional[int] = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_snake_case ) )
__snake_case : Tuple = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_snake_case ) )
@pytest.fixture(autouse=_snake_case , scope='''session''' )
def lowercase ( ) ->Any:
"""simple docstring"""
datasets.disable_progress_bar()
@pytest.fixture(autouse=_snake_case )
def lowercase ( _snake_case : Tuple ) ->Union[str, Any]:
"""simple docstring"""
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _snake_case )
@pytest.fixture
def lowercase ( _snake_case : Any ) ->Optional[Any]:
"""simple docstring"""
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _snake_case )
| 102 | 1 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = [[] for _ in range(_UpperCAmelCase )]
__a = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1 or len(_UpperCAmelCase ) <= key:
return input_string
for position, character in enumerate(_UpperCAmelCase ):
__a = position % (lowest * 2) # puts it in bounds
__a = min(_UpperCAmelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_UpperCAmelCase )
__a = [''''''.join(_UpperCAmelCase ) for row in temp_grid]
__a = ''''''.join(_UpperCAmelCase )
return output_string
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = []
__a = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1:
return input_string
__a = [[] for _ in range(_UpperCAmelCase )] # generates template
for position in range(len(_UpperCAmelCase ) ):
__a = position % (lowest * 2) # puts it in bounds
__a = min(_UpperCAmelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('''*''' )
__a = 0
for row in temp_grid: # fills in the characters
__a = input_string[counter : counter + len(_UpperCAmelCase )]
grid.append(list(_UpperCAmelCase ) )
counter += len(_UpperCAmelCase )
__a = '''''' # reads as zigzag
for position in range(len(_UpperCAmelCase ) ):
__a = position % (lowest * 2) # puts it in bounds
__a = min(_UpperCAmelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def __snake_case ( _UpperCAmelCase ):
__a = {}
for key_guess in range(1 , len(_UpperCAmelCase ) ): # tries every key
__a = decrypt(_UpperCAmelCase , _UpperCAmelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 131 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _A :
def __init__( self : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple=13 , __SCREAMING_SNAKE_CASE : Union[str, Any]=32 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Dict=3 , __SCREAMING_SNAKE_CASE : Any=16 , __SCREAMING_SNAKE_CASE : Optional[int]=[1, 2, 1] , __SCREAMING_SNAKE_CASE : Optional[int]=[2, 2, 4] , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Tuple=2.0 , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : Dict=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : str=0.1 , __SCREAMING_SNAKE_CASE : int="gelu" , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Dict=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-5 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Optional[int]=10 , __SCREAMING_SNAKE_CASE : int=8 , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = image_size
__a = patch_size
__a = num_channels
__a = embed_dim
__a = depths
__a = num_heads
__a = window_size
__a = mlp_ratio
__a = qkv_bias
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = drop_path_rate
__a = hidden_act
__a = use_absolute_embeddings
__a = patch_norm
__a = layer_norm_eps
__a = initializer_range
__a = is_training
__a = scope
__a = use_labels
__a = type_sequence_label_size
__a = encoder_stride
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__a = self.get_config()
return config, pixel_values, labels
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any]):
'''simple docstring'''
__a = SwinvaModel(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE)
__a = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
__a = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim))
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
__a = 1
__a = SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__a = model(__SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size))
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
__a = self.type_sequence_label_size
__a = SwinvaForImageClassification(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _A ( __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ : Dict = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
UpperCamelCase__ : Optional[int] = (
{'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ : int = False
UpperCamelCase__ : Tuple = False
UpperCamelCase__ : Optional[Any] = False
UpperCamelCase__ : Optional[Any] = False
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = SwinvaModelTester(self)
__a = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , embed_dim=37)
def _lowerCamelCase ( self : List[Any]):
'''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 _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)
@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip(reason='''Swinv2 does not use inputs_embeds''')
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
pass
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__SCREAMING_SNAKE_CASE)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
__a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear))
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__SCREAMING_SNAKE_CASE)
__a = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = True
for model_class in self.all_model_classes:
__a = True
__a = False
__a = True
__a = model_class(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
__a = outputs.attentions
__a = len(self.model_tester.depths)
self.assertEqual(len(__SCREAMING_SNAKE_CASE) , __SCREAMING_SNAKE_CASE)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a = True
__a = config.window_size**2
__a = model_class(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
__a = outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE) , __SCREAMING_SNAKE_CASE)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__a = len(__SCREAMING_SNAKE_CASE)
# Check attention is always last and order is fine
__a = True
__a = True
__a = model_class(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
if hasattr(self.model_tester , '''num_hidden_states_types'''):
__a = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__a = 2
self.assertEqual(out_len + added_hidden_states , len(__SCREAMING_SNAKE_CASE))
__a = outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE) , __SCREAMING_SNAKE_CASE)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str]):
'''simple docstring'''
__a = model_class(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
__a = outputs.hidden_states
__a = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths) + 1)
self.assertEqual(len(__SCREAMING_SNAKE_CASE) , __SCREAMING_SNAKE_CASE)
# Swinv2 has a different seq_length
__a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
__a = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
__a = outputs.reshaped_hidden_states
self.assertEqual(len(__SCREAMING_SNAKE_CASE) , __SCREAMING_SNAKE_CASE)
__a , __a , __a , __a = reshaped_hidden_states[0].shape
__a = (
reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , height * width).permute(0 , 2 , 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__a = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = 3
__a = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
__a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
__a = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__a = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__a = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width))
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)
@slow
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE)
self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = _config_zero_init(__SCREAMING_SNAKE_CASE)
for model_class in self.all_model_classes:
__a = model_class(config=__SCREAMING_SNAKE_CASE)
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class _A ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''')
if is_vision_available()
else None
)
@slow
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''').to(
__SCREAMING_SNAKE_CASE)
__a = self.default_image_processor
__a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
__a = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)
# forward pass
with torch.no_grad():
__a = model(**__SCREAMING_SNAKE_CASE)
# verify the logits
__a = torch.Size((1, 1_000))
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE)
__a = torch.tensor([-0.39_47, -0.43_06, 0.00_26]).to(__SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4))
| 131 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Optional[Any] = 384
_UpperCAmelCase : Optional[int] = 7
if "tiny" in model_name:
_UpperCAmelCase : int = 96
_UpperCAmelCase : int = (2, 2, 6, 2)
_UpperCAmelCase : Tuple = (3, 6, 12, 24)
elif "small" in model_name:
_UpperCAmelCase : int = 96
_UpperCAmelCase : Tuple = (2, 2, 18, 2)
_UpperCAmelCase : Any = (3, 6, 12, 24)
elif "base" in model_name:
_UpperCAmelCase : List[Any] = 128
_UpperCAmelCase : List[str] = (2, 2, 18, 2)
_UpperCAmelCase : Any = (4, 8, 16, 32)
_UpperCAmelCase : Any = 12
_UpperCAmelCase : Optional[Any] = 512
elif "large" in model_name:
_UpperCAmelCase : List[str] = 192
_UpperCAmelCase : List[str] = (2, 2, 18, 2)
_UpperCAmelCase : Dict = (6, 12, 24, 48)
_UpperCAmelCase : Union[str, Any] = 12
_UpperCAmelCase : Optional[int] = 768
# set label information
_UpperCAmelCase : Union[str, Any] = 150
_UpperCAmelCase : List[Any] = "huggingface/label-files"
_UpperCAmelCase : Optional[int] = "ade20k-id2label.json"
_UpperCAmelCase : str = json.load(open(hf_hub_download(_UpperCamelCase, _UpperCamelCase, repo_type="dataset" ), "r" ) )
_UpperCAmelCase : str = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : List[str] = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : int = SwinConfig(
embed_dim=_UpperCamelCase, depths=_UpperCamelCase, num_heads=_UpperCamelCase, window_size=_UpperCamelCase, out_features=["stage1", "stage2", "stage3", "stage4"], )
_UpperCAmelCase : List[Any] = UperNetConfig(
backbone_config=_UpperCamelCase, auxiliary_in_channels=_UpperCamelCase, num_labels=_UpperCamelCase, idalabel=_UpperCamelCase, labelaid=_UpperCamelCase, )
return config
def __UpperCAmelCase ( a_: List[Any] ):
_UpperCAmelCase : int = []
# fmt: off
# stem
rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
] )
# fmt: on
return rename_keys
def __UpperCAmelCase ( a_: int, a_: List[str], a_: Optional[Any] ):
_UpperCAmelCase : List[str] = dct.pop(_UpperCamelCase )
_UpperCAmelCase : int = val
def __UpperCAmelCase ( a_: Optional[int], a_: Dict ):
_UpperCAmelCase : Optional[Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Union[str, Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : List[Any] = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
_UpperCAmelCase : int = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[int] = in_proj_weight[:dim, :]
_UpperCAmelCase : Dict = in_proj_bias[: dim]
_UpperCAmelCase : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : int = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Any = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : int = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = x.shape
_UpperCAmelCase : Union[str, Any] = x.reshape(_UpperCamelCase, 4, in_channel // 4 )
_UpperCAmelCase : int = x[:, [0, 2, 1, 3], :].transpose(1, 2 ).reshape(_UpperCamelCase, _UpperCamelCase )
return x
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = x.shape
_UpperCAmelCase : Any = x.reshape(_UpperCamelCase, in_channel // 4, 4 )
_UpperCAmelCase : List[Any] = x[:, :, [0, 2, 1, 3]].transpose(1, 2 ).reshape(_UpperCamelCase, _UpperCamelCase )
return x
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = x.shape[0]
_UpperCAmelCase : Any = x.reshape(4, in_channel // 4 )
_UpperCAmelCase : Optional[int] = x[[0, 2, 1, 3], :].transpose(0, 1 ).reshape(_UpperCamelCase )
return x
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : List[str] = x.shape[0]
_UpperCAmelCase : Union[str, Any] = x.reshape(in_channel // 4, 4 )
_UpperCAmelCase : str = x[:, [0, 2, 1, 3]].transpose(0, 1 ).reshape(_UpperCamelCase )
return x
def __UpperCAmelCase ( a_: List[Any], a_: Dict, a_: str ):
_UpperCAmelCase : Union[str, Any] = {
"upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
"upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
"upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
"upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
}
_UpperCAmelCase : Tuple = model_name_to_url[model_name]
_UpperCAmelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(_UpperCamelCase, map_location="cpu", file_name=_UpperCamelCase )[
"state_dict"
]
for name, param in state_dict.items():
print(_UpperCamelCase, param.shape )
_UpperCAmelCase : Any = get_upernet_config(_UpperCamelCase )
_UpperCAmelCase : Tuple = UperNetForSemanticSegmentation(_UpperCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_UpperCAmelCase : Any = state_dict.pop(_UpperCamelCase )
if "bn" in key:
_UpperCAmelCase : int = key.replace("bn", "batch_norm" )
_UpperCAmelCase : List[Any] = val
# rename keys
_UpperCAmelCase : int = create_rename_keys(_UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
read_in_q_k_v(_UpperCamelCase, config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
_UpperCAmelCase : List[str] = reverse_correct_unfold_reduction_order(_UpperCamelCase )
if "norm" in key:
_UpperCAmelCase : Optional[Any] = reverse_correct_unfold_norm_order(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
# verify on image
_UpperCAmelCase : List[str] = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
_UpperCAmelCase : Dict = Image.open(requests.get(_UpperCamelCase, stream=_UpperCamelCase ).raw ).convert("RGB" )
_UpperCAmelCase : Optional[Any] = SegformerImageProcessor()
_UpperCAmelCase : str = processor(_UpperCamelCase, return_tensors="pt" ).pixel_values
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(_UpperCamelCase )
_UpperCAmelCase : Union[str, Any] = outputs.logits
print(logits.shape )
print("First values of logits:", logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
_UpperCAmelCase : str = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] )
elif model_name == "upernet-swin-small":
_UpperCAmelCase : Union[str, Any] = torch.tensor(
[[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]] )
elif model_name == "upernet-swin-base":
_UpperCAmelCase : str = torch.tensor(
[[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]] )
elif model_name == "upernet-swin-large":
_UpperCAmelCase : Any = torch.tensor(
[[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]] )
print("Logits:", outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3], _UpperCamelCase, atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCamelCase )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(_UpperCamelCase )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-swin-tiny',
type=str,
choices=[f'upernet-swin-{size}' for size in ['tiny', 'small', 'base', 'large']],
help='Name of the Swin + UperNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__a = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 145 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 6_55_36 , _snake_case = None , _snake_case = 2 , _snake_case = 2 , _snake_case = 0 , _snake_case = "fourier" , _snake_case = True , _snake_case = False , _snake_case = 0.0 , _snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , _snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , _snake_case = "UNetMidBlock1D" , _snake_case = None , _snake_case = (32, 32, 64) , _snake_case = None , _snake_case = 8 , _snake_case = 1 , _snake_case = False , ):
"""simple docstring"""
super().__init__()
lowerCAmelCase = sample_size
# time
if time_embedding_type == "fourier":
lowerCAmelCase = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=_snake_case , log=_snake_case , flip_sin_to_cos=_snake_case )
lowerCAmelCase = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
lowerCAmelCase = Timesteps(
block_out_channels[0] , flip_sin_to_cos=_snake_case , downscale_freq_shift=_snake_case )
lowerCAmelCase = block_out_channels[0]
if use_timestep_embedding:
lowerCAmelCase = block_out_channels[0] * 4
lowerCAmelCase = TimestepEmbedding(
in_channels=_snake_case , time_embed_dim=_snake_case , act_fn=_snake_case , out_dim=block_out_channels[0] , )
lowerCAmelCase = nn.ModuleList([] )
lowerCAmelCase = None
lowerCAmelCase = nn.ModuleList([] )
lowerCAmelCase = None
# down
lowerCAmelCase = in_channels
for i, down_block_type in enumerate(_snake_case ):
lowerCAmelCase = output_channel
lowerCAmelCase = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
lowerCAmelCase = i == len(_snake_case ) - 1
lowerCAmelCase = get_down_block(
_snake_case , num_layers=_snake_case , in_channels=_snake_case , out_channels=_snake_case , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(_snake_case )
# mid
lowerCAmelCase = get_mid_block(
_snake_case , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=_snake_case , add_downsample=_snake_case , )
# up
lowerCAmelCase = list(reversed(_snake_case ) )
lowerCAmelCase = reversed_block_out_channels[0]
if out_block_type is None:
lowerCAmelCase = out_channels
else:
lowerCAmelCase = block_out_channels[0]
for i, up_block_type in enumerate(_snake_case ):
lowerCAmelCase = output_channel
lowerCAmelCase = (
reversed_block_out_channels[i + 1] if i < len(_snake_case ) - 1 else final_upsample_channels
)
lowerCAmelCase = i == len(_snake_case ) - 1
lowerCAmelCase = get_up_block(
_snake_case , num_layers=_snake_case , in_channels=_snake_case , out_channels=_snake_case , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(_snake_case )
lowerCAmelCase = output_channel
# out
lowerCAmelCase = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
lowerCAmelCase = get_out_block(
out_block_type=_snake_case , num_groups_out=_snake_case , embed_dim=block_out_channels[0] , out_channels=_snake_case , act_fn=_snake_case , fc_dim=block_out_channels[-1] // 4 , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case = True , ):
"""simple docstring"""
lowerCAmelCase = timestep
if not torch.is_tensor(_snake_case ):
lowerCAmelCase = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(_snake_case ) and len(timesteps.shape ) == 0:
lowerCAmelCase = timesteps[None].to(sample.device )
lowerCAmelCase = self.time_proj(_snake_case )
if self.config.use_timestep_embedding:
lowerCAmelCase = self.time_mlp(_snake_case )
else:
lowerCAmelCase = timestep_embed[..., None]
lowerCAmelCase = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
lowerCAmelCase = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
lowerCAmelCase = ()
for downsample_block in self.down_blocks:
lowerCAmelCase ,lowerCAmelCase = downsample_block(hidden_states=_snake_case , temb=_snake_case )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
lowerCAmelCase = self.mid_block(_snake_case , _snake_case )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
lowerCAmelCase = down_block_res_samples[-1:]
lowerCAmelCase = down_block_res_samples[:-1]
lowerCAmelCase = upsample_block(_snake_case , res_hidden_states_tuple=_snake_case , temb=_snake_case )
# 5. post-process
if self.out_block:
lowerCAmelCase = self.out_block(_snake_case , _snake_case )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=_snake_case )
| 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_head.weight'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : str ):
lowerCAmelCase = torch.load(_UpperCAmelCase )
lowerCAmelCase = d.pop(_UpperCAmelCase )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
torch.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
__UpperCamelCase : Optional[int] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
__UpperCamelCase : Dict = os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
__UpperCamelCase : str = f'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 309 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _a ( a :str = "laptop" ) -> DataFrame:
a = F"""https://www.amazon.in/laptop/s?k={product}"""
a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
a = BeautifulSoup(requests.get(a , headers=a ).text )
# Initialize a Pandas dataframe with the column titles
a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''} ) , ):
try:
a = item.ha.text
a = '''https://www.amazon.in/''' + item.ha.a['''href''']
a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''} ).text
try:
a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''} ).text
except AttributeError:
a = '''Not available'''
try:
a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''} ).text.split('''₹''' )[1]
)
except AttributeError:
a = ''''''
try:
a = float(
(
(
float(product_mrp.strip('''₹''' ).replace(''',''' , '''''' ) )
- float(product_price.strip('''₹''' ).replace(''',''' , '''''' ) )
)
/ float(product_mrp.strip('''₹''' ).replace(''',''' , '''''' ) )
)
* 100 )
except ValueError:
a = float('''nan''' )
except AttributeError:
pass
a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
a = ''' '''
a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCAmelCase__ = "headphones"
get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")
| 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = ProphetNetTokenizer
__snake_case = False
def __lowerCAmelCase ( self : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
super().setUp()
a = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str ) ->Dict:
"""simple docstring"""
a = '''UNwant\u00E9d,running'''
a = '''unwanted, running'''
return input_text, output_text
def __lowerCAmelCase ( self : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
a = self.tokenizer_class(self.vocab_file )
a = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__UpperCAmelCase , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [9, 6, 7, 12, 10, 11] )
def __lowerCAmelCase ( self : int ) ->Any:
"""simple docstring"""
a = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def __lowerCAmelCase ( self : Any ) ->int:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase , strip_accents=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] )
def __lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase , strip_accents=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : Tuple ) ->Tuple:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase , strip_accents=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : int ) ->Optional[int]:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase , strip_accents=__UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : Any ) ->int:
"""simple docstring"""
a = BasicTokenizer(do_lower_case=__UpperCAmelCase , never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def __lowerCAmelCase ( self : Union[str, Any] ) ->int:
"""simple docstring"""
a = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
a = {}
for i, token in enumerate(__UpperCAmelCase ):
a = i
a = WordpieceTokenizer(vocab=__UpperCAmelCase , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] )
@require_torch
def __lowerCAmelCase ( self : int ) ->int:
"""simple docstring"""
a = self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )
a = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
a = [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
a = tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors='''pt''' )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
a = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
self.assertTrue(_is_whitespace(''' ''' ) )
self.assertTrue(_is_whitespace('''\t''' ) )
self.assertTrue(_is_whitespace('''\r''' ) )
self.assertTrue(_is_whitespace('''\n''' ) )
self.assertTrue(_is_whitespace('''\u00A0''' ) )
self.assertFalse(_is_whitespace('''A''' ) )
self.assertFalse(_is_whitespace('''-''' ) )
def __lowerCAmelCase ( self : Any ) ->List[str]:
"""simple docstring"""
self.assertTrue(_is_control('''\u0005''' ) )
self.assertFalse(_is_control('''A''' ) )
self.assertFalse(_is_control(''' ''' ) )
self.assertFalse(_is_control('''\t''' ) )
self.assertFalse(_is_control('''\r''' ) )
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
self.assertTrue(_is_punctuation('''-''' ) )
self.assertTrue(_is_punctuation('''$''' ) )
self.assertTrue(_is_punctuation('''`''' ) )
self.assertTrue(_is_punctuation('''.''' ) )
self.assertFalse(_is_punctuation('''A''' ) )
self.assertFalse(_is_punctuation(''' ''' ) )
@slow
def __lowerCAmelCase ( self : List[str] ) ->List[str]:
"""simple docstring"""
a = self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )
a = tokenizer.encode('''sequence builders''' , add_special_tokens=__UpperCAmelCase )
a = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__UpperCAmelCase )
a = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
a = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase , __UpperCAmelCase )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A_( A : Namespace):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name)
lowerCAmelCase : Optional[Any] = "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"
class SCREAMING_SNAKE_CASE__ ( a_):
@staticmethod
def UpperCAmelCase_ ( A_ )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase = parser.add_parser(
'convert' , help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.' , )
train_parser.add_argument('--model_type' , type=A_ , required=A_ , help='Model\'s type.' )
train_parser.add_argument(
'--tf_checkpoint' , type=A_ , required=A_ , help='TensorFlow checkpoint path or folder.' )
train_parser.add_argument(
'--pytorch_dump_output' , type=A_ , required=A_ , help='Path to the PyTorch saved model output.' )
train_parser.add_argument('--config' , type=A_ , default='' , help='Configuration file path or folder.' )
train_parser.add_argument(
'--finetuning_task_name' , type=A_ , default=A_ , help='Optional fine-tuning task name if the TF model was a finetuned model.' , )
train_parser.set_defaults(func=A_ )
def __init__( self , A_ , A_ , A_ , A_ , A_ , *A_ , )-> List[Any]:
'''simple docstring'''
UpperCamelCase = logging.get_logger('transformers-cli/converting' )
self._logger.info(F'''Loading model {model_type}''' )
UpperCamelCase = model_type
UpperCamelCase = tf_checkpoint
UpperCamelCase = pytorch_dump_output
UpperCamelCase = config
UpperCamelCase = finetuning_task_name
def UpperCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(A_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
if "ckpt" in self._tf_checkpoint.lower():
UpperCamelCase = self._tf_checkpoint
UpperCamelCase = ''
else:
UpperCamelCase = self._tf_checkpoint
UpperCamelCase = ''
convert_transfo_xl_checkpoint_to_pytorch(
A_ , self._config , self._pytorch_dump_output , A_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(A_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )
| 350 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : int = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = """timesformer"""
def __init__( self , A_=224 , A_=16 , A_=3 , A_=8 , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.0 , A_=0.0 , A_=0.02 , A_=1e-6 , A_=True , A_="divided_space_time" , A_=0 , **A_ , )-> Union[str, Any]:
'''simple docstring'''
super().__init__(**A_ )
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = num_frames
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = qkv_bias
UpperCamelCase = attention_type
UpperCamelCase = drop_path_rate
| 251 | 0 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
a : Union[str, Any] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class a ( nn.Module ):
def __init__( self : str , lowercase_ : List[str] ):
super().__init__()
snake_case_ = torchvision.models.resnetaaa(pretrained=lowercase_ )
snake_case_ = list(model.children() )[:-2]
snake_case_ = nn.Sequential(*lowercase_ )
snake_case_ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] ):
snake_case_ = self.pool(self.model(lowercase_ ) )
snake_case_ = torch.flatten(lowercase_ , start_dim=2 )
snake_case_ = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class a ( lowerCamelCase__ ):
def __init__( self : Any , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = [json.loads(lowercase_ ) for l in open(lowercase_ )]
snake_case_ = os.path.dirname(lowercase_ )
snake_case_ = tokenizer
snake_case_ = labels
snake_case_ = len(lowercase_ )
snake_case_ = max_seq_length
snake_case_ = transforms
def __len__( self : List[str] ):
return len(self.data )
def __getitem__( self : Dict , lowercase_ : Any ):
snake_case_ = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowercase_ ) )
snake_case_ ,snake_case_ ,snake_case_ = sentence[0], sentence[1:-1], sentence[-1]
snake_case_ = sentence[: self.max_seq_length]
snake_case_ = torch.zeros(self.n_classes )
snake_case_ = 1
snake_case_ = Image.open(os.path.join(self.data_dir , self.data[index]['''img'''] ) ).convert('''RGB''' )
snake_case_ = self.transforms(lowercase_ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def A_ ( self : List[Any] ):
snake_case_ = Counter()
for row in self.data:
label_freqs.update(row['''label'''] )
return label_freqs
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = [len(row['''sentence'''] ) for row in batch]
snake_case_ ,snake_case_ = len(_A ), max(_A )
snake_case_ = torch.zeros(_A, _A, dtype=torch.long )
snake_case_ = torch.zeros(_A, _A, dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_A, _A ) ):
snake_case_ = input_row['''sentence''']
snake_case_ = 1
snake_case_ = torch.stack([row['''image'''] for row in batch] )
snake_case_ = torch.stack([row['''label'''] for row in batch] )
snake_case_ = torch.stack([row['''image_start_token'''] for row in batch] )
snake_case_ = torch.stack([row['''image_end_token'''] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def __magic_name__ ( ) -> Dict:
'''simple docstring'''
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7], std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9], ),
] )
| 56 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188 | 0 |
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 snake_case__ ( _lowerCAmelCase ):
lowercase__ : str = ['''image_processor''', '''tokenizer''']
lowercase__ : int = '''BridgeTowerImageProcessor'''
lowercase__ : List[str] = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = True , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = True , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchEncoding:
__magic_name__ : List[str] = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
# add pixel_values + pixel_mask
__magic_name__ : List[Any] = self.image_processor(
lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_center_crop=lowerCAmelCase__ , **lowerCAmelCase__ )
encoding.update(lowerCAmelCase__ )
return encoding
def __magic_name__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> str:
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> str:
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def __magic_name__ ( self ) -> List[str]:
__magic_name__ : List[Any] = self.tokenizer.model_input_names
__magic_name__ : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 138 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
__magic_name__: Any = logging.get_logger(__name__)
__magic_name__: Dict = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : Any = '''blenderbot-small'''
lowercase__ : Optional[int] = ['''past_key_values''']
lowercase__ : Dict = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCAmelCase__=5_02_65 , lowerCAmelCase__=5_12 , lowerCAmelCase__=8 , lowerCAmelCase__=20_48 , lowerCAmelCase__=16 , lowerCAmelCase__=8 , lowerCAmelCase__=20_48 , lowerCAmelCase__=16 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__="gelu" , lowerCAmelCase__=5_12 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=1 , lowerCAmelCase__=False , lowerCAmelCase__=0 , lowerCAmelCase__=1 , lowerCAmelCase__=2 , lowerCAmelCase__=2 , **lowerCAmelCase__ , ) -> Tuple:
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Union[str, Any] = d_model
__magic_name__ : Optional[int] = encoder_ffn_dim
__magic_name__ : Union[str, Any] = encoder_layers
__magic_name__ : List[str] = encoder_attention_heads
__magic_name__ : List[Any] = decoder_ffn_dim
__magic_name__ : str = decoder_layers
__magic_name__ : List[str] = decoder_attention_heads
__magic_name__ : Union[str, Any] = dropout
__magic_name__ : Tuple = attention_dropout
__magic_name__ : List[Any] = activation_dropout
__magic_name__ : List[Any] = activation_function
__magic_name__ : Optional[int] = init_std
__magic_name__ : Dict = encoder_layerdrop
__magic_name__ : Union[str, Any] = decoder_layerdrop
__magic_name__ : Optional[int] = use_cache
__magic_name__ : List[Any] = encoder_layers
__magic_name__ : Dict = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , is_encoder_decoder=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , forced_eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
class snake_case__ ( _lowerCAmelCase ):
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
__magic_name__ : Optional[int] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
__magic_name__ : List[Any] = {0: """batch"""}
__magic_name__ : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
__magic_name__ : List[str] = {0: """batch""", 1: """decoder_sequence"""}
__magic_name__ : Any = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase__ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
__magic_name__ : Union[str, Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
__magic_name__ ,__magic_name__ : Dict = self.num_layers
for i in range(lowerCAmelCase__ ):
__magic_name__ : Dict = {0: """batch""", 2: """past_sequence + sequence"""}
__magic_name__ : int = {0: """batch""", 2: """past_sequence + sequence"""}
else:
__magic_name__ : Union[str, Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
__magic_name__ : Any = super().outputs
else:
__magic_name__ : int = super(lowerCAmelCase__ , self ).outputs
if self.use_past:
__magic_name__ ,__magic_name__ : str = self.num_layers
for i in range(lowerCAmelCase__ ):
__magic_name__ : Tuple = {0: """batch""", 2: """past_sequence + sequence"""}
__magic_name__ : Dict = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = -1 , lowerCAmelCase__ = -1 , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ) -> Mapping[str, Any]:
__magic_name__ : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Generate decoder inputs
__magic_name__ : Optional[int] = seq_length if not self.use_past else 1
__magic_name__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ : Optional[Any] = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
__magic_name__ : Dict = dict(**lowerCAmelCase__ , **lowerCAmelCase__ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__magic_name__ ,__magic_name__ : List[Any] = common_inputs["""input_ids"""].shape
__magic_name__ : Optional[Any] = common_inputs["""decoder_input_ids"""].shape[1]
__magic_name__ ,__magic_name__ : str = self.num_attention_heads
__magic_name__ : Optional[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__magic_name__ : Any = decoder_seq_length + 3
__magic_name__ : Dict = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__magic_name__ : List[str] = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase__ , lowerCAmelCase__ )] , dim=1 )
__magic_name__ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__magic_name__ ,__magic_name__ : List[str] = self.num_layers
__magic_name__ : Optional[Any] = min(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ : List[str] = max(lowerCAmelCase__ , lowerCAmelCase__ ) - min_num_layers
__magic_name__ : Tuple = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(lowerCAmelCase__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCAmelCase__ ),
torch.zeros(lowerCAmelCase__ ),
torch.zeros(lowerCAmelCase__ ),
torch.zeros(lowerCAmelCase__ ),
) )
# TODO: test this.
__magic_name__ : Union[str, Any] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(lowerCAmelCase__ , lowerCAmelCase__ ):
common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__ ), torch.zeros(lowerCAmelCase__ )) )
return common_inputs
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = -1 , lowerCAmelCase__ = -1 , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ) -> Mapping[str, Any]:
__magic_name__ : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__magic_name__ ,__magic_name__ : Tuple = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__magic_name__ : List[Any] = seqlen + 2
__magic_name__ ,__magic_name__ : Any = self.num_layers
__magic_name__ ,__magic_name__ : int = self.num_attention_heads
__magic_name__ : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__magic_name__ : Optional[int] = common_inputs["""attention_mask"""].dtype
__magic_name__ : Optional[Any] = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__ , lowerCAmelCase__ , dtype=lowerCAmelCase__ )] , dim=1 )
__magic_name__ : Tuple = [
(torch.zeros(lowerCAmelCase__ ), torch.zeros(lowerCAmelCase__ )) for _ in range(lowerCAmelCase__ )
]
return common_inputs
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = -1 , lowerCAmelCase__ = -1 , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
__magic_name__ : Tuple = 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
__magic_name__ : str = tokenizer.num_special_tokens_to_add(lowerCAmelCase__ )
__magic_name__ : List[str] = 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
__magic_name__ : List[Any] = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
__magic_name__ : List[str] = dict(tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ ) )
return common_inputs
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = -1 , lowerCAmelCase__ = -1 , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
__magic_name__ : Tuple = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ )
elif self.task == "causal-lm":
__magic_name__ : str = self._generate_dummy_inputs_for_causal_lm(
lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ )
else:
__magic_name__ : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ )
return common_inputs
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
if self.task in ["default", "seq2seq-lm"]:
__magic_name__ : List[Any] = super()._flatten_past_key_values_(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else:
__magic_name__ : Tuple = super(lowerCAmelCase__ , self )._flatten_past_key_values_(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
| 138 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 110 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_a = 2
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , *, # begin keyword-only arguments
UpperCAmelCase="<s>" , UpperCAmelCase="<pad>" , UpperCAmelCase="</s>" , UpperCAmelCase="<unk>" , UpperCAmelCase=None , ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = bos, unk, pad, eos
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {}
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = len(self.symbols )
def __eq__( self , UpperCAmelCase ):
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self , UpperCAmelCase ):
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self ):
"""simple docstring"""
return len(self.symbols )
def __contains__( self , UpperCAmelCase ):
"""simple docstring"""
return sym in self.indices
@classmethod
def UpperCamelCase ( cls , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = cls()
d.add_from_file(UpperCAmelCase )
return d
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=1 , UpperCAmelCase=False ):
"""simple docstring"""
if word in self.indices and not overwrite:
_UpperCAmelCase = self.indices[word]
_UpperCAmelCase = self.count[idx] + n
return idx
else:
_UpperCAmelCase = len(self.symbols )
_UpperCAmelCase = idx
self.symbols.append(UpperCAmelCase )
self.count.append(UpperCAmelCase )
return idx
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return 0
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
if isinstance(UpperCAmelCase , UpperCAmelCase ):
try:
with open(UpperCAmelCase , 'r' , encoding='utf-8' ) as fd:
self.add_from_file(UpperCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(UpperCAmelCase ) )
return
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = self._load_meta(UpperCAmelCase )
for line in lines[indices_start_line:]:
try:
_UpperCAmelCase , _UpperCAmelCase = line.rstrip().rsplit(' ' , 1 )
if field == "#fairseq:overwrite":
_UpperCAmelCase = True
_UpperCAmelCase , _UpperCAmelCase = line.rsplit(' ' , 1 )
else:
_UpperCAmelCase = False
_UpperCAmelCase = int(UpperCAmelCase )
_UpperCAmelCase = line
if word in self and not overwrite:
raise RuntimeError(
'Duplicate word found when loading Dictionary: \'{}\'. '
'Duplicate words can overwrite earlier ones by adding the '
'#fairseq:overwrite flag at the end of the corresponding row '
'in the dictionary file. If using the Camembert model, please '
'download an updated copy of the model file.'.format(UpperCAmelCase ) )
self.add_symbol(UpperCAmelCase , n=UpperCAmelCase , overwrite=UpperCAmelCase )
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'' )
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
_UpperCAmelCase = dict((re.sub(R'@@$' , '' , __lowerCAmelCase ), v) if k.endswith('@@' ) else (re.sub(R'$' , '</w>' , __lowerCAmelCase ), v) for k, v in d.items() )
_UpperCAmelCase = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
_UpperCAmelCase = d[k] # restore
return da
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> str:
"""simple docstring"""
if not os.path.exists(__lowerCAmelCase ):
raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""" )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'checkpoint.pt' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {checkpoint_file} does not exist!""" )
_UpperCAmelCase = torch.load(__lowerCAmelCase , map_location='cpu' )
_UpperCAmelCase = chkpt['cfg']['model']
# dicts
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'dict.txt' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {dict_file} does not exist!""" )
_UpperCAmelCase = Dictionary.load(__lowerCAmelCase )
_UpperCAmelCase = rewrite_dict_keys(src_dict.indices )
_UpperCAmelCase = len(__lowerCAmelCase )
_UpperCAmelCase = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES['vocab_file'] )
print(F"""Generating {src_vocab_file} of {src_vocab_size} records""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'bpecodes' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )
_UpperCAmelCase = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(__lowerCAmelCase , __lowerCAmelCase )
# model config
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'config.json' )
_UpperCAmelCase = {
'activation_dropout': args['activation_dropout'],
'architectures': ['BioGptForCausalLM'],
'attention_probs_dropout_prob': args['attention_dropout'],
'bos_token_id': 0,
'eos_token_id': 2,
'hidden_act': args['activation_fn'],
'hidden_dropout_prob': args['dropout'],
'hidden_size': args['decoder_embed_dim'],
'initializer_range': 0.02,
'intermediate_size': args['decoder_ffn_embed_dim'],
'layer_norm_eps': 1E-12,
'layerdrop': args['decoder_layerdrop'],
'max_position_embeddings': args['max_target_positions'],
'model_type': 'biogpt',
'num_attention_heads': args['decoder_attention_heads'],
'num_hidden_layers': args['decoder_layers'],
'pad_token_id': 1,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_decoder_input_output_embed'],
'vocab_size': src_vocab_size,
}
# good hparam defaults to start with
print(F"""Generating {biogpt_model_config_file}""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
_UpperCAmelCase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = {
'bos_token': '<s>',
'eos_token': '</s>',
'model_max_length': 1_024,
'pad_token': '<pad>',
'special_tokens_map_file': None,
'tokenizer_class': 'BioGptTokenizer',
'unk_token': '<unk>',
}
print(F"""Generating {biogpt_tokenizer_config_file}""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
_UpperCAmelCase = chkpt['model']
# remove unneeded keys
_UpperCAmelCase = [
'decoder.version',
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
_UpperCAmelCase = model_state_dict.pop(__lowerCAmelCase )
else:
_UpperCAmelCase = model_state_dict.pop(__lowerCAmelCase )
_UpperCAmelCase = BioGptConfig.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase = BioGptForCausalLM(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase )
# save
_UpperCAmelCase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print('Conversion is done!' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--biogpt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_a = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 39 | 0 |
"""simple docstring"""
def a__ ( __lowercase ) -> int:
assert column_title.isupper()
_A = 0
_A = len(__lowercase ) - 1
_A = 0
while index >= 0:
_A = (ord(column_title[index] ) - 64) * pow(26 , __lowercase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod() | 353 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class snake_case :
def __init__( self : Optional[int] , a__ : Tuple , a__ : str=1_00 , a__ : Dict=13 , a__ : Tuple=30 , a__ : str=2 , a__ : List[Any]=3 , a__ : Dict=True , a__ : Optional[Any]=True , a__ : List[Any]=32 , a__ : Tuple=4 , a__ : Tuple=4 , a__ : Optional[int]=37 , a__ : Tuple="gelu" , a__ : Optional[int]=0.1 , a__ : int=0.1 , a__ : Optional[Any]=10 , a__ : Optional[int]=0.0_2 , a__ : Dict=3 , a__ : str=None , a__ : Any=[0, 1, 2, 3] , ) -> Tuple:
'''simple docstring'''
_A = parent
_A = 1_00
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = is_training
_A = use_labels
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = type_sequence_label_size
_A = initializer_range
_A = scope
_A = out_indices
_A = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_A = (image_size // patch_size) ** 2
_A = num_patches + 1
def a_ ( self : List[str] ) -> str:
'''simple docstring'''
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_A = self.get_config()
return config, pixel_values, labels, pixel_labels
def a_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def a_ ( self : Any , a__ : List[str] , a__ : Tuple , a__ : List[str] , a__ : str ) -> Any:
'''simple docstring'''
_A = BeitModel(config=a__ )
model.to(a__ )
model.eval()
_A = model(a__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self : List[str] , a__ : Optional[Any] , a__ : Tuple , a__ : Any , a__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
_A = BeitForMaskedImageModeling(config=a__ )
model.to(a__ )
model.eval()
_A = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def a_ ( self : Optional[Any] , a__ : Optional[int] , a__ : Optional[Any] , a__ : List[str] , a__ : Dict ) -> Dict:
'''simple docstring'''
_A = self.type_sequence_label_size
_A = BeitForImageClassification(a__ )
model.to(a__ )
model.eval()
_A = model(a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_A = 1
_A = BeitForImageClassification(a__ )
model.to(a__ )
model.eval()
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self : Optional[Any] , a__ : Optional[Any] , a__ : Union[str, Any] , a__ : Union[str, Any] , a__ : Dict ) -> str:
'''simple docstring'''
_A = self.num_labels
_A = BeitForSemanticSegmentation(a__ )
model.to(a__ )
model.eval()
_A = model(a__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
_A = model(a__ , labels=a__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def a_ ( self : List[Any] ) -> Any:
'''simple docstring'''
_A = self.prepare_config_and_inputs()
_A , _A , _A , _A = config_and_inputs
_A = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase):
__UpperCamelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def a_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
_A = BeitModelTester(self )
_A = ConfigTester(self , config_class=a__ , has_text_modality=a__ , hidden_size=37 )
def a_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def a_ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def a_ ( self : Any ) -> int:
'''simple docstring'''
pass
def a_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(a__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a__ , nn.Linear ) )
def a_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(a__ )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a__ )
def a_ ( self : Dict ) -> Any:
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def a_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*a__ )
def a_ ( self : int ) -> List[Any]:
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a__ )
def a_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a__ )
def a_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(a__ ), BeitForMaskedImageModeling]:
continue
_A = model_class(a__ )
model.to(a__ )
model.train()
_A = self._prepare_for_class(a__ , a__ , return_labels=a__ )
_A = model(**a__ ).loss
loss.backward()
def a_ ( self : List[str] ) -> Dict:
'''simple docstring'''
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
_A = False
_A = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(a__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
_A = model_class(a__ )
model.gradient_checkpointing_enable()
model.to(a__ )
model.train()
_A = self._prepare_for_class(a__ , a__ , return_labels=a__ )
_A = model(**a__ ).loss
loss.backward()
def a_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = _config_zero_init(a__ )
for model_class in self.all_model_classes:
_A = model_class(config=a__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@slow
def a_ ( self : List[str] ) -> int:
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = BeitModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
def a__ ( ) -> Tuple:
_A = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase):
@cached_property
def a_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def a_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_A = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(a__ )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=a__ , return_tensors="pt" ).pixel_values.to(a__ )
# prepare bool_masked_pos
_A = torch.ones((1, 1_96) , dtype=torch.bool ).to(a__ )
# forward pass
with torch.no_grad():
_A = model(pixel_values=a__ , bool_masked_pos=a__ )
_A = outputs.logits
# verify the logits
_A = torch.Size((1, 1_96, 81_92) )
self.assertEqual(logits.shape , a__ )
_A = torch.tensor(
[[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]] ).to(a__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , a__ , atol=1E-2 ) )
@slow
def a_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_A = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(a__ )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=a__ , return_tensors="pt" ).to(a__ )
# forward pass
with torch.no_grad():
_A = model(**a__ )
_A = outputs.logits
# verify the logits
_A = torch.Size((1, 10_00) )
self.assertEqual(logits.shape , a__ )
_A = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] ).to(a__ )
self.assertTrue(torch.allclose(logits[0, :3] , a__ , atol=1E-4 ) )
_A = 2_81
self.assertEqual(logits.argmax(-1 ).item() , a__ )
@slow
def a_ ( self : List[Any] ) -> int:
'''simple docstring'''
_A = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
a__ )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=a__ , return_tensors="pt" ).to(a__ )
# forward pass
with torch.no_grad():
_A = model(**a__ )
_A = outputs.logits
# verify the logits
_A = torch.Size((1, 2_18_41) )
self.assertEqual(logits.shape , a__ )
_A = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] ).to(a__ )
self.assertTrue(torch.allclose(logits[0, :3] , a__ , atol=1E-4 ) )
_A = 23_96
self.assertEqual(logits.argmax(-1 ).item() , a__ )
@slow
def a_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
_A = model.to(a__ )
_A = BeitImageProcessor(do_resize=a__ , size=6_40 , do_center_crop=a__ )
_A = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
_A = Image.open(ds[0]["file"] )
_A = image_processor(images=a__ , return_tensors="pt" ).to(a__ )
# forward pass
with torch.no_grad():
_A = model(**a__ )
_A = outputs.logits
# verify the logits
_A = torch.Size((1, 1_50, 1_60, 1_60) )
self.assertEqual(logits.shape , a__ )
_A = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
_A = torch.tensor(
[
[[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
[[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
[[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
] , device=a__ , )
else:
_A = torch.tensor(
[
[[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
[[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
[[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
] , device=a__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , a__ , atol=1E-4 ) )
@slow
def a_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_A = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
_A = model.to(a__ )
_A = BeitImageProcessor(do_resize=a__ , size=6_40 , do_center_crop=a__ )
_A = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
_A = Image.open(ds[0]["file"] )
_A = image_processor(images=a__ , return_tensors="pt" ).to(a__ )
# forward pass
with torch.no_grad():
_A = model(**a__ )
_A = outputs.logits.detach().cpu()
_A = image_processor.post_process_semantic_segmentation(outputs=a__ , target_sizes=[(5_00, 3_00)] )
_A = torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape , a__ )
_A = image_processor.post_process_semantic_segmentation(outputs=a__ )
_A = torch.Size((1_60, 1_60) )
self.assertEqual(segmentation[0].shape , a__ ) | 163 | 0 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase__ ( __lowercase : int , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : Optional[int]=1024 ) -> Dict:
"""simple docstring"""
__UpperCamelCase , __UpperCamelCase = [], []
__UpperCamelCase = list(zip(__lowercase , __lowercase ) )
__UpperCamelCase , __UpperCamelCase = sorted_examples[0]
def is_too_big(__lowercase : Optional[Any] ):
return tok(__lowercase , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
__UpperCamelCase = new_src + ' ' + src
__UpperCamelCase = new_tgt + ' ' + tgt
if is_too_big(__lowercase ) or is_too_big(__lowercase ): # cant fit, finalize example
finished_src.append(__lowercase )
finished_tgt.append(__lowercase )
__UpperCamelCase , __UpperCamelCase = src, tgt
else: # can fit, keep adding
__UpperCamelCase , __UpperCamelCase = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__lowercase )
finished_tgt.append(__lowercase )
return finished_src, finished_tgt
def lowercase__ ( __lowercase : List[str] , __lowercase : Path , __lowercase : Dict , __lowercase : Dict ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase = Path(__lowercase )
save_path.mkdir(exist_ok=__lowercase )
for split in ["train"]:
__UpperCamelCase , __UpperCamelCase = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
__UpperCamelCase = [x.rstrip() for x in Path(__lowercase ).open().readlines()]
__UpperCamelCase = [x.rstrip() for x in Path(__lowercase ).open().readlines()]
__UpperCamelCase , __UpperCamelCase = pack_examples(__lowercase , __lowercase , __lowercase , __lowercase )
print(F'''packed {split} split from {len(__lowercase )} examples -> {len(__lowercase )}.''' )
Path(save_path / F'''{split}.source''' ).open('w' ).write('\n'.join(__lowercase ) )
Path(save_path / F'''{split}.target''' ).open('w' ).write('\n'.join(__lowercase ) )
for split in ["val", "test"]:
__UpperCamelCase , __UpperCamelCase = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(__lowercase , save_path / F'''{split}.source''' )
shutil.copyfile(__lowercase , save_path / F'''{split}.target''' )
def lowercase__ ( ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('--tok_name' , type=__lowercase , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' , type=__lowercase , default=128 )
parser.add_argument('--data_dir' , type=__lowercase )
parser.add_argument('--save_path' , type=__lowercase )
__UpperCamelCase = parser.parse_args()
__UpperCamelCase = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__lowercase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 53 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 241 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] ):
snake_case__ : Dict = SwinConfig()
snake_case__ : List[str] = swin_name.split("_" )
snake_case__ : List[Any] = name_split[1]
snake_case__ : Optional[int] = int(name_split[4] )
snake_case__ : Any = int(name_split[3][-1] )
if model_size == "tiny":
snake_case__ : Tuple = 96
snake_case__ : str = (2, 2, 6, 2)
snake_case__ : Tuple = (3, 6, 12, 24)
elif model_size == "small":
snake_case__ : Any = 96
snake_case__ : Dict = (2, 2, 18, 2)
snake_case__ : Optional[Any] = (3, 6, 12, 24)
elif model_size == "base":
snake_case__ : Tuple = 128
snake_case__ : List[str] = (2, 2, 18, 2)
snake_case__ : Optional[Any] = (4, 8, 16, 32)
else:
snake_case__ : Optional[int] = 192
snake_case__ : Optional[int] = (2, 2, 18, 2)
snake_case__ : Dict = (6, 12, 24, 48)
if "in22k" in swin_name:
snake_case__ : List[str] = 21841
else:
snake_case__ : Dict = 1000
snake_case__ : Optional[Any] = '''huggingface/label-files'''
snake_case__ : Any = '''imagenet-1k-id2label.json'''
snake_case__ : Optional[Any] = json.load(open(hf_hub_download(A_ , A_ , repo_type="dataset" ) , "r" ) )
snake_case__ : Any = {int(A_ ): v for k, v in idalabel.items()}
snake_case__ : str = idalabel
snake_case__ : List[str] = {v: k for k, v in idalabel.items()}
snake_case__ : Dict = img_size
snake_case__ : str = num_classes
snake_case__ : Union[str, Any] = embed_dim
snake_case__ : Any = depths
snake_case__ : Optional[Any] = num_heads
snake_case__ : int = window_size
return config
def SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] ):
if "patch_embed.proj" in name:
snake_case__ : str = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
snake_case__ : str = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
snake_case__ : Any = '''encoder.''' + name
if "attn.proj" in name:
snake_case__ : List[str] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
snake_case__ : str = name.replace("attn" , "attention.self" )
if "norm1" in name:
snake_case__ : Tuple = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
snake_case__ : Tuple = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
snake_case__ : int = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
snake_case__ : Dict = name.replace("mlp.fc2" , "output.dense" )
if name == "norm.weight":
snake_case__ : Optional[Any] = '''layernorm.weight'''
if name == "norm.bias":
snake_case__ : str = '''layernorm.bias'''
if "head" in name:
snake_case__ : List[str] = name.replace("head" , "classifier" )
else:
snake_case__ : List[Any] = '''swin.''' + name
return name
def SCREAMING_SNAKE_CASE ( snake_case_ : List[Any] , snake_case_ : int ):
for key in orig_state_dict.copy().keys():
snake_case__ : Any = orig_state_dict.pop(A_ )
if "mask" in key:
continue
elif "qkv" in key:
snake_case__ : List[Any] = key.split("." )
snake_case__ : List[str] = int(key_split[1] )
snake_case__ : Optional[Any] = int(key_split[3] )
snake_case__ : Union[str, Any] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case__ : Union[str, Any] = val[:dim, :]
snake_case__ : Optional[Any] = val[
dim : dim * 2, :
]
snake_case__ : Any = val[-dim:, :]
else:
snake_case__ : int = val[
:dim
]
snake_case__ : Any = val[
dim : dim * 2
]
snake_case__ : Union[str, Any] = val[
-dim:
]
else:
snake_case__ : Dict = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( snake_case_ : Tuple , snake_case_ : List[str] ):
snake_case__ : int = timm.create_model(A_ , pretrained=A_ )
timm_model.eval()
snake_case__ : Tuple = get_swin_config(A_ )
snake_case__ : str = SwinForImageClassification(A_ )
model.eval()
snake_case__ : List[Any] = convert_state_dict(timm_model.state_dict() , A_ )
model.load_state_dict(A_ )
snake_case__ : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case__ : Any = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_" , "-" ) ) )
snake_case__ : str = Image.open(requests.get(A_ , stream=A_ ).raw )
snake_case__ : Any = image_processor(images=A_ , return_tensors="pt" )
snake_case__ : str = timm_model(inputs["pixel_values"] )
snake_case__ : Any = model(**A_ ).logits
assert torch.allclose(A_ , A_ , atol=1E-3 )
print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A_ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(A_ )
if __name__ == "__main__":
__lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin timm model you\'d like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
__lowerCamelCase : List[Any] = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 358 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Any = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerConfig""",
"""InstructBlipVisionConfig""",
],
"""processing_instructblip""": ["""InstructBlipProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[Any] = [
"""INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InstructBlipQFormerModel""",
"""InstructBlipPreTrainedModel""",
"""InstructBlipForConditionalGeneration""",
"""InstructBlipVisionModel""",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
__lowerCamelCase : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 286 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def __a ( UpperCAmelCase , UpperCAmelCase=False ) ->List[str]:
"""simple docstring"""
try:
A = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
A = default
else:
# KEY is set, convert it to True or False.
try:
A = strtobool(UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
_lowerCamelCase : Optional[int] = parse_flag_from_env('RUN_SLOW', default=False)
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
return unittest.skip("""Test was skipped""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , """test is slow""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , """test requires only a CPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , """test requires a GPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , """test requires a XPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , """test requires a `mps` backend support in `torch`""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , """test requires the Hugging Face suite""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , """test requires the bitsandbytes library""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , """test requires TPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , """test requires a GPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , """test requires a XPU""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , """test requires multiple GPUs""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , """test requires multiple XPUs""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , """test requires safetensors""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Any:
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , """test requires DeepSpeed""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_torch_version(""">=""" , """1.12.0""" ) , """test requires torch version >= 1.12.0""" )(UpperCAmelCase )
def __a ( UpperCAmelCase=None , UpperCAmelCase=None ) ->Optional[int]:
"""simple docstring"""
if test_case is None:
return partial(UpperCAmelCase , version=UpperCAmelCase )
return unittest.skipUnless(is_torch_version(""">=""" , UpperCAmelCase ) , f"""test requires torch version >= {version}""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , """test requires Tensorboard""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , """test requires wandb""" )(UpperCAmelCase )
def __a ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , """test requires comet_ml""" )(UpperCAmelCase )
_lowerCamelCase : List[Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , """test requires at least one tracker to be available and for `comet_ml` to not be installed""" , )(UpperCAmelCase )
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = True
@classmethod
def A (cls : Any ):
A = tempfile.mkdtemp()
@classmethod
def A (cls : Dict ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def A (self : Union[str, Any] ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(_lowerCAmelCase )
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : List[Any] ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : str , _lowerCAmelCase : Union[mock.Mock, List[mock.Mock]] ):
A = mocks if isinstance(_lowerCAmelCase , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
A = AcceleratorState()
A = tensor[None].clone().to(state.device )
A = gather(UpperCAmelCase ).cpu()
A = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , UpperCAmelCase ):
return False
return True
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ):
A = returncode
A = stdout
A = stderr
async def __a ( UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
while True:
A = await stream.readline()
if line:
callback(UpperCAmelCase )
else:
break
async def __a ( UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=False ) ->_RunOutput:
"""simple docstring"""
if echo:
print("""\nRunning: """ , """ """.join(UpperCAmelCase ) )
A = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
A = []
A = []
def tee(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase="" ):
A = line.decode("""utf-8""" ).rstrip()
sink.append(UpperCAmelCase )
if not quiet:
print(UpperCAmelCase , UpperCAmelCase , file=UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda UpperCAmelCase : tee(UpperCAmelCase , UpperCAmelCase , sys.stdout , label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda UpperCAmelCase : tee(UpperCAmelCase , UpperCAmelCase , sys.stderr , label="""stderr:""" ) ) ),
] , timeout=UpperCAmelCase , )
return _RunOutput(await p.wait() , UpperCAmelCase , UpperCAmelCase )
def __a ( UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=180 , UpperCAmelCase=False , UpperCAmelCase=True ) ->_RunOutput:
"""simple docstring"""
A = asyncio.get_event_loop()
A = loop.run_until_complete(
_stream_subprocess(UpperCAmelCase , env=UpperCAmelCase , stdin=UpperCAmelCase , timeout=UpperCAmelCase , quiet=UpperCAmelCase , echo=UpperCAmelCase ) )
A = """ """.join(UpperCAmelCase )
if result.returncode > 0:
A = """\n""".join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
return result
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
pass
def __a ( UpperCAmelCase , UpperCAmelCase=False ) ->Dict:
"""simple docstring"""
try:
A = subprocess.check_output(UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(UpperCAmelCase , """decode""" ):
A = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"""Command `{" ".join(UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
_lowerCamelCase : str = os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
_lowerCamelCase : Tuple = os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : Any ):
A = get_test_to_tester_mapping(_lowerCAmelCase )
A = get_test_to_tester_mapping(_lowerCAmelCase )
A = {"""BertModelTest""": """BertModelTester"""}
A = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
def A (self : str ):
A = get_model_to_test_mapping(_lowerCAmelCase )
A = get_model_to_test_mapping(_lowerCAmelCase )
A = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
A = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
def A (self : Union[str, Any] ):
A = get_model_to_tester_mapping(_lowerCAmelCase )
A = get_model_to_tester_mapping(_lowerCAmelCase )
A = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
A = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
self.assertEqual(get_test_info.to_json(_lowerCAmelCase ) , _lowerCAmelCase )
| 258 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __A:
def __init__( self , _snake_case , _snake_case=13 , _snake_case=10 , _snake_case=3 , _snake_case=2 , _snake_case=2 , _snake_case=2 , _snake_case=True , _snake_case=True , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=10 , _snake_case=0.02 , _snake_case=0.9 , _snake_case=None , ) -> Tuple:
'''simple docstring'''
__a = parent
__a = batch_size
__a = image_size
__a = num_channels
__a = patch_size
__a = tubelet_size
__a = num_frames
__a = is_training
__a = use_labels
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = type_sequence_label_size
__a = initializer_range
__a = mask_ratio
__a = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
__a = (image_size // patch_size) ** 2
__a = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
__a = int(mask_ratio * self.seq_length )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_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 , is_decoder=_snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = VideoMAEModel(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = VideoMAEForPreTraining(_snake_case )
model.to(_snake_case )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__a = torch.ones((self.num_masks,) )
__a = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
__a = mask.expand(self.batch_size , -1 ).bool()
__a = model(_snake_case , _snake_case )
# model only returns predictions for masked patches
__a = mask.sum().item()
__a = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A( a , a , unittest.TestCase ):
snake_case_ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
snake_case_ = (
{'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = VideoMAEModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case=False ) -> Tuple:
'''simple docstring'''
__a = copy.deepcopy(_snake_case )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__a = torch.ones((self.model_tester.num_masks,) )
__a = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
__a = mask.expand(self.model_tester.batch_size , -1 ).bool()
__a = bool_masked_pos.to(_snake_case )
if return_labels:
if model_class in [
*get_values(_snake_case ),
]:
__a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''VideoMAE does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(_snake_case )
__a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_snake_case )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = VideoMAEModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
if not self.has_attentions:
pass
else:
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = True
for model_class in self.all_model_classes:
__a = self.model_tester.seq_length - self.model_tester.num_masks
__a = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
__a = True
__a = False
__a = True
__a = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(_snake_case , _snake_case ) )
__a = outputs.attentions
self.assertEqual(len(_snake_case ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a = True
__a = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(_snake_case , _snake_case ) )
__a = outputs.attentions
self.assertEqual(len(_snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
__a = len(_snake_case )
# Check attention is always last and order is fine
__a = True
__a = True
__a = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(_snake_case , _snake_case ) )
self.assertEqual(out_len + 1 , len(_snake_case ) )
__a = outputs.attentions
self.assertEqual(len(_snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
def check_hidden_states_output(_snake_case , _snake_case , _snake_case ):
__a = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(_snake_case , _snake_case ) )
__a = outputs.hidden_states
__a = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(_snake_case ) , _snake_case )
__a = self.model_tester.seq_length - self.model_tester.num_masks
__a = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = 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"]
__a = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def __lowerCAmelCase ( ):
__a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
__a = np.load(a__ )
return list(a__ )
@require_torch
@require_vision
class __A( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''' ).to(
_snake_case )
__a = self.default_image_processor
__a = prepare_video()
__a = image_processor(_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
# verify the logits
__a = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , _snake_case )
__a = torch.tensor([0.3669, -0.0688, -0.2421] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ).to(_snake_case )
__a = self.default_image_processor
__a = prepare_video()
__a = image_processor(_snake_case , return_tensors='''pt''' ).to(_snake_case )
# add boolean mask, indicating which patches to mask
__a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
__a = torch.load(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
# verify the logits
__a = torch.Size([1, 1_408, 1_536] )
__a = torch.tensor(
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] , device=_snake_case )
self.assertEqual(outputs.logits.shape , _snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _snake_case , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
__a = torch.tensor([0.5142] , device=_snake_case )
self.assertTrue(torch.allclose(outputs.loss , _snake_case , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
__a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' , norm_pix_loss=_snake_case ).to(
_snake_case )
with torch.no_grad():
__a = model(**_snake_case )
__a = torch.tensor(torch.tensor([0.6469] ) , device=_snake_case )
self.assertTrue(torch.allclose(outputs.loss , _snake_case , atol=1E-4 ) ) | 351 |
A : Optional[Any] = tuple[float, float, float]
A : Union[str, Any] = tuple[float, float, float]
def __lowerCAmelCase ( a__ , a__ ) -> Vectorad:
__a = end_pointa[0] - end_pointa[0]
__a = end_pointa[1] - end_pointa[1]
__a = end_pointa[2] - end_pointa[2]
return (x, y, z)
def __lowerCAmelCase ( a__ , a__ ) -> Vectorad:
__a = ab[1] * ac[2] - ab[2] * ac[1] # *i
__a = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
__a = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def __lowerCAmelCase ( a__ , a__ ) -> bool:
return tuple(round(a__ , a__ ) for x in vector ) == (0, 0, 0)
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 10 ) -> bool:
__a = create_vector(a__ , a__ )
__a = create_vector(a__ , a__ )
return is_zero_vector(get_ad_vectors_cross(a__ , a__ ) , a__ ) | 33 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A ( __snake_case ):
__magic_name__ = DistilBertTokenizer
__magic_name__ = DistilBertTokenizerFast
__magic_name__ = True
@slow
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : List[Any] = DistilBertTokenizer.from_pretrained('''distilbert-base-uncased''' )
A : Dict = tokenizer.encode('''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : List[str] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : Optional[Any] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE )
A : Tuple = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 3 |
import unittest
from transformers import DonutProcessor
lowerCamelCase = '''naver-clova-ix/donut-base'''
class _a ( unittest.TestCase):
def UpperCAmelCase__( self : str )-> int:
lowerCAmelCase__ : Any = DonutProcessor.from_pretrained(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : Optional[int] )-> List[Any]:
lowerCAmelCase__ : Dict = {
'''name''': '''John Doe''',
'''age''': '''99''',
'''city''': '''Atlanta''',
'''state''': '''GA''',
'''zip''': '''30301''',
'''phone''': '''123-4567''',
'''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
}
lowerCAmelCase__ : Any = (
'''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
'''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
'''<s_nicknames><s_nickname>Johnny</s_nickname>'''
'''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
)
lowerCAmelCase__ : str = self.processor.tokenajson(_SCREAMING_SNAKE_CASE )
self.assertDictEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 131 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
_lowerCAmelCase = len(snake_case_ )
for i in range(1 , snake_case_ ):
_lowerCAmelCase = collection[i]
_lowerCAmelCase = 0
_lowerCAmelCase = i - 1
while low <= high:
_lowerCAmelCase = (low + high) // 2
if val < collection[mid]:
_lowerCAmelCase = mid - 1
else:
_lowerCAmelCase = mid + 1
for j in range(snake_case_ , snake_case_ , -1 ):
_lowerCAmelCase = collection[j - 1]
_lowerCAmelCase = val
return collection
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : Tuple = [int(item) for item in user_input.split(''',''')]
print(binary_insertion_sort(unsorted)) | 317 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" ) | 317 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class a_ (_a ):
__lowerCAmelCase : torch.FloatTensor
class a_ (_a , _a ):
@register_to_config
def __init__( self , snake_case_ = 6_5_5_3_6 , snake_case_ = None , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 0 , snake_case_ = "fourier" , snake_case_ = True , snake_case_ = False , snake_case_ = 0.0 , snake_case_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , snake_case_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , snake_case_ = "UNetMidBlock1D" , snake_case_ = None , snake_case_ = (3_2, 3_2, 6_4) , snake_case_ = None , snake_case_ = 8 , snake_case_ = 1 , snake_case_ = False , ):
super().__init__()
_lowerCAmelCase : Tuple = sample_size
# time
if time_embedding_type == "fourier":
_lowerCAmelCase : Dict = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=snake_case_ , log=snake_case_ , flip_sin_to_cos=snake_case_ )
_lowerCAmelCase : str = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
_lowerCAmelCase : int = Timesteps(
block_out_channels[0] , flip_sin_to_cos=snake_case_ , downscale_freq_shift=snake_case_ )
_lowerCAmelCase : int = block_out_channels[0]
if use_timestep_embedding:
_lowerCAmelCase : Optional[int] = block_out_channels[0] * 4
_lowerCAmelCase : Optional[Any] = TimestepEmbedding(
in_channels=snake_case_ , time_embed_dim=snake_case_ , act_fn=snake_case_ , out_dim=block_out_channels[0] , )
_lowerCAmelCase : Optional[int] = nn.ModuleList([] )
_lowerCAmelCase : Dict = None
_lowerCAmelCase : int = nn.ModuleList([] )
_lowerCAmelCase : int = None
# down
_lowerCAmelCase : Any = in_channels
for i, down_block_type in enumerate(snake_case_ ):
_lowerCAmelCase : List[Any] = output_channel
_lowerCAmelCase : Any = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
_lowerCAmelCase : str = i == len(snake_case_ ) - 1
_lowerCAmelCase : Tuple = get_down_block(
snake_case_ , num_layers=snake_case_ , in_channels=snake_case_ , out_channels=snake_case_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(snake_case_ )
# mid
_lowerCAmelCase : Any = get_mid_block(
snake_case_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=snake_case_ , add_downsample=snake_case_ , )
# up
_lowerCAmelCase : Any = list(reversed(snake_case_ ) )
_lowerCAmelCase : Dict = reversed_block_out_channels[0]
if out_block_type is None:
_lowerCAmelCase : Optional[Any] = out_channels
else:
_lowerCAmelCase : str = block_out_channels[0]
for i, up_block_type in enumerate(snake_case_ ):
_lowerCAmelCase : Dict = output_channel
_lowerCAmelCase : List[Any] = (
reversed_block_out_channels[i + 1] if i < len(snake_case_ ) - 1 else final_upsample_channels
)
_lowerCAmelCase : Dict = i == len(snake_case_ ) - 1
_lowerCAmelCase : str = get_up_block(
snake_case_ , num_layers=snake_case_ , in_channels=snake_case_ , out_channels=snake_case_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(snake_case_ )
_lowerCAmelCase : List[Any] = output_channel
# out
_lowerCAmelCase : Dict = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 3_2 )
_lowerCAmelCase : List[str] = get_out_block(
out_block_type=snake_case_ , num_groups_out=snake_case_ , embed_dim=block_out_channels[0] , out_channels=snake_case_ , act_fn=snake_case_ , fc_dim=block_out_channels[-1] // 4 , )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ = True , ):
_lowerCAmelCase : List[str] = timestep
if not torch.is_tensor(snake_case_ ):
_lowerCAmelCase : Optional[int] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(snake_case_ ) and len(timesteps.shape ) == 0:
_lowerCAmelCase : Union[str, Any] = timesteps[None].to(sample.device )
_lowerCAmelCase : List[str] = self.time_proj(snake_case_ )
if self.config.use_timestep_embedding:
_lowerCAmelCase : str = self.time_mlp(snake_case_ )
else:
_lowerCAmelCase : str = timestep_embed[..., None]
_lowerCAmelCase : Any = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
_lowerCAmelCase : str = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
_lowerCAmelCase : List[str] = ()
for downsample_block in self.down_blocks:
_lowerCAmelCase , _lowerCAmelCase : List[str] = downsample_block(hidden_states=snake_case_ , temb=snake_case_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
_lowerCAmelCase : List[str] = self.mid_block(snake_case_ , snake_case_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
_lowerCAmelCase : Any = down_block_res_samples[-1:]
_lowerCAmelCase : Tuple = down_block_res_samples[:-1]
_lowerCAmelCase : List[str] = upsample_block(snake_case_ , res_hidden_states_tuple=snake_case_ , temb=snake_case_ )
# 5. post-process
if self.out_block:
_lowerCAmelCase : int = self.out_block(snake_case_ , snake_case_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=snake_case_ )
| 309 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCAmelCase ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[int] ) -> Union[str, Any]:
# ===== initialization =====
_lowerCAmelCase : Tuple = Mock()
_lowerCAmelCase : Any = conn, Mock()
_lowerCAmelCase : Optional[Any] = iter([1, None] )
_lowerCAmelCase : str = lambda _lowerCamelCase : next(_lowerCamelCase )
# ===== invoke =====
send_file(filename="""mytext.txt""" , testing=_lowerCamelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 309 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowerCamelCase_ ( a__ ):
'''simple docstring'''
a__ = 42
class lowerCamelCase_ ( a__ ,a__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Any , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 3 , __lowerCamelCase : Tuple[str] = ("DownEncoderBlock2D",) , __lowerCamelCase : Tuple[str] = ("UpDecoderBlock2D",) , __lowerCamelCase : Tuple[int] = (64,) , __lowerCamelCase : int = 1 , __lowerCamelCase : str = "silu" , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 2_56 , __lowerCamelCase : int = 32 , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : float = 0.18215 , __lowerCamelCase : str = "group" , ) -> Optional[Any]:
super().__init__()
# pass init params to Encoder
A : Optional[Any] = Encoder(
in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , down_block_types=_lowerCamelCase , block_out_channels=_lowerCamelCase , layers_per_block=_lowerCamelCase , act_fn=_lowerCamelCase , norm_num_groups=_lowerCamelCase , double_z=_lowerCamelCase , )
A : int = vq_embed_dim if vq_embed_dim is not None else latent_channels
A : Tuple = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
A : Dict = VectorQuantizer(_lowerCamelCase , _lowerCamelCase , beta=0.25 , remap=_lowerCamelCase , sane_index_shape=_lowerCamelCase )
A : str = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
# pass init params to Decoder
A : List[Any] = Decoder(
in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , up_block_types=_lowerCamelCase , block_out_channels=_lowerCamelCase , layers_per_block=_lowerCamelCase , act_fn=_lowerCamelCase , norm_num_groups=_lowerCamelCase , norm_type=_lowerCamelCase , )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> List[Any]:
A : Any = self.encoder(_lowerCamelCase )
A : str = self.quant_conv(_lowerCamelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_lowerCamelCase )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__ ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = False , __lowerCamelCase : bool = True ) -> List[Any]:
if not force_not_quantize:
A : Union[str, Any] = self.quantize(_lowerCamelCase )
else:
A : Tuple = h
A : int = self.post_quant_conv(_lowerCamelCase )
A : List[Any] = self.decoder(_lowerCamelCase , quant if self.config.norm_type == "spatial" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True ) -> Union[str, Any]:
A : List[str] = sample
A : Dict = self.encode(_lowerCamelCase ).latents
A : Optional[Any] = self.decode(_lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase ) | 354 |
from math import factorial
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers" )
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("the function is defined for non-negative integers" )
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0" )
A : Union[str, Any] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
A : Union[str, Any] = float(factorial(_lowerCamelCase ) )
coefficient /= factorial(_lowerCamelCase ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("""Probability of 2 successes out of 4 trails""")
print("""with probability of 0.75 is:""", end=""" """)
print(binomial_distribution(2, 4, 0.75)) | 256 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , a : List[Any] , a : List[Any]=13 , a : Dict=7 , a : Optional[Any]=True , a : List[Any]=True , a : Tuple=False , a : Dict=True , a : Dict=99 , a : List[str]=32 , a : Dict=5 , a : Dict=4 , a : Tuple=37 , a : str="gelu" , a : Optional[Any]=0.1 , a : Optional[int]=0.1 , a : int=512 , a : Union[str, Any]=16 , a : Dict=2 , a : List[str]=0.02 , a : Any=3 , a : Tuple=4 , a : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = parent
SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = seq_length
SCREAMING_SNAKE_CASE : Optional[Any] = is_training
SCREAMING_SNAKE_CASE : str = use_input_mask
SCREAMING_SNAKE_CASE : List[str] = use_token_type_ids
SCREAMING_SNAKE_CASE : int = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Any = num_attention_heads
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : str = type_vocab_size
SCREAMING_SNAKE_CASE : str = type_sequence_label_size
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : Any = num_labels
SCREAMING_SNAKE_CASE : List[Any] = num_choices
SCREAMING_SNAKE_CASE : Dict = scope
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Tuple = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : str , a : Any , a : Optional[Any] , a : List[str] , a : List[str] , a : Union[str, Any] , a : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Tuple = model(a , a )
SCREAMING_SNAKE_CASE : str = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Optional[Any] , a : Dict , a : Tuple , a : Optional[int] , a : Optional[Any] , a : Any , a : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : str , a : Optional[Any] , a : int , a : Tuple , a : Union[str, Any] , a : int , a : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = model(
a , attention_mask=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 : Tuple , a : str , a : Any , a : Optional[int] , a : Dict , a : Dict , a : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.num_labels
SCREAMING_SNAKE_CASE : List[Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[int] , a : Dict , a : Any , a : List[Any] , a : List[Any] , a : Dict , a : List[str] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Dict = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : str , a : Any , a : int , a : Dict , a : int , a : Optional[Any] , a : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.num_choices
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Tuple = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : Union[str, Any] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Dict = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : int = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Dict = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : int = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 76 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
SCREAMING_SNAKE_CASE : Optional[int] = len(A ) - 1
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
SCREAMING_SNAKE_CASE : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree, A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(A ), 5 ) == 1
return output_values
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
SCREAMING_SNAKE_CASE : str = self.basis_function(A )
SCREAMING_SNAKE_CASE : str = 0.0
SCREAMING_SNAKE_CASE : List[Any] = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def UpperCamelCase_ ( self, A = 0.01 ):
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
SCREAMING_SNAKE_CASE : list[float] = [] # x coordinates of points to plot
SCREAMING_SNAKE_CASE : list[float] = [] # y coordinates of points to plot
SCREAMING_SNAKE_CASE : List[str] = 0.0
while t <= 1:
SCREAMING_SNAKE_CASE : Optional[int] = self.bezier_curve_function(A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
SCREAMING_SNAKE_CASE : List[Any] = [i[0] for i in self.list_of_points]
SCREAMING_SNAKE_CASE : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
A, A, color='blue', label='Curve of Degree ' + str(self.degree ), )
plt.scatter(A, A, color='red', label='Control Points' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 251 | 0 |
from math import factorial
lowercase_ = {str(digit): factorial(digit) for digit in range(10)}
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if not isinstance(__a , __a ):
raise TypeError("Parameter number must be int" )
if number < 0:
raise ValueError("Parameter number must be greater than or equal to 0" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__a ) )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ = 60 , SCREAMING_SNAKE_CASE_ = 100_0000 ):
if not isinstance(__a , __a ) or not isinstance(__a , __a ):
raise TypeError("Parameters chain_length and number_limit must be int" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"Parameters chain_length and number_limit must be greater than 0" )
# the counter for the chains with the exact desired length
lowercase__ = 0
# the cached sizes of the previous chains
lowercase__ = {}
for start_chain_element in range(1 , __a ):
# The temporary set will contain the elements of the chain
lowercase__ = set()
lowercase__ = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
lowercase__ = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__a )
chain_set_length += 1
lowercase__ = digit_factorial_sum(__a )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
lowercase__ = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
| 355 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = int(SCREAMING_SNAKE_CASE_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE_ )
lowercase__ , lowercase__ = divmod(SCREAMING_SNAKE_CASE_ , 2 )
return binary_recursive(SCREAMING_SNAKE_CASE_ ) + str(SCREAMING_SNAKE_CASE_ )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = str(SCREAMING_SNAKE_CASE_ ).strip()
if not number:
raise ValueError("No input value was provided" )
lowercase__ = "-" if number.startswith("-" ) else ""
lowercase__ = number.lstrip("-" )
if not number.isnumeric():
raise ValueError("Input value is not an integer" )
return f'''{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_ ) )}'''
if __name__ == "__main__":
from doctest import testmod
testmod()
| 224 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class snake_case__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = StableUnCLIPPipeline
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_SCREAMING_SNAKE_CASE = False
def lowercase_ ( self : Dict ) ->Union[str, Any]:
snake_case__ : Optional[int] = 3_2
snake_case__ : List[str] = embedder_hidden_size
# prior components
torch.manual_seed(0 )
snake_case__ : Optional[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
torch.manual_seed(0 )
snake_case__ : int = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=_snake_case, projection_dim=_snake_case, 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 )
snake_case__ : List[str] = PriorTransformer(
num_attention_heads=2, attention_head_dim=1_2, embedding_dim=_snake_case, num_layers=1, )
torch.manual_seed(0 )
snake_case__ : List[Any] = DDPMScheduler(
variance_type='fixed_small_log', prediction_type='sample', num_train_timesteps=1_0_0_0, clip_sample=_snake_case, clip_sample_range=5.0, beta_schedule='squaredcos_cap_v2', )
# regular denoising components
torch.manual_seed(0 )
snake_case__ : Optional[Any] = StableUnCLIPImageNormalizer(embedding_dim=_snake_case )
snake_case__ : Dict = DDPMScheduler(beta_schedule='squaredcos_cap_v2' )
torch.manual_seed(0 )
snake_case__ : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
torch.manual_seed(0 )
snake_case__ : Any = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=_snake_case, 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 )
snake_case__ : Optional[Any] = 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=_snake_case, layers_per_block=1, upcast_attention=_snake_case, use_linear_projection=_snake_case, )
torch.manual_seed(0 )
snake_case__ : Tuple = DDIMScheduler(
beta_schedule='scaled_linear', beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, prediction_type='v_prediction', set_alpha_to_one=_snake_case, steps_offset=1, )
torch.manual_seed(0 )
snake_case__ : Tuple = AutoencoderKL()
snake_case__ : Dict = {
# prior components
'prior_tokenizer': prior_tokenizer,
'prior_text_encoder': prior_text_encoder,
'prior': prior,
'prior_scheduler': prior_scheduler,
# image noising components
'image_normalizer': image_normalizer,
'image_noising_scheduler': image_noising_scheduler,
# regular denoising components
'tokenizer': tokenizer,
'text_encoder': text_encoder,
'unet': unet,
'scheduler': scheduler,
'vae': vae,
}
return components
def lowercase_ ( self : List[str], _snake_case : str, _snake_case : Optional[int]=0 ) ->int:
if str(_snake_case ).startswith('mps' ):
snake_case__ : Optional[Any] = torch.manual_seed(_snake_case )
else:
snake_case__ : Any = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
snake_case__ : str = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'prior_num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self : Any ) ->List[Any]:
snake_case__ : Tuple = torch_device == 'cpu'
self._test_attention_slicing_forward_pass(test_max_difference=_snake_case )
def lowercase_ ( self : Union[str, Any] ) ->List[str]:
snake_case__ : Optional[int] = torch_device in ['cpu', 'mps']
self._test_inference_batch_single_identical(test_max_difference=_snake_case )
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : Any ) ->Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : List[str] ) ->Union[str, Any]:
snake_case__ : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )
snake_case__ : int = StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l', torch_dtype=torch.floataa )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
# 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()
snake_case__ : Optional[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
snake_case__ : Dict = pipe('anime turle', generator=_snake_case, output_type='np' )
snake_case__ : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_snake_case, _snake_case )
def lowercase_ ( self : Optional[int] ) ->Optional[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case__ : Tuple = StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l', torch_dtype=torch.floataa )
snake_case__ : Optional[int] = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
snake_case__ : str = pipe(
'anime turtle', prior_num_inference_steps=2, num_inference_steps=2, output_type='np', )
snake_case__ : Union[str, Any] = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 277 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
a_ :Any = random.Random()
def lowercase_ (A : int , A : Union[str, Any]=1.0 , A : List[str]=None , A : Any=None ):
if rng is None:
snake_case__ : List[str] = global_rng
snake_case__ : int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any], _snake_case : List[str], _snake_case : Tuple=7, _snake_case : Union[str, Any]=4_0_0, _snake_case : Any=2_0_0_0, _snake_case : Dict=1, _snake_case : Optional[Any]=0.0, _snake_case : List[Any]=1_6_0_0_0, _snake_case : List[Any]=True, _snake_case : List[Any]=8_0, _snake_case : Dict=1_6, _snake_case : str=6_4, _snake_case : Tuple="hann_window", _snake_case : Union[str, Any]=8_0, _snake_case : Optional[Any]=7_6_0_0, _snake_case : str=1e-10, _snake_case : Any=True, ) ->Union[str, Any]:
snake_case__ : Optional[int] = parent
snake_case__ : Optional[Any] = batch_size
snake_case__ : List[Any] = min_seq_length
snake_case__ : List[Any] = max_seq_length
snake_case__ : Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
snake_case__ : Tuple = feature_size
snake_case__ : List[Any] = padding_value
snake_case__ : Any = sampling_rate
snake_case__ : Dict = do_normalize
snake_case__ : Union[str, Any] = num_mel_bins
snake_case__ : Any = hop_length
snake_case__ : Any = win_length
snake_case__ : Any = win_function
snake_case__ : Optional[int] = fmin
snake_case__ : int = fmax
snake_case__ : Union[str, Any] = mel_floor
snake_case__ : Union[str, Any] = return_attention_mask
def lowercase_ ( self : Optional[int] ) ->List[str]:
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def lowercase_ ( self : Any, _snake_case : Optional[Any]=False, _snake_case : List[str]=False ) ->Union[str, Any]:
def _flatten(_snake_case : List[str] ):
return list(itertools.chain(*_snake_case ) )
if equal_length:
snake_case__ : Any = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
snake_case__ : int = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
snake_case__ : Any = [np.asarray(_snake_case ) for x in speech_inputs]
return speech_inputs
def lowercase_ ( self : Union[str, Any], _snake_case : str=False, _snake_case : Dict=False ) ->List[str]:
if equal_length:
snake_case__ : Optional[Any] = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
snake_case__ : List[str] = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
snake_case__ : int = [np.asarray(_snake_case ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case__ ( lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = SpeechTaFeatureExtractor
def lowercase_ ( self : int ) ->Union[str, Any]:
snake_case__ : List[str] = SpeechTaFeatureExtractionTester(self )
def lowercase_ ( self : Any, _snake_case : Dict ) ->Any:
self.assertTrue(np.all(np.mean(_snake_case, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(_snake_case, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase_ ( self : List[Any] ) ->Union[str, Any]:
# Tests that all call wrap to encode_plus and batch_encode_plus
snake_case__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
snake_case__ : int = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : Tuple = [np.asarray(_snake_case ) for speech_input in speech_inputs]
# Test not batched input
snake_case__ : str = feat_extract(speech_inputs[0], return_tensors='np' ).input_values
snake_case__ : List[str] = feat_extract(np_speech_inputs[0], return_tensors='np' ).input_values
self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3 ) )
# Test batched
snake_case__ : Any = feat_extract(_snake_case, return_tensors='np' ).input_values
snake_case__ : Union[str, Any] = feat_extract(_snake_case, return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case ):
self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3 ) )
def lowercase_ ( self : int ) ->Optional[int]:
snake_case__ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case__ : Tuple = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : int = ['longest', 'max_length', 'do_not_pad']
snake_case__ : List[str] = [None, 1_6_0_0, None]
for max_length, padding in zip(_snake_case, _snake_case ):
snake_case__ : Optional[int] = feat_extract(_snake_case, padding=_snake_case, max_length=_snake_case, return_tensors='np' )
snake_case__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def lowercase_ ( self : Union[str, Any] ) ->Optional[Any]:
snake_case__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case__ : Tuple = range(8_0_0, 1_4_0_0, 2_0_0 )
snake_case__ : Optional[Any] = [floats_list((1, x) )[0] for x in lengths]
snake_case__ : Union[str, Any] = ['longest', 'max_length', 'do_not_pad']
snake_case__ : str = [None, 1_6_0_0, None]
for max_length, padding in zip(_snake_case, _snake_case ):
snake_case__ : List[str] = feat_extract(_snake_case, max_length=_snake_case, padding=_snake_case )
snake_case__ : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def lowercase_ ( self : List[Any] ) ->Optional[Any]:
snake_case__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case__ : str = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : Optional[Any] = feat_extract(
_snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='max_length', return_tensors='np' )
snake_case__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def lowercase_ ( self : int ) ->Union[str, Any]:
snake_case__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case__ : Dict = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : str = feat_extract(
_snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='longest', return_tensors='np' )
snake_case__ : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
snake_case__ : Tuple = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : List[str] = feat_extract(
_snake_case, truncation=_snake_case, max_length=2_0_0_0, padding='longest', return_tensors='np' )
snake_case__ : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
def lowercase_ ( self : List[str] ) ->Dict:
snake_case__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case__ : List[Any] = np.random.rand(1_0_0 ).astype(np.floataa )
snake_case__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
snake_case__ : int = feature_extractor.pad([{'input_values': inputs}], return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
snake_case__ : Optional[int] = feature_extractor.pad([{'input_values': inputs}], return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def lowercase_ ( self : Optional[int] ) ->Optional[Any]:
# Tests that all call wrap to encode_plus and batch_encode_plus
snake_case__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
snake_case__ : List[Any] = [floats_list((1, x) )[0] for x in range(8_0_0, 1_4_0_0, 2_0_0 )]
snake_case__ : Dict = [np.asarray(_snake_case ) for speech_input in speech_inputs]
# Test feature size
snake_case__ : Optional[int] = feature_extractor(audio_target=_snake_case, padding=_snake_case, return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
snake_case__ : Dict = feature_extractor(speech_inputs[0], return_tensors='np' ).input_values
snake_case__ : Any = feature_extractor(np_speech_inputs[0], return_tensors='np' ).input_values
self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3 ) )
# Test batched
snake_case__ : Dict = feature_extractor(_snake_case, return_tensors='np' ).input_values
snake_case__ : Dict = feature_extractor(_snake_case, return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case ):
self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
snake_case__ : Optional[Any] = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
snake_case__ : int = np.asarray(_snake_case )
snake_case__ : Union[str, Any] = feature_extractor(_snake_case, return_tensors='np' ).input_values
snake_case__ : Union[str, Any] = feature_extractor(_snake_case, return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case ):
self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3 ) )
def lowercase_ ( self : Union[str, Any] ) ->str:
snake_case__ : int = self.feat_extract_tester.prepare_inputs_for_target()
snake_case__ : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case__ : Optional[Any] = feat_extract.model_input_names[0]
snake_case__ : Tuple = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case, processed_features[input_name] ) ) )
snake_case__ : int = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case )
snake_case__ : Union[str, Any] = BatchFeature({input_name: speech_inputs}, tensor_type='np' )
snake_case__ : Dict = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
snake_case__ : List[str] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def lowercase_ ( self : List[str] ) ->Any:
snake_case__ : int = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case )
snake_case__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case__ : Tuple = feat_extract.model_input_names[0]
snake_case__ : List[Any] = BatchFeature({input_name: speech_inputs}, tensor_type='pt' )
snake_case__ : Tuple = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
snake_case__ : Any = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def lowercase_ ( self : Optional[int] ) ->Tuple:
snake_case__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
snake_case__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_target()
snake_case__ : Optional[Any] = feat_extract.model_input_names[0]
snake_case__ : List[str] = BatchFeature({input_name: speech_inputs} )
snake_case__ : int = feat_extract.num_mel_bins # hack!
snake_case__ : Tuple = feat_extract.pad(_snake_case, padding='longest', return_tensors='np' )[input_name]
snake_case__ : Union[str, Any] = feat_extract.pad(_snake_case, padding='longest', return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def lowercase_ ( self : int ) ->Any:
snake_case__ : Any = self.feat_extract_dict
snake_case__ : List[Any] = True
snake_case__ : Union[str, Any] = self.feature_extraction_class(**_snake_case )
snake_case__ : Any = self.feat_extract_tester.prepare_inputs_for_target()
snake_case__ : List[Any] = [len(_snake_case ) for x in speech_inputs]
snake_case__ : Union[str, Any] = feat_extract.model_input_names[0]
snake_case__ : Optional[int] = BatchFeature({input_name: speech_inputs} )
snake_case__ : List[str] = feat_extract.num_mel_bins # hack!
snake_case__ : str = feat_extract.pad(_snake_case, padding='longest', return_tensors='np' )
self.assertIn('attention_mask', _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ), list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist(), _snake_case )
def lowercase_ ( self : Optional[int] ) ->str:
snake_case__ : int = self.feat_extract_dict
snake_case__ : List[str] = True
snake_case__ : Tuple = self.feature_extraction_class(**_snake_case )
snake_case__ : List[str] = self.feat_extract_tester.prepare_inputs_for_target()
snake_case__ : str = [len(_snake_case ) for x in speech_inputs]
snake_case__ : Optional[Any] = feat_extract.model_input_names[0]
snake_case__ : Optional[int] = BatchFeature({input_name: speech_inputs} )
snake_case__ : Optional[Any] = min(_snake_case )
snake_case__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
snake_case__ : Tuple = feat_extract.pad(
_snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='np' )
self.assertIn('attention_mask', _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ), [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist(), [max_length for x in speech_inputs] )
def lowercase_ ( self : List[Any], _snake_case : Optional[int] ) ->Optional[Any]:
from datasets import load_dataset
snake_case__ : str = load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation' )
# automatic decoding with librispeech
snake_case__ : Dict = ds.sort('id' ).select(range(_snake_case ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowercase_ ( self : str ) ->str:
# fmt: off
snake_case__ : List[Any] = torch.tensor(
[2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03] )
# fmt: on
snake_case__ : Union[str, Any] = self._load_datasamples(1 )
snake_case__ : Optional[int] = SpeechTaFeatureExtractor()
snake_case__ : List[Any] = feature_extractor(_snake_case, return_tensors='pt' ).input_values
self.assertEquals(input_values.shape, (1, 9_3_6_8_0) )
self.assertTrue(torch.allclose(input_values[0, :3_0], _snake_case, atol=1e-6 ) )
def lowercase_ ( self : Any ) ->str:
# fmt: off
snake_case__ : Optional[Any] = torch.tensor(
[-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
-3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
-3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
-3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8] )
# fmt: on
snake_case__ : List[str] = self._load_datasamples(1 )
snake_case__ : str = SpeechTaFeatureExtractor()
snake_case__ : Optional[Any] = feature_extractor(audio_target=_snake_case, return_tensors='pt' ).input_values
self.assertEquals(input_values.shape, (1, 3_6_6, 8_0) )
self.assertTrue(torch.allclose(input_values[0, 0, :3_0], _snake_case, atol=1e-4 ) )
| 277 | 1 |
"""simple docstring"""
from __future__ import annotations
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
"""simple docstring"""
if len(lowerCamelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase_ , n - 1 )
rec_insertion_sort(lowerCamelCase_ , n - 1 )
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
"""simple docstring"""
if index >= len(lowerCamelCase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
lowerCAmelCase__ :Union[str, Any] = (
collection[index],
collection[index - 1],
)
insert_next(lowerCamelCase_ , index + 1 )
if __name__ == "__main__":
__A = input("""Enter integers separated by spaces: """)
__A = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 358 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Tuple = """facebook/bart-large-mnli"""
__magic_name__ :Any = (
"""This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
"""should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
"""It returns the most likely label in the list of provided `labels` for the input text."""
)
__magic_name__ :Optional[int] = """text_classifier"""
__magic_name__ :List[Any] = AutoTokenizer
__magic_name__ :str = AutoModelForSequenceClassification
__magic_name__ :int = ["""text""", ["""text"""]]
__magic_name__ :int = ["""text"""]
def snake_case ( self ):
'''simple docstring'''
super().setup()
lowerCAmelCase__ :Any = self.model.config
lowerCAmelCase__ :Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('entail' ):
lowerCAmelCase__ :Optional[Any] = int(__UpperCAmelCase )
if self.entailment_id == -1:
raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.' )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = labels
return self.pre_processor(
[text] * len(__UpperCAmelCase ) , [F"This example is {label}" for label in labels] , return_tensors='pt' , padding='max_length' , )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = outputs.logits
lowerCAmelCase__ :int = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 254 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : int = 'vision-encoder-decoder'
UpperCAmelCase__ : Union[str, Any] = True
def __init__( self: List[str] , **UpperCamelCase_: List[Any] ):
super().__init__(**UpperCamelCase_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F'A configuraton of type {self.model_type} cannot be instantiated because '
F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}' )
__lowerCamelCase = kwargs.pop("""encoder""" )
__lowerCamelCase = encoder_config.pop("""model_type""" )
__lowerCamelCase = kwargs.pop("""decoder""" )
__lowerCamelCase = decoder_config.pop("""model_type""" )
__lowerCamelCase = AutoConfig.for_model(UpperCamelCase_ , **UpperCamelCase_ )
__lowerCamelCase = AutoConfig.for_model(UpperCamelCase_ , **UpperCamelCase_ )
__lowerCamelCase = True
@classmethod
def lowerCAmelCase__ ( cls: Tuple , UpperCamelCase_: PretrainedConfig , UpperCamelCase_: PretrainedConfig , **UpperCamelCase_: Any ):
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
__lowerCamelCase = True
__lowerCamelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.encoder.to_dict()
__lowerCamelCase = self.decoder.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Tuple = version.parse('1.11')
@property
def lowerCAmelCase__ ( self: Any ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase__ ( self: Union[str, Any] ):
return 1E-4
@property
def lowerCAmelCase__ ( self: Any ):
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class lowerCamelCase__( __lowerCamelCase):
@property
def lowerCAmelCase__ ( self: int ):
__lowerCamelCase = OrderedDict()
__lowerCamelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
__lowerCamelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
__lowerCamelCase = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: "PreTrainedTokenizerBase" , UpperCamelCase_: int = -1 , UpperCamelCase_: int = -1 , UpperCamelCase_: bool = False , UpperCamelCase_: Optional["TensorType"] = None , ):
import torch
__lowerCamelCase = OrderedDict()
__lowerCamelCase = super().generate_dummy_inputs(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = dummy_input["""input_ids"""].shape
__lowerCamelCase = (batch, encoder_sequence, self._config.encoder_hidden_size)
__lowerCamelCase = dummy_input.pop("""input_ids""" )
__lowerCamelCase = dummy_input.pop("""attention_mask""" )
__lowerCamelCase = torch.zeros(UpperCamelCase_ )
return common_inputs
class lowerCamelCase__( __lowerCamelCase):
@property
def lowerCAmelCase__ ( self: Union[str, Any] ):
pass
def lowerCAmelCase__ ( self: str , UpperCamelCase_: PretrainedConfig ):
return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase_ )
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: PretrainedConfig , UpperCamelCase_: PretrainedConfig , UpperCamelCase_: str = "default" ):
__lowerCamelCase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase_ , UpperCamelCase_ )
| 12 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowercase ( __A ):
'''simple docstring'''
return (data["data"], data["target"])
def _lowercase ( __A ,__A ,__A ):
'''simple docstring'''
__UpperCamelCase = XGBRegressor(verbosity=0 ,random_state=42 )
xgb.fit(__A ,__A )
# Predict target for test data
__UpperCamelCase = xgb.predict(__A )
__UpperCamelCase = predictions.reshape(len(__A ) ,1 )
return predictions
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = fetch_california_housing()
__UpperCamelCase , __UpperCamelCase = data_handling(__A )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = train_test_split(
__A ,__A ,test_size=0.25 ,random_state=1 )
__UpperCamelCase = xgboost(__A ,__A ,__A )
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(__A ,__A )}" )
print(f"Mean Square Error : {mean_squared_error(__A ,__A )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 349 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ = 1_60_00 ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(round(sample_rate * max_length ) )
if len(snake_case__ ) <= sample_length:
return wav
_SCREAMING_SNAKE_CASE = randint(0 ,len(snake_case__ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __UpperCAmelCase :
__snake_case : Optional[str] = field(default=_UpperCAmelCase ,metadata={"help": "Name of a dataset from the datasets package"} )
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "A file containing the training audio paths and labels."} )
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "A file containing the validation audio paths and labels."} )
__snake_case : str = field(
default="train" ,metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} ,)
__snake_case : str = field(
default="validation" ,metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
} ,)
__snake_case : str = field(
default="audio" ,metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"} ,)
__snake_case : str = field(
default="label" ,metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
__snake_case : Optional[int] = field(
default=_UpperCAmelCase ,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} ,)
__snake_case : Optional[int] = field(
default=_UpperCAmelCase ,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} ,)
__snake_case : float = field(
default=20 ,metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} ,)
@dataclass
class __UpperCAmelCase :
__snake_case : str = field(
default="facebook/wav2vec2-base" ,metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ,)
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
__snake_case : str = field(
default="main" ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,)
__snake_case : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "Name or path of preprocessor config."} )
__snake_case : bool = field(
default=_UpperCAmelCase ,metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
__snake_case : bool = field(
default=_UpperCAmelCase ,metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
__snake_case : bool = field(
default=_UpperCAmelCase ,metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} ,)
__snake_case : Optional[bool] = field(
default=_UpperCAmelCase ,metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
__snake_case : bool = field(
default=_UpperCAmelCase ,metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} ,)
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , UpperCAmelCase_ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def __lowerCamelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 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.
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" ,snake_case__ ,snake_case__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,handlers=[logging.StreamHandler(sys.stdout )] ,)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = training_args.get_process_log_level()
logger.setLevel(snake_case__ )
transformers.utils.logging.set_verbosity(snake_case__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_SCREAMING_SNAKE_CASE = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_SCREAMING_SNAKE_CASE = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset and prepare it for the audio classification task.
_SCREAMING_SNAKE_CASE = DatasetDict()
_SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.train_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
_SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.eval_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
"""Make sure to set `--label_column_name` to the correct text column - one of """
F'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_SCREAMING_SNAKE_CASE = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path ,return_attention_mask=model_args.attention_mask ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_SCREAMING_SNAKE_CASE = raw_datasets.cast_column(
data_args.audio_column_name ,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_SCREAMING_SNAKE_CASE = feature_extractor.model_input_names[0]
def train_transforms(snake_case__ ):
_SCREAMING_SNAKE_CASE = []
for audio in batch[data_args.audio_column_name]:
_SCREAMING_SNAKE_CASE = random_subsample(
audio["""array"""] ,max_length=data_args.max_length_seconds ,sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(snake_case__ )
_SCREAMING_SNAKE_CASE = feature_extractor(snake_case__ ,sampling_rate=feature_extractor.sampling_rate )
_SCREAMING_SNAKE_CASE = {model_input_name: inputs.get(snake_case__ )}
_SCREAMING_SNAKE_CASE = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(snake_case__ ):
_SCREAMING_SNAKE_CASE = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
_SCREAMING_SNAKE_CASE = feature_extractor(snake_case__ ,sampling_rate=feature_extractor.sampling_rate )
_SCREAMING_SNAKE_CASE = {model_input_name: inputs.get(snake_case__ )}
_SCREAMING_SNAKE_CASE = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_SCREAMING_SNAKE_CASE = raw_datasets["""train"""].features[data_args.label_column_name].names
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = {}, {}
for i, label in enumerate(snake_case__ ):
_SCREAMING_SNAKE_CASE = str(snake_case__ )
_SCREAMING_SNAKE_CASE = label
# Load the accuracy metric from the datasets package
_SCREAMING_SNAKE_CASE = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(snake_case__ ):
_SCREAMING_SNAKE_CASE = np.argmax(eval_pred.predictions ,axis=1 )
return metric.compute(predictions=snake_case__ ,references=eval_pred.label_ids )
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path ,num_labels=len(snake_case__ ) ,labelaid=snake_case__ ,idalabel=snake_case__ ,finetuning_task="""audio-classification""" ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
_SCREAMING_SNAKE_CASE = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) ,config=snake_case__ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_SCREAMING_SNAKE_CASE = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(snake_case__ ,output_all_columns=snake_case__ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_SCREAMING_SNAKE_CASE = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(snake_case__ ,output_all_columns=snake_case__ )
# Initialize our trainer
_SCREAMING_SNAKE_CASE = Trainer(
model=snake_case__ ,args=snake_case__ ,train_dataset=raw_datasets["""train"""] if training_args.do_train else None ,eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None ,compute_metrics=snake_case__ ,tokenizer=snake_case__ ,)
# Training
if training_args.do_train:
_SCREAMING_SNAKE_CASE = None
if training_args.resume_from_checkpoint is not None:
_SCREAMING_SNAKE_CASE = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_SCREAMING_SNAKE_CASE = last_checkpoint
_SCREAMING_SNAKE_CASE = trainer.train(resume_from_checkpoint=snake_case__ )
trainer.save_model()
trainer.log_metrics("""train""" ,train_result.metrics )
trainer.save_metrics("""train""" ,train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_SCREAMING_SNAKE_CASE = trainer.evaluate()
trainer.log_metrics("""eval""" ,snake_case__ )
trainer.save_metrics("""eval""" ,snake_case__ )
# Write model card and (optionally) push to hub
_SCREAMING_SNAKE_CASE = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case__ )
else:
trainer.create_model_card(**snake_case__ )
if __name__ == "__main__":
main()
| 125 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
UpperCamelCase = '''0.12''' # assumed parallelism: 8
@require_flax
@is_staging_test
class __UpperCAmelCase (unittest.TestCase ):
@classmethod
def UpperCamelCase ( cls: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(UpperCAmelCase_ )
@classmethod
def UpperCamelCase ( cls: Union[str, Any] ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="""test-model-flax""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-model-flax-org""" )
except HTTPError:
pass
def UpperCamelCase ( self: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_SCREAMING_SNAKE_CASE = FlaxBertModel(UpperCAmelCase_ )
model.push_to_hub("""test-model-flax""" , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(F'{USER}/test-model-flax' )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(UpperCAmelCase_ , 1E-3 , msg=F'{key} not identical' )
# Reset repo
delete_repo(token=self._token , repo_id="""test-model-flax""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_ , repo_id="""test-model-flax""" , push_to_hub=UpperCAmelCase_ , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(F'{USER}/test-model-flax' )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(UpperCAmelCase_ , 1E-3 , msg=F'{key} not identical' )
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_SCREAMING_SNAKE_CASE = FlaxBertModel(UpperCAmelCase_ )
model.push_to_hub("""valid_org/test-model-flax-org""" , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(UpperCAmelCase_ , 1E-3 , msg=F'{key} not identical' )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-model-flax-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
UpperCAmelCase_ , repo_id="""valid_org/test-model-flax-org""" , push_to_hub=UpperCAmelCase_ , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
_SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(UpperCAmelCase_ , 1E-3 , msg=F'{key} not identical' )
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
_SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
_SCREAMING_SNAKE_CASE = False
return models_are_equal
@require_flax
class __UpperCAmelCase (unittest.TestCase ):
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
_SCREAMING_SNAKE_CASE = FlaxBertModel(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) )
with self.assertRaises(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ , subfolder=UpperCAmelCase_ )
self.assertTrue(check_models_equal(UpperCAmelCase_ , UpperCAmelCase_ ) )
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
_SCREAMING_SNAKE_CASE = FlaxBertModel(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) , max_shard_size="""10KB""" )
with self.assertRaises(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ , subfolder=UpperCAmelCase_ )
self.assertTrue(check_models_equal(UpperCAmelCase_ , UpperCAmelCase_ ) )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """bert"""
_SCREAMING_SNAKE_CASE = """hf-internal-testing/tiny-random-bert-subfolder"""
with self.assertRaises(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ , subfolder=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """bert"""
_SCREAMING_SNAKE_CASE = """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
with self.assertRaises(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(UpperCAmelCase_ , subfolder=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
| 125 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
A_ : List[str] = logging.get_logger(__name__)
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
SCREAMING_SNAKE_CASE__ = json.loads(snake_case__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
SCREAMING_SNAKE_CASE__ = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
SCREAMING_SNAKE_CASE__ = json.loads(snake_case__ )
if not mpi_options.get("""sagemaker_mpi_enabled""" , snake_case__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCamelCase (A__ ):
lowerCamelCase__ : str = field(
default='' ,metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'} ,)
def SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , __UpperCAmelCase , )
@cached_property
def SCREAMING_SNAKE_CASE ( self : Any ) -> "torch.device":
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
SCREAMING_SNAKE_CASE__ = torch.device("""cpu""" )
SCREAMING_SNAKE_CASE__ = 0
elif is_sagemaker_model_parallel_available():
SCREAMING_SNAKE_CASE__ = smp.local_rank()
SCREAMING_SNAKE_CASE__ = torch.device("""cuda""" , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
SCREAMING_SNAKE_CASE__ = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
SCREAMING_SNAKE_CASE__ = torch.device("""cuda""" , self.local_rank )
SCREAMING_SNAKE_CASE__ = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
SCREAMING_SNAKE_CASE__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
SCREAMING_SNAKE_CASE__ = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
SCREAMING_SNAKE_CASE__ = torch.device("""cuda""" , self.local_rank )
SCREAMING_SNAKE_CASE__ = 1
if device.type == "cuda":
torch.cuda.set_device(__UpperCAmelCase )
return device
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
return not is_sagemaker_model_parallel_available()
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
return False
| 165 |
"""simple docstring"""
from __future__ import annotations
import math
def A ( snake_case__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = str(snake_case__ )
SCREAMING_SNAKE_CASE__ = [n]
for i in range(1 , len(snake_case__ ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def A ( snake_case__ ):
'''simple docstring'''
if len(str(snake_case__ ) ) > 3:
if not is_prime(int(str(snake_case__ )[-3:] ) ) or not is_prime(int(str(snake_case__ )[:3] ) ):
return False
return True
def A ( snake_case__ = 11 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 13
while len(snake_case__ ) != count:
if validate(snake_case__ ):
SCREAMING_SNAKE_CASE__ = list_truncated_nums(snake_case__ )
if all(is_prime(snake_case__ ) for i in list_nums ):
list_truncated_primes.append(snake_case__ )
num += 2
return list_truncated_primes
def A ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(F'{sum(compute_truncated_primes(11)) = }')
| 165 | 1 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> Dict:
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def _A () -> str:
'''simple docstring'''
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {"vocab_file": "spiece.model"}
a_ : List[Any] = {
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
a_ : Dict = {"bert_for_seq_generation": 5_1_2}
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = []
_lowerCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self , __magic_name__ , __magic_name__="<s>" , __magic_name__="</s>" , __magic_name__="<unk>" , __magic_name__="<pad>" , __magic_name__="<::::>" , __magic_name__ = None , **__magic_name__ , ) -> None:
_a = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=__magic_name__ , eos_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , sep_token=__magic_name__ , sp_model_kwargs=self.sp_model_kwargs , **__magic_name__ , )
_a = vocab_file
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__magic_name__ )
@property
def __UpperCAmelCase ( self ) -> Dict:
return self.sp_model.get_piece_size()
def __UpperCAmelCase ( self ) -> str:
_a = {self.convert_ids_to_tokens(__magic_name__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Any:
_a = self.__dict__.copy()
_a = None
return state
def __setstate__( self , __magic_name__ ) -> int:
_a = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_a = {}
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
return self.sp_model.encode(__magic_name__ , out_type=__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
return self.sp_model.piece_to_id(__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
_a = self.sp_model.IdToPiece(__magic_name__ )
return token
def __UpperCAmelCase ( self , __magic_name__ ) -> Optional[Any]:
_a = []
_a = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__magic_name__ ) + token
_a = []
else:
current_sub_tokens.append(__magic_name__ )
out_string += self.sp_model.decode(__magic_name__ )
return out_string.strip()
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
if not os.path.isdir(__magic_name__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_a = os.path.join(
__magic_name__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __magic_name__ )
elif not os.path.isfile(self.vocab_file ):
with open(__magic_name__ , 'wb' ) as fi:
_a = self.sp_model.serialized_model_proto()
fi.write(__magic_name__ )
return (out_vocab_file,)
| 104 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class __lowerCamelCase ( _A , _A ):
'''simple docstring'''
A_ : str = "focalnet"
def __init__( self , __UpperCAmelCase=224 , __UpperCAmelCase=4 , __UpperCAmelCase=3 , __UpperCAmelCase=96 , __UpperCAmelCase=False , __UpperCAmelCase=[192, 384, 768, 768] , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[2, 2, 2, 2] , __UpperCAmelCase=[3, 3, 3, 3] , __UpperCAmelCase="gelu" , __UpperCAmelCase=4.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=False , __UpperCAmelCase=1e-4 , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=32 , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase , ) -> Union[str, Any]:
super().__init__(**__UpperCAmelCase )
_a = image_size
_a = patch_size
_a = num_channels
_a = embed_dim
_a = use_conv_embed
_a = hidden_sizes
_a = depths
_a = focal_levels
_a = focal_windows
_a = hidden_act
_a = mlp_ratio
_a = hidden_dropout_prob
_a = drop_path_rate
_a = use_layerscale
_a = layerscale_value
_a = use_post_layernorm
_a = use_post_layernorm_in_modulation
_a = normalize_modulator
_a = initializer_range
_a = layer_norm_eps
_a = encoder_stride
_a = ['''stem'''] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_a = get_aligned_output_features_output_indices(
out_features=__UpperCAmelCase , out_indices=__UpperCAmelCase , stage_names=self.stage_names ) | 320 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : Dict = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class _UpperCAmelCase ( unittest.TestCase , _A ):
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = load_tool('''text-question-answering''' )
self.tool.setup()
lowercase_ : Union[str, Any] = load_tool('''text-question-answering''' , remote=A )
def A ( self : Any ) -> List[str]:
lowercase_ : Union[str, Any] = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : str ) -> List[str]:
lowercase_ : int = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[Any] ) -> int:
lowercase_ : Optional[Any] = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : int = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 33 | 0 |
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 SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any ) -> Tuple:
return None
class SCREAMING_SNAKE_CASE__ :
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Any:
return None
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
snake_case__ : Optional[Any] = [
# (model_name, model_kwargs)
('''bert-base-cased''', {}),
('''gpt2''', {'''use_cache''': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(SCREAMING_SNAKE_CASE__ , 'tf' , 1_2 , **SCREAMING_SNAKE_CASE__ )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(SCREAMING_SNAKE_CASE__ , 'pt' , 1_2 , **SCREAMING_SNAKE_CASE__ )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> str:
from transformers import BertModel
a_ : Any = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(SCREAMING_SNAKE_CASE__ ) )
vocab_file.flush()
a_ : Any = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
a_ : List[str] = BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE__ ) ) )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
self._test_export(SCREAMING_SNAKE_CASE__ , 'pt' , 1_2 , SCREAMING_SNAKE_CASE__ )
@require_tf
@slow
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
a_ : Union[str, Any] = self._test_export(SCREAMING_SNAKE_CASE__ , 'tf' , 1_2 , **SCREAMING_SNAKE_CASE__ )
a_ : Any = quantize(Path(SCREAMING_SNAKE_CASE__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE__ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
a_ : Optional[int] = self._test_export(SCREAMING_SNAKE_CASE__ , 'pt' , 1_2 , **SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = quantize(SCREAMING_SNAKE_CASE__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE__ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Dict=None , **SCREAMING_SNAKE_CASE__ : List[str] ) -> List[str]:
try:
# Compute path
with TemporaryDirectory() as tempdir:
a_ : int = Path(SCREAMING_SNAKE_CASE__ ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
return path
except Exception as e:
self.fail(SCREAMING_SNAKE_CASE__ )
@require_torch
@require_tokenizers
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
from transformers import BertModel
a_ : Tuple = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
a_ : Tuple = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 'pt' )
@require_tf
@require_tokenizers
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
from transformers import TFBertModel
a_ : Any = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
a_ : List[str] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 'tf' )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[Any]:
a_ : Union[str, Any] = FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
a_ : List[Any] = infer_shapes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Assert all variables are present
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , SCREAMING_SNAKE_CASE__ )
self.assertSequenceEqual(variable_names[3:] , SCREAMING_SNAKE_CASE__ )
# 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 SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
a_ : Any = ['input_ids', 'attention_mask', 'token_type_ids']
a_ : Tuple = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
a_ : List[Any] = ensure_valid_input(FuncContiguousArgs() , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(SCREAMING_SNAKE_CASE__ ) , set(SCREAMING_SNAKE_CASE__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(SCREAMING_SNAKE_CASE__ , (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
a_ : Any = ensure_valid_input(FuncNonContiguousArgs() , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1 )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['input_ids'] )
self.assertEqual(ordered_input_names[0] , 'input_ids' )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Dict:
a_ : Tuple = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ) , '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx' , generated.as_posix() )
| 359 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 120 | 0 |
import numpy as np
from transformers import Pipeline
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = np.max(_A , axis=-1 , keepdims=_A )
lowercase = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_A )
class lowercase ( __UpperCAmelCase ):
def A__ ( self ,**A__):
lowercase = {}
if "second_text" in kwargs:
lowercase = kwargs['''second_text''']
return preprocess_kwargs, {}, {}
def A__ ( self ,A__ ,A__=None):
return self.tokenizer(UpperCamelCase__ ,text_pair=UpperCamelCase__ ,return_tensors=self.framework)
def A__ ( self ,A__):
return self.model(**UpperCamelCase__)
def A__ ( self ,A__):
lowercase = model_outputs.logits[0].numpy()
lowercase = softmax(UpperCamelCase__)
lowercase = np.argmax(UpperCamelCase__)
lowercase = self.model.config.idalabel[best_class]
lowercase = probabilities[best_class].item()
lowercase = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 101 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowerCAmelCase_ = 192
lowerCAmelCase_ = 768
lowerCAmelCase_ = 12
lowerCAmelCase_ = 3
lowerCAmelCase_ = [800, 1333]
lowerCAmelCase_ = False
elif yolos_name == "yolos_s_dWr":
lowerCAmelCase_ = 330
lowerCAmelCase_ = 14
lowerCAmelCase_ = 6
lowerCAmelCase_ = 1320
elif "yolos_s" in yolos_name:
lowerCAmelCase_ = 384
lowerCAmelCase_ = 1536
lowerCAmelCase_ = 12
lowerCAmelCase_ = 6
elif "yolos_b" in yolos_name:
lowerCAmelCase_ = [800, 1344]
lowerCAmelCase_ = 91
lowerCAmelCase_ = '''huggingface/label-files'''
lowerCAmelCase_ = '''coco-detection-id2label.json'''
lowerCAmelCase_ = json.load(open(hf_hub_download(_A , _A , repo_type='''dataset''' ) , '''r''' ) )
lowerCAmelCase_ = {int(_A ): v for k, v in idalabel.items()}
lowerCAmelCase_ = idalabel
lowerCAmelCase_ = {v: k for k, v in idalabel.items()}
return config
def __UpperCamelCase ( _A , _A , _A = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase_ = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
lowerCAmelCase_ = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ = in_proj_weight[: config.hidden_size, :]
lowerCAmelCase_ = in_proj_bias[: config.hidden_size]
lowerCAmelCase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase_ = in_proj_weight[-config.hidden_size :, :]
lowerCAmelCase_ = in_proj_bias[-config.hidden_size :]
def __UpperCamelCase ( _A ):
if "backbone" in name:
lowerCAmelCase_ = name.replace('''backbone''' , '''vit''' )
if "cls_token" in name:
lowerCAmelCase_ = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "det_token" in name:
lowerCAmelCase_ = name.replace('''det_token''' , '''embeddings.detection_tokens''' )
if "mid_pos_embed" in name:
lowerCAmelCase_ = name.replace('''mid_pos_embed''' , '''encoder.mid_position_embeddings''' )
if "pos_embed" in name:
lowerCAmelCase_ = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
lowerCAmelCase_ = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "blocks" in name:
lowerCAmelCase_ = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
lowerCAmelCase_ = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
lowerCAmelCase_ = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowerCAmelCase_ = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowerCAmelCase_ = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowerCAmelCase_ = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowerCAmelCase_ = name.replace('''mlp.fc2''' , '''output.dense''' )
if "class_embed" in name:
lowerCAmelCase_ = name.replace('''class_embed''' , '''class_labels_classifier''' )
if "bbox_embed" in name:
lowerCAmelCase_ = name.replace('''bbox_embed''' , '''bbox_predictor''' )
if "vit.norm" in name:
lowerCAmelCase_ = name.replace('''vit.norm''' , '''vit.layernorm''' )
return name
def __UpperCamelCase ( _A , _A ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase_ = orig_state_dict.pop(_A )
if "qkv" in key:
lowerCAmelCase_ = key.split('''.''' )
lowerCAmelCase_ = int(key_split[2] )
lowerCAmelCase_ = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowerCAmelCase_ = val[:dim, :]
lowerCAmelCase_ = val[
dim : dim * 2, :
]
lowerCAmelCase_ = val[-dim:, :]
else:
lowerCAmelCase_ = val[:dim]
lowerCAmelCase_ = val[dim : dim * 2]
lowerCAmelCase_ = val[-dim:]
else:
lowerCAmelCase_ = val
return orig_state_dict
def __UpperCamelCase ( ):
lowerCAmelCase_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCAmelCase_ = Image.open(requests.get(_A , stream=_A ).raw )
return im
@torch.no_grad()
def __UpperCamelCase ( _A , _A , _A , _A = False ):
lowerCAmelCase_ = get_yolos_config(_A )
# load original state_dict
lowerCAmelCase_ = torch.load(_A , map_location='''cpu''' )['''model''']
# load 🤗 model
lowerCAmelCase_ = YolosForObjectDetection(_A )
model.eval()
lowerCAmelCase_ = convert_state_dict(_A , _A )
model.load_state_dict(_A )
# Check outputs on an image, prepared by YolosImageProcessor
lowerCAmelCase_ = 800 if yolos_name != '''yolos_ti''' else 512
lowerCAmelCase_ = YolosImageProcessor(format='''coco_detection''' , size=_A )
lowerCAmelCase_ = image_processor(images=prepare_img() , return_tensors='''pt''' )
lowerCAmelCase_ = model(**_A )
lowerCAmelCase_ , lowerCAmelCase_ = outputs.logits, outputs.pred_boxes
lowerCAmelCase_ , lowerCAmelCase_ = None, None
if yolos_name == "yolos_ti":
lowerCAmelCase_ = torch.tensor(
[[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
lowerCAmelCase_ = torch.tensor(
[[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
elif yolos_name == "yolos_s_200_pre":
lowerCAmelCase_ = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
lowerCAmelCase_ = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
elif yolos_name == "yolos_s_300_pre":
lowerCAmelCase_ = torch.tensor(
[[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
lowerCAmelCase_ = torch.tensor(
[[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
elif yolos_name == "yolos_s_dWr":
lowerCAmelCase_ = torch.tensor(
[[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
lowerCAmelCase_ = torch.tensor(
[[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
elif yolos_name == "yolos_base":
lowerCAmelCase_ = torch.tensor(
[[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
lowerCAmelCase_ = torch.tensor(
[[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
else:
raise ValueError(f"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , _A , atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , _A , atol=1E-4 )
Path(_A ).mkdir(exist_ok=_A )
print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_A )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_A )
if push_to_hub:
lowerCAmelCase_ = {
'''yolos_ti''': '''yolos-tiny''',
'''yolos_s_200_pre''': '''yolos-small''',
'''yolos_s_300_pre''': '''yolos-small-300''',
'''yolos_s_dWr''': '''yolos-small-dwr''',
'''yolos_base''': '''yolos-base''',
}
print('''Pushing to the hub...''' )
lowerCAmelCase_ = model_mapping[yolos_name]
image_processor.push_to_hub(_A , organization='''hustvl''' )
model.push_to_hub(_A , organization='''hustvl''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--yolos_name''',
default='''yolos_s_200_pre''',
type=str,
help=(
'''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
_A = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 278 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->tuple[int, int]:
"""simple docstring"""
if b == 0:
return (1, 0)
((a_) , (a_)) = extended_euclid(UpperCAmelCase , a % b )
a_ = a // b
return (y, x - k * y)
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
((a_) , (a_)) = extended_euclid(UpperCAmelCase , UpperCAmelCase )
a_ = na * na
a_ = ra * x * na + ra * y * na
return (n % m + m) % m
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
((a_) , (a_)) = extended_euclid(UpperCAmelCase , UpperCAmelCase )
if b < 0:
a_ = (b % n + n) % n
return b
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ , a_ = invert_modulo(UpperCAmelCase , UpperCAmelCase ), invert_modulo(UpperCAmelCase , UpperCAmelCase )
a_ = na * na
a_ = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='chinese_remainder_theorem', verbose=True)
testmod(name='chinese_remainder_theorem2', verbose=True)
testmod(name='invert_modulo', verbose=True)
testmod(name='extended_euclid', verbose=True) | 303 |
"""simple docstring"""
import math
UpperCamelCase_ = 10
UpperCamelCase_ = 7
UpperCamelCase_ = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( UpperCAmelCase = 20 ) ->str:
"""simple docstring"""
a_ = math.comb(UpperCAmelCase , UpperCAmelCase )
a_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCAmelCase )
a_ = NUM_COLOURS * (1 - missing_colour / total)
return F'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20)) | 303 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase : Union[str, Any] = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowercase : Union[str, Any] = None
def A_ ( ) -> Dict:
a__ : Dict = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' , metavar='data.json' , help='Input data JSON file.' )
parser.add_argument('pred_file' , metavar='pred.json' , help='Model predictions.' )
parser.add_argument(
'--out-file' , '-o' , metavar='eval.json' , help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' , '-n' , metavar='na_prob.json' , help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' , '-t' , type=A__ , default=1.0 , help='Predict "" if no-answer probability exceeds this (default = 1.0).' , )
parser.add_argument(
'--out-image-dir' , '-p' , metavar='out_images' , default=A__ , help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' , '-v' , action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def A_ ( A__ ) -> int:
a__ : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
a__ : Optional[int] = bool(qa['answers']['text'] )
return qid_to_has_ans
def A_ ( A__ ) -> List[Any]:
def remove_articles(A__ ):
return ARTICLES_REGEX.sub(' ' , A__ )
def white_space_fix(A__ ):
return " ".join(text.split() )
def remove_punc(A__ ):
a__ : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(A__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(A__ ) ) ) )
def A_ ( A__ ) -> Union[str, Any]:
if not s:
return []
return normalize_answer(A__ ).split()
def A_ ( A__ , A__ ) -> Optional[Any]:
return int(normalize_answer(A__ ) == normalize_answer(A__ ) )
def A_ ( A__ , A__ ) -> Any:
a__ : Tuple = get_tokens(A__ )
a__ : Optional[int] = get_tokens(A__ )
a__ : int = collections.Counter(A__ ) & collections.Counter(A__ )
a__ : Optional[Any] = sum(common.values() )
if len(A__ ) == 0 or len(A__ ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
a__ : int = 1.0 * num_same / len(A__ )
a__ : List[Any] = 1.0 * num_same / len(A__ )
a__ : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def A_ ( A__ , A__ ) -> Any:
a__ : Tuple = {}
a__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
a__ : Optional[int] = qa['id']
a__ : Any = [t for t in qa['answers']['text'] if normalize_answer(A__ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
a__ : List[str] = ['']
if qid not in preds:
print(F'Missing prediction for {qid}' )
continue
a__ : Union[str, Any] = preds[qid]
# Take max over all gold answers
a__ : Tuple = max(compute_exact(A__ , A__ ) for a in gold_answers )
a__ : List[Any] = max(compute_fa(A__ , A__ ) for a in gold_answers )
return exact_scores, fa_scores
def A_ ( A__ , A__ , A__ , A__ ) -> Tuple:
a__ : List[Any] = {}
for qid, s in scores.items():
a__ : Tuple = na_probs[qid] > na_prob_thresh
if pred_na:
a__ : str = float(not qid_to_has_ans[qid] )
else:
a__ : int = s
return new_scores
def A_ ( A__ , A__ , A__=None ) -> List[Any]:
if not qid_list:
a__ : str = len(A__ )
return collections.OrderedDict(
[
('exact', 1_00.0 * sum(exact_scores.values() ) / total),
('f1', 1_00.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
a__ : int = len(A__ )
return collections.OrderedDict(
[
('exact', 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def A_ ( A__ , A__ , A__ ) -> Optional[int]:
for k in new_eval:
a__ : Optional[int] = new_eval[k]
def A_ ( A__ , A__ , A__ , A__ ) -> Union[str, Any]:
plt.step(A__ , A__ , color='b' , alpha=0.2 , where='post' )
plt.fill_between(A__ , A__ , step='post' , alpha=0.2 , color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(A__ )
plt.savefig(A__ )
plt.clf()
def A_ ( A__ , A__ , A__ , A__ , A__=None , A__=None ) -> Any:
a__ : str = sorted(A__ , key=lambda A__ : na_probs[k] )
a__ : Tuple = 0.0
a__ : List[str] = 1.0
a__ : Optional[int] = 0.0
a__ : Any = [1.0]
a__ : Optional[int] = [0.0]
a__ : Tuple = 0.0
for i, qid in enumerate(A__ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
a__ : Union[str, Any] = true_pos / float(i + 1 )
a__ : List[Any] = true_pos / float(A__ )
if i == len(A__ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(A__ )
recalls.append(A__ )
if out_image:
plot_pr_curve(A__ , A__ , A__ , A__ )
return {"ap": 1_00.0 * avg_prec}
def A_ ( A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
if out_image_dir and not os.path.exists(A__ ):
os.makedirs(A__ )
a__ : List[str] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
a__ : Optional[int] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , 'pr_exact.png' ) , title='Precision-Recall curve for Exact Match score' , )
a__ : Optional[int] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , 'pr_f1.png' ) , title='Precision-Recall curve for F1 score' , )
a__ : int = {k: float(A__ ) for k, v in qid_to_has_ans.items()}
a__ : Optional[Any] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , 'pr_oracle.png' ) , title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' , )
merge_eval(A__ , A__ , 'pr_exact' )
merge_eval(A__ , A__ , 'pr_f1' )
merge_eval(A__ , A__ , 'pr_oracle' )
def A_ ( A__ , A__ , A__ , A__ ) -> List[Any]:
if not qid_list:
return
a__ : List[str] = [na_probs[k] for k in qid_list]
a__ : Dict = np.ones_like(A__ ) / float(len(A__ ) )
plt.hist(A__ , weights=A__ , bins=20 , range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(F'Histogram of no-answer probability: {name}' )
plt.savefig(os.path.join(A__ , F'na_prob_hist_{name}.png' ) )
plt.clf()
def A_ ( A__ , A__ , A__ , A__ ) -> Optional[int]:
a__ : Any = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
a__ : List[str] = num_no_ans
a__ : Tuple = cur_score
a__ : Tuple = 0.0
a__ : str = sorted(A__ , key=lambda A__ : na_probs[k] )
for i, qid in enumerate(A__ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
a__ : str = scores[qid]
else:
if preds[qid]:
a__ : int = -1
else:
a__ : Tuple = 0
cur_score += diff
if cur_score > best_score:
a__ : Dict = cur_score
a__ : Tuple = na_probs[qid]
return 1_00.0 * best_score / len(A__ ), best_thresh
def A_ ( A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
a__ , a__ : str = find_best_thresh(A__ , A__ , A__ , A__ )
a__ , a__ : Tuple = find_best_thresh(A__ , A__ , A__ , A__ )
a__ : Optional[Any] = best_exact
a__ : Optional[Any] = exact_thresh
a__ : Tuple = best_fa
a__ : int = fa_thresh
def A_ ( ) -> Union[str, Any]:
with open(OPTS.data_file ) as f:
a__ : Optional[int] = json.load(A__ )
a__ : Any = dataset_json['data']
with open(OPTS.pred_file ) as f:
a__ : Optional[Any] = json.load(A__ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
a__ : int = json.load(A__ )
else:
a__ : Union[str, Any] = {k: 0.0 for k in preds}
a__ : str = make_qid_to_has_ans(A__ ) # maps qid to True/False
a__ : int = [k for k, v in qid_to_has_ans.items() if v]
a__ : Tuple = [k for k, v in qid_to_has_ans.items() if not v]
a__ , a__ : str = get_raw_scores(A__ , A__ )
a__ : str = apply_no_ans_threshold(A__ , A__ , A__ , OPTS.na_prob_thresh )
a__ : Union[str, Any] = apply_no_ans_threshold(A__ , A__ , A__ , OPTS.na_prob_thresh )
a__ : List[Any] = make_eval_dict(A__ , A__ )
if has_ans_qids:
a__ : str = make_eval_dict(A__ , A__ , qid_list=A__ )
merge_eval(A__ , A__ , 'HasAns' )
if no_ans_qids:
a__ : int = make_eval_dict(A__ , A__ , qid_list=A__ )
merge_eval(A__ , A__ , 'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(A__ , A__ , A__ , A__ , A__ , A__ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(A__ , A__ , A__ , A__ , A__ , OPTS.out_image_dir )
histogram_na_prob(A__ , A__ , OPTS.out_image_dir , 'hasAns' )
histogram_na_prob(A__ , A__ , OPTS.out_image_dir , 'noAns' )
if OPTS.out_file:
with open(OPTS.out_file , 'w' ) as f:
json.dump(A__ , A__ )
else:
print(json.dumps(A__ , indent=2 ) )
if __name__ == "__main__":
lowercase : List[Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("""Agg""")
import matplotlib.pyplot as plt
main()
| 99 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
lowercase__ : Dict = logging.get_logger(__name__)
@add_end_docstrings(
UpperCamelCase_ , r"""
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
""" , )
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->np.ndarray:
if self.framework == "tf":
lowerCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
lowerCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=__SCREAMING_SNAKE_CASE )
else:
raise ValueError('''Unsupported framework''' )
return masked_index
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->np.ndarray:
lowerCAmelCase = self.get_masked_index(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , F"No mask_token ({self.tokenizer.mask_token}) found on the input" , )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->str:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , **__SCREAMING_SNAKE_CASE ) ->Dict[str, GenericTensor]:
if return_tensors is None:
lowerCAmelCase = self.framework
lowerCAmelCase = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE )
self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->Tuple:
lowerCAmelCase = self.model(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = model_inputs['''input_ids''']
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=None ) ->str:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
lowerCAmelCase = target_ids.shape[0]
lowerCAmelCase = model_outputs['''input_ids'''][0]
lowerCAmelCase = model_outputs['''logits''']
if self.framework == "tf":
lowerCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
lowerCAmelCase = outputs.numpy()
lowerCAmelCase = outputs[0, masked_index, :]
lowerCAmelCase = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
if target_ids is not None:
lowerCAmelCase = tf.gather_nd(tf.squeeze(__SCREAMING_SNAKE_CASE , 0 ) , target_ids.reshape(-1 , 1 ) )
lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE , 0 )
lowerCAmelCase = tf.math.top_k(__SCREAMING_SNAKE_CASE , k=__SCREAMING_SNAKE_CASE )
lowerCAmelCase , lowerCAmelCase = topk.values.numpy(), topk.indices.numpy()
else:
lowerCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=__SCREAMING_SNAKE_CASE ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
lowerCAmelCase = outputs[0, masked_index, :]
lowerCAmelCase = logits.softmax(dim=-1 )
if target_ids is not None:
lowerCAmelCase = probs[..., target_ids]
lowerCAmelCase , lowerCAmelCase = probs.topk(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = []
lowerCAmelCase = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
lowerCAmelCase = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
lowerCAmelCase = input_ids.numpy().copy()
if target_ids is not None:
lowerCAmelCase = target_ids[p].tolist()
lowerCAmelCase = p
# Filter padding out:
lowerCAmelCase = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
lowerCAmelCase = self.tokenizer.decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
row.append(__SCREAMING_SNAKE_CASE )
result.append(__SCREAMING_SNAKE_CASE )
if single_mask:
return result[0]
return result
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None ) ->Optional[Any]:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCAmelCase = [targets]
try:
lowerCAmelCase = self.tokenizer.get_vocab()
except Exception:
lowerCAmelCase = {}
lowerCAmelCase = []
for target in targets:
lowerCAmelCase = vocab.get(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if id_ is None:
lowerCAmelCase = self.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE , max_length=1 , truncation=__SCREAMING_SNAKE_CASE , )['''input_ids''']
if len(__SCREAMING_SNAKE_CASE ) == 0:
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
'''We cannot replace it with anything meaningful, ignoring it''' )
continue
lowerCAmelCase = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." )
target_ids.append(id_ )
lowerCAmelCase = list(set(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('''At least one target must be provided when passed.''' )
lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
return target_ids
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->Dict:
lowerCAmelCase = {}
if targets is not None:
lowerCAmelCase = self.get_target_ids(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCAmelCase = target_ids
if top_k is not None:
lowerCAmelCase = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , '''The tokenizer does not define a `mask_token`.''' )
return {}, {}, postprocess_params
def __call__( self , __SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) ->List[Any]:
lowerCAmelCase = super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) == 1:
return outputs[0]
return outputs
| 338 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE : int = get_tests_dir("fixtures/test_sentencepiece.model")
if is_sentencepiece_available():
import sentencepiece as sp
SCREAMING_SNAKE_CASE : Dict = 5
SCREAMING_SNAKE_CASE : int = 10
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase( snake_case_, unittest.TestCase ):
"""simple docstring"""
lowercase_ : Any = SpeechaTextTokenizer
lowercase_ : int = False
lowercase_ : Any = True
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
super().setUp()
_lowercase : Optional[int] = sp.SentencePieceProcessor()
spm_model.Load(lowerCamelCase)
_lowercase : Dict = ['<s>', '<pad>', '</s>', '<unk>']
vocab += [spm_model.IdToPiece(id_) for id_ in range(len(lowerCamelCase))]
_lowercase : Any = dict(zip(lowerCamelCase, range(len(lowerCamelCase))))
_lowercase : str = Path(self.tmpdirname)
save_json(lowerCamelCase, save_dir / VOCAB_FILES_NAMES['vocab_file'])
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCamelCase, save_dir / VOCAB_FILES_NAMES['spm_file'])
_lowercase : Dict = SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : str = '<pad>'
_lowercase : str = 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) -> Any:
"""simple docstring"""
_lowercase : Optional[Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], '<s>')
self.assertEqual(vocab_keys[1], '<pad>')
self.assertEqual(vocab_keys[-1], 'j')
self.assertEqual(len(lowerCamelCase), 10_01)
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 10_01)
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : Any = SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
_lowercase : Any = tokenizer.tokenize('This is a test')
self.assertListEqual(lowerCamelCase, ['▁This', '▁is', '▁a', '▁t', 'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase), [2_89, 50, 14, 1_74, 3_86], )
_lowercase : str = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
lowerCamelCase, [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 : Any = tokenizer.convert_tokens_to_ids(lowerCamelCase)
self.assertListEqual(lowerCamelCase, [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8])
_lowercase : Optional[int] = tokenizer.convert_ids_to_tokens(lowerCamelCase)
self.assertListEqual(
lowerCamelCase, [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>', '.'], )
@slow
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Optional[int] = {'input_ids': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='facebook/s2t-small-mustc-en-de-st', revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad', )
@require_sentencepiece
class _lowerCamelCase( unittest.TestCase ):
"""simple docstring"""
lowercase_ : Optional[Any] = """valhalla/s2t_mustc_multilinguial_medium"""
lowercase_ : Dict = """C\'est trop cool"""
lowercase_ : str = """Esto es genial"""
@classmethod
def UpperCamelCase ( cls) -> Optional[Any]:
"""simple docstring"""
_lowercase : Optional[int] = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
return cls
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['pt'], 4)
self.assertEqual(self.tokenizer.lang_code_to_id['ru'], 6)
self.assertEqual(self.tokenizer.lang_code_to_id['it'], 9)
self.assertEqual(self.tokenizer.lang_code_to_id['de'], 11)
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 1_00_00)
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
self.assertIn(lowerCamelCase, self.tokenizer.all_special_ids)
_lowercase : int = [ES_CODE, 4, 16_01, 47, 76_47, 2]
_lowercase : List[Any] = self.tokenizer.decode(lowerCamelCase, skip_special_tokens=lowerCamelCase)
_lowercase : Dict = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCamelCase)
self.assertEqual(lowerCamelCase, lowerCamelCase)
self.assertNotIn(self.tokenizer.eos_token, lowerCamelCase)
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : List[str] = 'fr'
_lowercase : Dict = self.tokenizer(self.french_text).input_ids
self.assertEqual(encoded[0], lowerCamelCase)
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id)
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : str = 'fr'
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE])
_lowercase : int = 'es'
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE])
| 353 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Union[str, Any] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
_lowercase : Any = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase ={
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 334 | 1 |
import torch
from transformers import AutoModel
class __magic_name__ ( torch.nn.Module ):
def __init__( self , __snake_case="sayef/fsner-bert-base-uncased" ) -> Optional[int]:
'''simple docstring'''
super(__snake_case , self ).__init__()
__a =AutoModel.from_pretrained(__snake_case , return_dict=__snake_case )
__a =torch.nn.CosineSimilarity(3 , 1e-08 )
__a =torch.nn.Softmax(dim=1 )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
return self.bert(**__snake_case ).last_hidden_state
def __magic_name__ ( self , __snake_case ) -> Any:
'''simple docstring'''
return token_embeddings.sum(2 , keepdim=__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
return self.softmax(T * self.cos(__snake_case , __snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =W_supports['sizes'].tolist()
__a =W_supports['start_token_id'].item()
__a =W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
__a =self.BERT(**__snake_case )
__a =self.BERT(**__snake_case )
__a =None
__a =None
__a =W_supports['input_ids'] == start_token_id
__a =W_supports['input_ids'] == end_token_id
for i, size in enumerate(__snake_case ):
if i == 0:
__a =0
else:
__a =support_sizes[i - 1]
__a =S[s : s + size][start_token_masks[s : s + size]]
__a =S[s : s + size][end_token_masks[s : s + size]]
__a =torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
__a =torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
__a =torch.vstack((p_starts, p_start) )
__a =torch.vstack((p_ends, p_end) )
else:
__a =p_start
__a =p_end
return p_starts, p_ends
| 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308 | 0 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__lowerCamelCase : Optional[int] = '''src/transformers'''
__lowerCamelCase : List[str] = '''docs/source/en/tasks'''
def _snake_case ( lowerCAmelCase : Tuple , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Optional[int] ):
"""simple docstring"""
with open(lowerCAmelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
SCREAMING_SNAKE_CASE_ : Optional[Any] = f.readlines()
# Find the start prompt.
SCREAMING_SNAKE_CASE_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase ):
start_index += 1
start_index += 1
SCREAMING_SNAKE_CASE_ : Any = start_index
while not lines[end_index].startswith(lowerCAmelCase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase : Dict = direct_transformers_import(TRANSFORMERS_PATH)
__lowerCamelCase : Union[str, Any] = {
'''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__lowerCamelCase : List[Any] = {
'''summarization.md''': ('''nllb''',),
'''translation.md''': ('''nllb''',),
}
def _snake_case ( lowerCAmelCase : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = TASK_GUIDE_TO_MODELS[task_guide]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase , set() )
SCREAMING_SNAKE_CASE_ : Tuple = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f'[{name}](../model_doc/{code})' for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] = _find_text_in_file(
filename=os.path.join(lowerCAmelCase , lowerCAmelCase ) , start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->" , end_prompt="<!--End of the generated tip-->" , )
SCREAMING_SNAKE_CASE_ : List[Any] = get_model_list_for_task(lowerCAmelCase )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowerCAmelCase , lowerCAmelCase ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
" to fix this." )
if __name__ == "__main__":
__lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__lowerCamelCase : str = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PegasusXForConditionalGeneration''',
'''PegasusXModel''',
'''PegasusXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 254 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
A_ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 316 |
"""simple docstring"""
import re
def lowerCamelCase_ ( _lowerCamelCase ):
if len(re.findall('[ATCG]' , _lowerCamelCase ) ) != len(_lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316 | 1 |
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