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import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Dict=13 , lowerCAmelCase__ : int=7 , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : List[Any]=True , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : Dict=99 , lowerCAmelCase__ : List[Any]=32 , lowerCAmelCase__ : Dict=5 , lowerCAmelCase__ : Union[str, Any]=4 , lowerCAmelCase__ : List[str]=37 , lowerCAmelCase__ : str="gelu" , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : Union[str, Any]=0.1 , lowerCAmelCase__ : Tuple=512 , lowerCAmelCase__ : Dict=16 , lowerCAmelCase__ : List[Any]=2 , lowerCAmelCase__ : Dict=0.02 , lowerCAmelCase__ : List[str]=4 , ):
SCREAMING_SNAKE_CASE_: Optional[int] = parent
SCREAMING_SNAKE_CASE_: Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE_: Tuple = seq_length
SCREAMING_SNAKE_CASE_: Optional[Any] = is_training
SCREAMING_SNAKE_CASE_: int = use_attention_mask
SCREAMING_SNAKE_CASE_: Optional[int] = use_token_type_ids
SCREAMING_SNAKE_CASE_: Tuple = use_labels
SCREAMING_SNAKE_CASE_: List[Any] = vocab_size
SCREAMING_SNAKE_CASE_: Any = hidden_size
SCREAMING_SNAKE_CASE_: str = num_hidden_layers
SCREAMING_SNAKE_CASE_: Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE_: List[str] = intermediate_size
SCREAMING_SNAKE_CASE_: Dict = hidden_act
SCREAMING_SNAKE_CASE_: Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_: List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_: Any = max_position_embeddings
SCREAMING_SNAKE_CASE_: str = type_vocab_size
SCREAMING_SNAKE_CASE_: Optional[int] = type_sequence_label_size
SCREAMING_SNAKE_CASE_: List[str] = initializer_range
SCREAMING_SNAKE_CASE_: Dict = num_choices
def _SCREAMING_SNAKE_CASE ( self : int):
SCREAMING_SNAKE_CASE_: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
SCREAMING_SNAKE_CASE_: Dict = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE_: str = random_attention_mask([self.batch_size, self.seq_length])
SCREAMING_SNAKE_CASE_: Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE_: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
SCREAMING_SNAKE_CASE_: int = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: List[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE_: Any = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase : List[Any] = True
_UpperCAmelCase : str = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Tuple = FlaxRoFormerModelTester(self)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE_: Optional[int] = model_class_name.from_pretrained("junnyu/roformer_chinese_small" , from_pt=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = model(np.ones((1, 1)))
self.assertIsNotNone(lowerCAmelCase__)
@require_flax
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: List[str] = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
SCREAMING_SNAKE_CASE_: Union[str, Any] = jnp.array([[0, 1, 2, 3, 4, 5]])
SCREAMING_SNAKE_CASE_: Optional[int] = model(lowerCAmelCase__)[0]
SCREAMING_SNAKE_CASE_: str = 5_0000
SCREAMING_SNAKE_CASE_: Optional[int] = (1, 6, vocab_size)
self.assertEqual(output.shape , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , lowerCAmelCase__ , atol=1E-4))
| 13
|
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84
| 0
|
"""simple docstring"""
from math import ceil
def lowercase__( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple ):
lowercase_ : List[Any] = list(range(0 , __SCREAMING_SNAKE_CASE ) )
lowercase_ : List[Any] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
lowercase_ : Optional[int] = []
for i in device_map_blocks:
if device_map_blocks.count(__SCREAMING_SNAKE_CASE ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__SCREAMING_SNAKE_CASE )
# Missing blocks
lowercase_ : List[Any] = [i for i in blocks if i not in device_map_blocks]
lowercase_ : Optional[int] = [i for i in device_map_blocks if i not in blocks]
if len(__SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
' These attention blocks were specified more than once: ' + str(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
'There are attention blocks for this model that are not specified in the device_map. Add these attention '
'blocks to a device on the device_map: ' + str(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
+ str(__SCREAMING_SNAKE_CASE ) )
def lowercase__( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ):
lowercase_ : Optional[Any] = list(range(__SCREAMING_SNAKE_CASE ) )
lowercase_ : List[str] = int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE ) ) )
lowercase_ : Optional[Any] = [layers[i : i + n_blocks] for i in range(0 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )]
return dict(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
| 351
|
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCamelCase ( unittest.TestCase ):
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[str]:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] ,model_result['ss'] ):
lowercase_ : Dict = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__UpperCamelCase )
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
lowercase_ : int = 'sshleifer/tiny-gpt2'
lowercase_ : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,eager_mode=__UpperCamelCase ,multi_process=__UpperCamelCase ,)
lowercase_ : Union[str, Any] = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : List[str] = 'sgugger/tiny-distilbert-classification'
lowercase_ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,only_pretrain_model=__UpperCamelCase ,)
lowercase_ : int = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ : Any = 'sshleifer/tiny-gpt2'
lowercase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,)
lowercase_ : Optional[Any] = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ : Dict = 'sshleifer/tiny-gpt2'
lowercase_ : Tuple = AutoConfig.from_pretrained(__UpperCamelCase )
lowercase_ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,eager_mode=__UpperCamelCase ,multi_process=__UpperCamelCase ,)
lowercase_ : str = TensorFlowBenchmark(__UpperCamelCase ,[config] )
lowercase_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Any = 'sshleifer/tiny-gpt2'
lowercase_ : Any = AutoConfig.from_pretrained(__UpperCamelCase )
lowercase_ : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,)
lowercase_ : int = TensorFlowBenchmark(__UpperCamelCase ,[config] )
lowercase_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : int = 'sshleifer/tiny-gpt2'
lowercase_ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,)
lowercase_ : List[str] = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowercase_ : List[str] = 'sshleifer/tiny-gpt2'
lowercase_ : Optional[int] = AutoConfig.from_pretrained(__UpperCamelCase )
lowercase_ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,)
lowercase_ : str = TensorFlowBenchmark(__UpperCamelCase ,[config] )
lowercase_ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
lowercase_ : str = 'patrickvonplaten/t5-tiny-random'
lowercase_ : int = AutoConfig.from_pretrained(__UpperCamelCase )
lowercase_ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__UpperCamelCase ,)
lowercase_ : List[str] = TensorFlowBenchmark(__UpperCamelCase ,configs=[config] )
lowercase_ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 ,'Cannot do xla on CPU.' )
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase_ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=__UpperCamelCase ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,use_xla=__UpperCamelCase ,multi_process=__UpperCamelCase ,)
lowercase_ : Union[str, Any] = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowercase_ : List[str] = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,inference=__UpperCamelCase ,save_to_csv=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,inference_time_csv_file=os.path.join(__UpperCamelCase ,'inf_time.csv' ) ,inference_memory_csv_file=os.path.join(__UpperCamelCase ,'inf_mem.csv' ) ,env_info_csv_file=os.path.join(__UpperCamelCase ,'env.csv' ) ,multi_process=__UpperCamelCase ,)
lowercase_ : List[str] = TensorFlowBenchmark(__UpperCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(__UpperCamelCase ,'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase ,'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase ,'env.csv' ) ).exists() )
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
lowercase_ : int = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__UpperCamelCase ):
self.assertTrue(hasattr(__UpperCamelCase ,'sequential' ) )
self.assertTrue(hasattr(__UpperCamelCase ,'cumulative' ) )
self.assertTrue(hasattr(__UpperCamelCase ,'current' ) )
self.assertTrue(hasattr(__UpperCamelCase ,'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,inference=__UpperCamelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,log_filename=os.path.join(__UpperCamelCase ,'log.txt' ) ,log_print=__UpperCamelCase ,trace_memory_line_by_line=__UpperCamelCase ,eager_mode=__UpperCamelCase ,multi_process=__UpperCamelCase ,)
lowercase_ : Dict = TensorFlowBenchmark(__UpperCamelCase )
lowercase_ : Any = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(__UpperCamelCase ,'log.txt' ) ).exists() )
| 321
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 100, ):
UpperCAmelCase_ : List[str] = x_start
UpperCAmelCase_ : List[str] = fnc(snake_case_ )
UpperCAmelCase_ : Tuple = 0.0
for _ in range(snake_case_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
UpperCAmelCase_ : List[Any] = (x_end - x_start) / steps + xa
UpperCAmelCase_ : Any = fnc(snake_case_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
UpperCAmelCase_ : Optional[Any] = xa
UpperCAmelCase_ : Any = fxa
return area
if __name__ == "__main__":
def __a ( __lowerCamelCase ):
return x**3 + x**2
print('f(x) = x^3 + x^2')
print('The area between the curve, x = -5, x = 5 and the x axis is:')
_a = 10
while i <= 100_000:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 10
| 61
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_snake_case = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
_snake_case = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
_snake_case = "▁"
class lowercase ( UpperCamelCase__ ):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = ["input_ids", "token_type_ids"]
_a = FNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=True , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , **_a , ) -> Optional[int]:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_A : int = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
super().__init__(
_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , **_a , )
_A : Optional[int] = do_lower_case
_A : List[Any] = remove_space
_A : str = keep_accents
_A : int = vocab_file
_A : int = False if not self.vocab_file else True
def a__ ( self , _a , _a = None ) -> List[int]:
_A : str = [self.sep_token_id]
_A : 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 a__ ( self , _a , _a = None ) -> List[int]:
_A : Any = [self.sep_token_id]
_A : 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 a__ ( self , _a , _a = None ) -> Tuple[str]:
if not os.path.isdir(_a ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A : List[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 ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 26
| 0
|
'''simple docstring'''
import os
import sys
import unittest
UpperCamelCase : str = 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,
)
UpperCamelCase : Optional[int] = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
UpperCamelCase : Dict = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self : List[str]):
"""simple docstring"""
a : str = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_)
a : Union[str, Any] = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_)
a : Union[str, Any] = {"""BertModelTest""": """BertModelTester"""}
a : str = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
def SCREAMING_SNAKE_CASE_ ( self : List[str]):
"""simple docstring"""
a : List[str] = get_model_to_test_mapping(SCREAMING_SNAKE_CASE_)
a : List[str] = get_model_to_test_mapping(SCREAMING_SNAKE_CASE_)
a : Union[str, Any] = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
a : int = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
def SCREAMING_SNAKE_CASE_ ( self : int):
"""simple docstring"""
a : Any = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_)
a : Optional[int] = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_)
a : Optional[int] = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
a : Union[str, Any] = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_)
| 355
|
'''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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase ( a_ ):
"""simple docstring"""
A : List[str] = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
A : List[Any] = "CIDAS/clipseg-rd64-refined"
A : Optional[Any] = "image_segmenter"
A : List[Any] = CLIPSegForImageSegmentation
A : Tuple = ["image", "text"]
A : Optional[int] = ["image"]
def __init__( self : str , *UpperCAmelCase_ : str , **UpperCAmelCase_ : str):
"""simple docstring"""
requires_backends(self , ['vision'])
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCAmelCase_ : "Image" , UpperCAmelCase_ : str):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=UpperCAmelCase_ , return_tensors='pt')
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCAmelCase_ : str):
"""simple docstring"""
with torch.no_grad():
a : Union[str, Any] = self.model(**UpperCAmelCase_).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , UpperCAmelCase_ : int):
"""simple docstring"""
a : int = outputs.cpu().detach().numpy()
a : str = 0
a : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta))
| 345
| 0
|
import fire
from utils import calculate_rouge, save_json
def _SCREAMING_SNAKE_CASE ( a , a , a=None , **a ) -> Dict:
__A : Optional[int] = [x.strip() for x in open(a ).readlines()]
__A : str = [x.strip() for x in open(a ).readlines()][: len(a )]
__A : Tuple = calculate_rouge(a , a , **a )
if save_path is not None:
save_json(a , a , indent=a )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 280
|
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ) -> List[Any]:
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=a , default='biencoder-nq-dev.json' , help='Path to raw DPR training data' , )
parser.add_argument(
'--evaluation_set' , type=a , help='where to store parsed evaluation_set file' , )
parser.add_argument(
'--gold_data_path' , type=a , help='where to store parsed gold_data_path file' , )
__A : Optional[int] = parser.parse_args()
with open(args.src_path , 'r' ) as src_file, open(args.evaluation_set , 'w' ) as eval_file, open(
args.gold_data_path , 'w' ) as gold_file:
__A : List[Any] = json.load(a )
for dpr_record in tqdm(a ):
__A : Dict = dpr_record['question']
__A : Any = [context['title'] for context in dpr_record['positive_ctxs']]
eval_file.write(question + '\n' )
gold_file.write('\t'.join(a ) + '\n' )
if __name__ == "__main__":
main()
| 280
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase_ = 'canine'
def __init__( self : Union[str, Any] , lowercase__ : List[str]=768 , lowercase__ : Any=12 , lowercase__ : Dict=12 , lowercase__ : Optional[Any]=3_072 , lowercase__ : List[Any]="gelu" , lowercase__ : Optional[Any]=0.1 , lowercase__ : Optional[Any]=0.1 , lowercase__ : Dict=16_384 , lowercase__ : List[str]=16 , lowercase__ : Union[str, Any]=0.02 , lowercase__ : Dict=1e-12 , lowercase__ : int=0 , lowercase__ : str=0xE000 , lowercase__ : Optional[Any]=0xE001 , lowercase__ : Any=4 , lowercase__ : Union[str, Any]=4 , lowercase__ : Optional[int]=8 , lowercase__ : Dict=16_384 , lowercase__ : Union[str, Any]=128 , **lowercase__ : Tuple , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__)
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = layer_norm_eps
# Character config:
lowerCAmelCase__ = downsampling_rate
lowerCAmelCase__ = upsampling_kernel_size
lowerCAmelCase__ = num_hash_functions
lowerCAmelCase__ = num_hash_buckets
lowerCAmelCase__ = local_transformer_stride
| 119
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
'processing_x_clip': ['XCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'XCLIPModel',
'XCLIPPreTrainedModel',
'XCLIPTextModel',
'XCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 119
| 1
|
from maths.prime_check import is_prime
def UpperCamelCase( __UpperCamelCase : int ):
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCAmelCase_ : Union[str, Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__UpperCamelCase )
if is_prime(__UpperCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return [ord(SCREAMING_SNAKE_CASE ) - 96 for elem in plain]
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def UpperCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase = encode(input("""-> """ ).strip().lower() )
print("""Encoded: """ , SCREAMING_SNAKE_CASE )
print("""Decoded:""" , decode(SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
main()
| 46
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : List[Any] = ["image_processor", "tokenizer"]
lowercase__ : Tuple = "BlipImageProcessor"
lowercase__ : List[Any] = "AutoTokenizer"
def __init__( self , lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
lowerCAmelCase__ = False
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = self.image_processor
def __call__( self , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = True , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = 0 , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = False , lowerCamelCase_ = False , lowerCamelCase_ = False , lowerCamelCase_ = False , lowerCamelCase_ = False , lowerCamelCase_ = True , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> BatchEncoding:
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:
lowerCAmelCase__ = self.tokenizer
lowerCAmelCase__ = 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
# add pixel_values
lowerCAmelCase__ = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ )
if text is not None:
lowerCAmelCase__ = 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_ , )
else:
lowerCAmelCase__ = None
if text_encoding is not None:
encoding_image_processor.update(lowerCamelCase_ )
return encoding_image_processor
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> Any:
return self.tokenizer.batch_decode(*lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> Any:
return self.tokenizer.decode(*lowerCamelCase_ , **lowerCamelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
lowerCAmelCase__ = self.tokenizer.model_input_names
lowerCAmelCase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 228
|
'''simple docstring'''
import argparse
import json
import subprocess
def _snake_case ( A , A ) -> Tuple:
lowerCAmelCase__ = []
lowerCAmelCase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
)
lowerCAmelCase__ = subprocess.run(A , shell=A , stdout=subprocess.PIPE )
lowerCAmelCase__ = output.stdout.decode('''utf-8''' )
lowerCAmelCase__ = json.loads(A )
lowerCAmelCase__ = status['''runners''']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(A )
# save the result so we can report them on Slack
with open('''offline_runners.txt''' , '''w''' ) as fp:
fp.write(json.dumps(A ) )
if len(A ) > 0:
lowerCAmelCase__ = '''\n'''.join([x['''name'''] for x in offline_runners] )
raise ValueError(F"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def _snake_case ( A ) -> Optional[Any]:
return values.split(''',''' )
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
__UpperCAmelCase = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 228
| 1
|
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = get_activation("""swish""" )
self.assertIsInstance(UpperCamelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = get_activation("""silu""" )
self.assertIsInstance(UpperCamelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = get_activation("""mish""" )
self.assertIsInstance(UpperCamelCase , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = get_activation("""gelu""" )
self.assertIsInstance(UpperCamelCase , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 335
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Optional[int]=7 , UpperCamelCase: str=3 , UpperCamelCase: int=30 , UpperCamelCase: int=4_00 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Tuple=None , UpperCamelCase: Any=True , UpperCamelCase: int=[0.5, 0.5, 0.5] , UpperCamelCase: Any=[0.5, 0.5, 0.5] , UpperCamelCase: Optional[Any]=True , UpperCamelCase: List[Any]=1 / 2_55 , UpperCamelCase: Tuple=True , ):
"""simple docstring"""
A__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase ( self: Any , UpperCamelCase: List[str] , UpperCamelCase: int=False ):
"""simple docstring"""
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCamelCase , Image.Image ):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size["""shortest_edge"""] * h / w )
A__ = self.size["""shortest_edge"""]
elif w > h:
A__ = self.size["""shortest_edge"""]
A__ = int(self.size["""shortest_edge"""] * w / h )
else:
A__ = self.size["""shortest_edge"""]
A__ = self.size["""shortest_edge"""]
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[0] )[0]
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = YolosImageProcessingTester(self )
@property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """size""" ) )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = 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
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = 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
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
A__ = self.image_processing_class(do_resize=UpperCamelCase , do_normalize=UpperCamelCase , do_rescale=UpperCamelCase )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
A__ = image_processing_a.pad(UpperCamelCase , return_tensors="""pt""" )
A__ = image_processing_a(UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
A__ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
@slow
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
A__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
A__ = YolosImageProcessor(format="""coco_panoptic""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , masks_path=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify masks
A__ = 82_28_73
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , UpperCamelCase )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
| 335
| 1
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__lowercase = logging.get_logger(__name__)
# General docstring
__lowercase = '''RegNetConfig'''
# Base docstring
__lowercase = '''facebook/regnet-y-040'''
__lowercase = [1, 1088, 7, 7]
# Image classification docstring
__lowercase = '''facebook/regnet-y-040'''
__lowercase = '''tabby, tabby cat'''
__lowercase = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase = 3 , __lowercase = 1 , __lowercase = 1 , __lowercase = "relu" , ) -> Dict:
super().__init__()
__UpperCamelCase :Optional[int] = nn.Convad(
__lowercase , __lowercase , kernel_size=__lowercase , stride=__lowercase , padding=kernel_size // 2 , groups=__lowercase , bias=__lowercase , )
__UpperCamelCase :Tuple = nn.BatchNormad(__lowercase)
__UpperCamelCase :Any = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCamelCase__ ( self , __lowercase) -> Optional[Any]:
__UpperCamelCase :str = self.convolution(__lowercase)
__UpperCamelCase :Optional[Any] = self.normalization(__lowercase)
__UpperCamelCase :List[str] = self.activation(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase) -> Any:
super().__init__()
__UpperCamelCase :Any = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act)
__UpperCamelCase :Optional[int] = config.num_channels
def UpperCamelCase__ ( self , __lowercase) -> Union[str, Any]:
__UpperCamelCase :str = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''')
__UpperCamelCase :Any = self.embedder(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase = 2) -> Optional[Any]:
super().__init__()
__UpperCamelCase :Dict = nn.Convad(__lowercase , __lowercase , kernel_size=1 , stride=__lowercase , bias=__lowercase)
__UpperCamelCase :str = nn.BatchNormad(__lowercase)
def UpperCamelCase__ ( self , __lowercase) -> Tensor:
__UpperCamelCase :Any = self.convolution(__lowercase)
__UpperCamelCase :Optional[Any] = self.normalization(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase) -> int:
super().__init__()
__UpperCamelCase :List[str] = nn.AdaptiveAvgPoolad((1, 1))
__UpperCamelCase :Union[str, Any] = nn.Sequential(
nn.Convad(__lowercase , __lowercase , kernel_size=1) , nn.ReLU() , nn.Convad(__lowercase , __lowercase , kernel_size=1) , nn.Sigmoid() , )
def UpperCamelCase__ ( self , __lowercase) -> Any:
# b c h w -> b c 1 1
__UpperCamelCase :str = self.pooler(__lowercase)
__UpperCamelCase :List[Any] = self.attention(__lowercase)
__UpperCamelCase :Union[str, Any] = hidden_state * attention
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase = 1) -> List[str]:
super().__init__()
__UpperCamelCase :Tuple = in_channels != out_channels or stride != 1
__UpperCamelCase :Optional[int] = max(1 , out_channels // config.groups_width)
__UpperCamelCase :Optional[int] = (
RegNetShortCut(__lowercase , __lowercase , stride=__lowercase) if should_apply_shortcut else nn.Identity()
)
__UpperCamelCase :Optional[int] = nn.Sequential(
RegNetConvLayer(__lowercase , __lowercase , kernel_size=1 , activation=config.hidden_act) , RegNetConvLayer(__lowercase , __lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act) , RegNetConvLayer(__lowercase , __lowercase , kernel_size=1 , activation=__lowercase) , )
__UpperCamelCase :Dict = ACTaFN[config.hidden_act]
def UpperCamelCase__ ( self , __lowercase) -> Union[str, Any]:
__UpperCamelCase :Dict = hidden_state
__UpperCamelCase :int = self.layer(__lowercase)
__UpperCamelCase :List[str] = self.shortcut(__lowercase)
hidden_state += residual
__UpperCamelCase :Any = self.activation(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase = 1) -> Tuple:
super().__init__()
__UpperCamelCase :List[Any] = in_channels != out_channels or stride != 1
__UpperCamelCase :Optional[Any] = max(1 , out_channels // config.groups_width)
__UpperCamelCase :List[Any] = (
RegNetShortCut(__lowercase , __lowercase , stride=__lowercase) if should_apply_shortcut else nn.Identity()
)
__UpperCamelCase :List[str] = nn.Sequential(
RegNetConvLayer(__lowercase , __lowercase , kernel_size=1 , activation=config.hidden_act) , RegNetConvLayer(__lowercase , __lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act) , RegNetSELayer(__lowercase , reduced_channels=int(round(in_channels / 4))) , RegNetConvLayer(__lowercase , __lowercase , kernel_size=1 , activation=__lowercase) , )
__UpperCamelCase :List[str] = ACTaFN[config.hidden_act]
def UpperCamelCase__ ( self , __lowercase) -> Any:
__UpperCamelCase :List[str] = hidden_state
__UpperCamelCase :str = self.layer(__lowercase)
__UpperCamelCase :Optional[int] = self.shortcut(__lowercase)
hidden_state += residual
__UpperCamelCase :Optional[int] = self.activation(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase = 2 , __lowercase = 2 , ) -> str:
super().__init__()
__UpperCamelCase :Tuple = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
__UpperCamelCase :Any = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
__lowercase , __lowercase , __lowercase , stride=__lowercase , ) , *[layer(__lowercase , __lowercase , __lowercase) for _ in range(depth - 1)] , )
def UpperCamelCase__ ( self , __lowercase) -> Dict:
__UpperCamelCase :str = self.layers(__lowercase)
return hidden_state
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase) -> Tuple:
super().__init__()
__UpperCamelCase :int = nn.ModuleList([])
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
__lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ))
__UpperCamelCase :List[str] = zip(config.hidden_sizes , config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(__lowercase , config.depths[1:]):
self.stages.append(RegNetStage(__lowercase , __lowercase , __lowercase , depth=__lowercase))
def UpperCamelCase__ ( self , __lowercase , __lowercase = False , __lowercase = True) -> BaseModelOutputWithNoAttention:
__UpperCamelCase :Tuple = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__UpperCamelCase :List[str] = hidden_states + (hidden_state,)
__UpperCamelCase :Optional[Any] = stage_module(__lowercase)
if output_hidden_states:
__UpperCamelCase :List[Any] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(last_hidden_state=__lowercase , hidden_states=__lowercase)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Optional[int] = RegNetConfig
a__ : Dict = """regnet"""
a__ : Any = """pixel_values"""
a__ : Optional[Any] = True
def UpperCamelCase__ ( self , __lowercase) -> List[str]:
if isinstance(__lowercase , nn.Convad):
nn.init.kaiming_normal_(module.weight , mode='''fan_out''' , nonlinearity='''relu''')
elif isinstance(__lowercase , (nn.BatchNormad, nn.GroupNorm)):
nn.init.constant_(module.weight , 1)
nn.init.constant_(module.bias , 0)
def UpperCamelCase__ ( self , __lowercase , __lowercase=False) -> int:
if isinstance(__lowercase , __lowercase):
__UpperCamelCase :Any = value
__lowercase = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__lowercase = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , UpperCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase) -> Optional[int]:
super().__init__(__lowercase)
__UpperCamelCase :str = config
__UpperCamelCase :str = RegNetEmbeddings(__lowercase)
__UpperCamelCase :Optional[Any] = RegNetEncoder(__lowercase)
__UpperCamelCase :Tuple = nn.AdaptiveAvgPoolad((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__lowercase)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCamelCase__ ( self , __lowercase , __lowercase = None , __lowercase = None) -> BaseModelOutputWithPoolingAndNoAttention:
__UpperCamelCase :int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase :Tuple = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase :str = self.embedder(__lowercase)
__UpperCamelCase :List[str] = self.encoder(
__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase)
__UpperCamelCase :Union[str, Any] = encoder_outputs[0]
__UpperCamelCase :str = self.pooler(__lowercase)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowercase , pooler_output=__lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , UpperCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase) -> Union[str, Any]:
super().__init__(__lowercase)
__UpperCamelCase :int = config.num_labels
__UpperCamelCase :str = RegNetModel(__lowercase)
# classification head
__UpperCamelCase :Any = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__lowercase)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCamelCase__ ( self , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , ) -> ImageClassifierOutputWithNoAttention:
__UpperCamelCase :Tuple = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase :Any = self.regnet(__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase)
__UpperCamelCase :Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
__UpperCamelCase :List[Any] = self.classifier(__lowercase)
__UpperCamelCase :str = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__UpperCamelCase :Dict = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__UpperCamelCase :Union[str, Any] = '''single_label_classification'''
else:
__UpperCamelCase :Any = '''multi_label_classification'''
if self.config.problem_type == "regression":
__UpperCamelCase :List[str] = MSELoss()
if self.num_labels == 1:
__UpperCamelCase :Any = loss_fct(logits.squeeze() , labels.squeeze())
else:
__UpperCamelCase :str = loss_fct(__lowercase , __lowercase)
elif self.config.problem_type == "single_label_classification":
__UpperCamelCase :List[str] = CrossEntropyLoss()
__UpperCamelCase :Optional[Any] = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
__UpperCamelCase :Optional[int] = BCEWithLogitsLoss()
__UpperCamelCase :List[str] = loss_fct(__lowercase , __lowercase)
if not return_dict:
__UpperCamelCase :List[str] = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__lowercase , logits=__lowercase , hidden_states=outputs.hidden_states)
| 105
|
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :str = 0
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Dict = Path(__lowercase) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :Union[str, Any] = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Union[str, Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :str = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Union[str, Any] = Path(__lowercase) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__UpperCamelCase :Tuple = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :Optional[Any] = Path(__lowercase) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(__lowercase).to_dict()
config_dict.pop('''image_processor_type''')
__UpperCamelCase :List[str] = CLIPImageProcessor(**__lowercase)
# save in new folder
model_config.save_pretrained(__lowercase)
config.save_pretrained(__lowercase)
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained(__lowercase)
# make sure private variable is not incorrectly saved
__UpperCamelCase :Union[str, Any] = json.loads(config.to_json_string())
self.assertTrue('''_processor_class''' not in dict_as_saved)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :Tuple = Path(__lowercase) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
__UpperCamelCase :List[str] = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[int]:
with self.assertRaisesRegex(
__lowercase , '''clip-base is not a local folder and is not a valid model identifier'''):
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained('''clip-base''')
def UpperCamelCase__ ( self) -> List[Any]:
with self.assertRaisesRegex(
__lowercase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
__UpperCamelCase :str = AutoImageProcessor.from_pretrained(__lowercase , revision='''aaaaaa''')
def UpperCamelCase__ ( self) -> List[str]:
with self.assertRaisesRegex(
__lowercase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')
def UpperCamelCase__ ( self) -> str:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__lowercase):
__UpperCamelCase :Dict = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
# If remote code is disabled, we can't load this config.
with self.assertRaises(__lowercase):
__UpperCamelCase :List[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase)
__UpperCamelCase :List[Any] = AutoImageProcessor.from_pretrained(__lowercase , trust_remote_code=__lowercase)
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''')
def UpperCamelCase__ ( self) -> Optional[Any]:
try:
AutoConfig.register('''custom''' , __lowercase)
AutoImageProcessor.register(__lowercase , __lowercase)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__lowercase):
AutoImageProcessor.register(__lowercase , __lowercase)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :int = Path(__lowercase) / '''preprocessor_config.json'''
__UpperCamelCase :List[str] = Path(__lowercase) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''') , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w'''))
__UpperCamelCase :int = CustomImageProcessor.from_pretrained(__lowercase)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase)
__UpperCamelCase :int = AutoImageProcessor.from_pretrained(__lowercase)
self.assertIsInstance(__lowercase , __lowercase)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCamelCase__ ( self) -> List[Any]:
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[str] = True
try:
AutoConfig.register('''custom''' , __lowercase)
AutoImageProcessor.register(__lowercase , __lowercase)
# If remote code is not set, the default is to use local
__UpperCamelCase :str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__UpperCamelCase :Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__UpperCamelCase :List[str] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase)
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''')
self.assertTrue(not hasattr(__lowercase , '''is_local'''))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 105
| 1
|
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__lowercase : Union[str, Any] = Mapping[str, np.ndarray]
__lowercase : Tuple = Mapping[str, Any] # Is a nested dict.
__lowercase : Any = 0.01
@dataclasses.dataclass(frozen=lowerCAmelCase_ )
class __UpperCamelCase :
A_ = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
A_ = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
A_ = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
A_ = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
A_ = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
A_ = None
# Optional remark about the protein. Included as a comment in output PDB
# files
A_ = None
# Templates used to generate this protein (prediction-only)
A_ = None
# Chain corresponding to each parent
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = r'(\[[A-Z]+\]\n)'
__a : List[str] = [tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if len(_SCREAMING_SNAKE_CASE ) > 0]
__a : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split('\n' ) for l in tags[1::2]] )
__a : List[str] = ["N", "CA", "C"]
__a : Dict = None
__a : Optional[int] = None
__a : Optional[Any] = None
for g in groups:
if "[PRIMARY]" == g[0]:
__a : str = g[1][0].strip()
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if seq[i] not in residue_constants.restypes:
__a : Union[str, Any] = 'X' # FIXME: strings are immutable
__a : List[Any] = np.array(
[residue_constants.restype_order.get(_SCREAMING_SNAKE_CASE , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
__a : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_SCREAMING_SNAKE_CASE , g[1][axis].split() ) ) )
__a : List[Any] = np.array(_SCREAMING_SNAKE_CASE )
__a : str = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
__a : Dict = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
__a : int = np.array(list(map({'-': 0, '+': 1}.get , g[1][0].strip() ) ) )
__a : str = np.zeros(
(
len(_SCREAMING_SNAKE_CASE ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_SCREAMING_SNAKE_CASE , atom_mask=_SCREAMING_SNAKE_CASE , aatype=_SCREAMING_SNAKE_CASE , residue_index=np.arange(len(_SCREAMING_SNAKE_CASE ) ) , b_factors=_SCREAMING_SNAKE_CASE , )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Protein , _SCREAMING_SNAKE_CASE : int = 0 ):
__a : List[str] = []
__a : Any = prot.remark
if remark is not None:
pdb_headers.append(F"""REMARK {remark}""" )
__a : Any = prot.parents
__a : Tuple = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
__a : Tuple = [p for i, p in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if i == chain_id]
if parents is None or len(_SCREAMING_SNAKE_CASE ) == 0:
__a : List[str] = ['N/A']
pdb_headers.append(F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE )}""" )
return pdb_headers
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Protein , _SCREAMING_SNAKE_CASE : str ):
__a : List[str] = []
__a : List[str] = pdb_str.split('\n' )
__a : List[Any] = prot.remark
if remark is not None:
out_pdb_lines.append(F"""REMARK {remark}""" )
__a : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
__a : List[Any] = []
if prot.parents_chain_index is not None:
__a : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_SCREAMING_SNAKE_CASE ) , [] )
parent_dict[str(_SCREAMING_SNAKE_CASE )].append(_SCREAMING_SNAKE_CASE )
__a : Any = max([int(_SCREAMING_SNAKE_CASE ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
__a : str = parent_dict.get(str(_SCREAMING_SNAKE_CASE ) , ['N/A'] )
parents_per_chain.append(_SCREAMING_SNAKE_CASE )
else:
parents_per_chain.append(list(prot.parents ) )
else:
__a : List[str] = [['N/A']]
def make_parent_line(_SCREAMING_SNAKE_CASE : Sequence[str] ) -> str:
return F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
__a : int = 0
for i, l in enumerate(_SCREAMING_SNAKE_CASE ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_SCREAMING_SNAKE_CASE )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_SCREAMING_SNAKE_CASE ):
__a : Optional[int] = parents_per_chain[chain_counter]
else:
__a : int = ['N/A']
out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE ) )
return "\n".join(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Protein ):
__a : Union[str, Any] = residue_constants.restypes + ['X']
def res_atoa(_SCREAMING_SNAKE_CASE : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , 'UNK' )
__a : Any = residue_constants.atom_types
__a : List[str] = []
__a : str = prot.atom_mask
__a : str = prot.aatype
__a : str = prot.atom_positions
__a : Union[str, Any] = prot.residue_index.astype(np.intaa )
__a : int = prot.b_factors
__a : Tuple = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError('Invalid aatypes.' )
__a : Tuple = get_pdb_headers(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
pdb_lines.extend(_SCREAMING_SNAKE_CASE )
__a : List[Any] = aatype.shape[0]
__a : Any = 1
__a : List[Any] = 0
__a : Optional[Any] = string.ascii_uppercase
__a : Any = None
# Add all atom sites.
for i in range(_SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_SCREAMING_SNAKE_CASE , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
__a : int = 'ATOM'
__a : Tuple = atom_name if len(_SCREAMING_SNAKE_CASE ) == 4 else F""" {atom_name}"""
__a : Union[str, Any] = ''
__a : Tuple = ''
__a : str = 1.0_0
__a : Union[str, Any] = atom_name[0] # Protein supports only C, N, O, S, this works.
__a : str = ''
__a : List[Any] = 'A'
if chain_index is not None:
__a : Any = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
__a : int = (
F"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
F"""{res_name_a:>3} {chain_tag:>1}"""
F"""{residue_index[i]:>4}{insertion_code:>1} """
F"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
F"""{occupancy:>6.2f}{b_factor:>6.2f} """
F"""{element:>2}{charge:>2}"""
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
__a : Union[str, Any] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
__a : Tuple = True
__a : Dict = chain_index[i + 1]
if should_terminate:
# Close the chain.
__a : int = 'TER'
__a : List[Any] = (
F"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
pdb_lines.append('END' )
pdb_lines.append('' )
return "\n".join(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Protein ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : FeatureDict , _SCREAMING_SNAKE_CASE : ModelOutput , _SCREAMING_SNAKE_CASE : Optional[np.ndarray] = None , _SCREAMING_SNAKE_CASE : Optional[np.ndarray] = None , _SCREAMING_SNAKE_CASE : Optional[str] = None , _SCREAMING_SNAKE_CASE : Optional[Sequence[str]] = None , _SCREAMING_SNAKE_CASE : Optional[Sequence[int]] = None , ):
return Protein(
aatype=features['aatype'] , atom_positions=result['final_atom_positions'] , atom_mask=result['final_atom_mask'] , residue_index=features['residue_index'] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask'] ) , chain_index=_SCREAMING_SNAKE_CASE , remark=_SCREAMING_SNAKE_CASE , parents=_SCREAMING_SNAKE_CASE , parents_chain_index=_SCREAMING_SNAKE_CASE , )
| 27
|
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self: Optional[int] ) -> Any:
UpperCAmelCase_ : List[str] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
UpperCAmelCase_ : Union[str, Any] = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase_ )
UpperCAmelCase_ : str = -1
UpperCAmelCase_ : Dict = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = model.generate(lowerCamelCase_ ,max_new_tokens=10 ,do_sample=lowerCamelCase_ )
UpperCAmelCase_ : Any = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
UpperCAmelCase_ : List[Any] = TextStreamer(lowerCamelCase_ )
model.generate(lowerCamelCase_ ,max_new_tokens=10 ,do_sample=lowerCamelCase_ ,streamer=lowerCamelCase_ )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
UpperCAmelCase_ : Optional[int] = cs.out[:-1]
self.assertEqual(lowerCamelCase_ ,lowerCamelCase_ )
def A__ ( self: Dict ) -> Optional[Any]:
UpperCAmelCase_ : str = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
UpperCAmelCase_ : Optional[Any] = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = -1
UpperCAmelCase_ : List[Any] = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase_ )
UpperCAmelCase_ : List[str] = model.generate(lowerCamelCase_ ,max_new_tokens=10 ,do_sample=lowerCamelCase_ )
UpperCAmelCase_ : Dict = tokenizer.decode(greedy_ids[0] )
UpperCAmelCase_ : str = TextIteratorStreamer(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
UpperCAmelCase_ : str = Thread(target=model.generate ,kwargs=lowerCamelCase_ )
thread.start()
UpperCAmelCase_ : int = """"""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(lowerCamelCase_ ,lowerCamelCase_ )
def A__ ( self: List[Any] ) -> Dict:
UpperCAmelCase_ : List[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
UpperCAmelCase_ : Optional[Any] = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = -1
UpperCAmelCase_ : Tuple = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase_ )
UpperCAmelCase_ : Dict = model.generate(lowerCamelCase_ ,max_new_tokens=10 ,do_sample=lowerCamelCase_ )
UpperCAmelCase_ : str = greedy_ids[:, input_ids.shape[1] :]
UpperCAmelCase_ : Dict = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
UpperCAmelCase_ : List[Any] = TextStreamer(lowerCamelCase_ ,skip_prompt=lowerCamelCase_ )
model.generate(lowerCamelCase_ ,max_new_tokens=10 ,do_sample=lowerCamelCase_ ,streamer=lowerCamelCase_ )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
UpperCAmelCase_ : List[str] = cs.out[:-1]
self.assertEqual(lowerCamelCase_ ,lowerCamelCase_ )
def A__ ( self: str ) -> str:
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
UpperCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained("""distilgpt2""" )
UpperCAmelCase_ : Optional[Any] = AutoModelForCausalLM.from_pretrained("""distilgpt2""" ).to(lowerCamelCase_ )
UpperCAmelCase_ : Any = -1
UpperCAmelCase_ : Union[str, Any] = torch.ones((1, 5) ,device=lowerCamelCase_ ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
UpperCAmelCase_ : Union[str, Any] = TextStreamer(lowerCamelCase_ ,skip_special_tokens=lowerCamelCase_ )
model.generate(lowerCamelCase_ ,max_new_tokens=1 ,do_sample=lowerCamelCase_ ,streamer=lowerCamelCase_ )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
UpperCAmelCase_ : List[str] = cs.out[:-1] # Remove the final "\n"
UpperCAmelCase_ : Dict = tokenizer(lowerCamelCase_ ,return_tensors="""pt""" )
self.assertEqual(streamer_text_tokenized.input_ids.shape ,(1, 1) )
def A__ ( self: List[str] ) -> Any:
UpperCAmelCase_ : List[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
UpperCAmelCase_ : Any = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase_ )
UpperCAmelCase_ : List[str] = -1
UpperCAmelCase_ : Optional[Any] = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = TextIteratorStreamer(lowerCamelCase_ ,timeout=0.0_0_1 )
UpperCAmelCase_ : Any = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
UpperCAmelCase_ : Dict = Thread(target=model.generate ,kwargs=lowerCamelCase_ )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(lowerCamelCase_ ):
UpperCAmelCase_ : Union[str, Any] = """"""
for new_text in streamer:
streamer_text += new_text
| 345
| 0
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _lowerCamelCase ( self ):
__a , __a : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , )
__a : Union[str, Any] = '''A painting of a squirrel eating a burger'''
__a : int = jax.device_count()
__a : List[Any] = num_samples * [prompt]
__a : Union[str, Any] = sd_pipe.prepare_inputs(_UpperCAmelCase )
__a : Optional[int] = replicate(_UpperCAmelCase )
__a : List[str] = shard(_UpperCAmelCase )
__a : Dict = jax.random.PRNGKey(0 )
__a : List[str] = jax.random.split(_UpperCAmelCase , jax.device_count() )
__a : Tuple = sd_pipe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , num_inference_steps=25 , jit=_UpperCAmelCase )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__a : int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a : str = images[0, 253:256, 253:256, -1]
__a : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a : Tuple = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _lowerCamelCase ( self ):
__a : Optional[int] = '''stabilityai/stable-diffusion-2'''
__a , __a : Optional[int] = FlaxDPMSolverMultistepScheduler.from_pretrained(_UpperCAmelCase , subfolder='''scheduler''' )
__a , __a : int = FlaxStableDiffusionPipeline.from_pretrained(
_UpperCAmelCase , scheduler=_UpperCAmelCase , revision='''bf16''' , dtype=jnp.bfloataa , )
__a : Dict = scheduler_params
__a : Optional[Any] = '''A painting of a squirrel eating a burger'''
__a : List[str] = jax.device_count()
__a : Optional[int] = num_samples * [prompt]
__a : Optional[int] = sd_pipe.prepare_inputs(_UpperCAmelCase )
__a : List[str] = replicate(_UpperCAmelCase )
__a : Dict = shard(_UpperCAmelCase )
__a : Dict = jax.random.PRNGKey(0 )
__a : Optional[int] = jax.random.split(_UpperCAmelCase , jax.device_count() )
__a : Union[str, Any] = sd_pipe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , num_inference_steps=25 , jit=_UpperCAmelCase )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__a : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a : str = images[0, 253:256, 253:256, -1]
__a : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a : Any = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 188
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''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:
A = [
'''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
A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188
| 1
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def UpperCamelCase ( snake_case__ : Any , snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : str , snake_case__ : str , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : Union[str, Any] , snake_case__ : str , ) -> str:
UpperCamelCase : Optional[Any] = {
'7z': (seven_zip_file, SevenZipExtractor),
'bz2': (bza_file, BzipaExtractor),
'gzip': (gz_file, GzipExtractor),
'lz4': (lza_file, LzaExtractor),
'tar': (tar_file, TarExtractor),
'xz': (xz_file, XzExtractor),
'zip': (zip_file, ZipExtractor),
'zstd': (zstd_file, ZstdExtractor),
}
UpperCamelCase , UpperCamelCase : Dict = input_paths_and_base_extractors[compression_format]
if input_path is None:
UpperCamelCase : List[str] = F"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
UpperCamelCase : int = tmp_path / ('extracted' if is_archive else 'extracted.txt')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCamelCase : List[str] = file_path.read_text(encoding='utf-8' )
else:
UpperCamelCase : Optional[Any] = output_path.read_text(encoding='utf-8' )
UpperCamelCase : List[Any] = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def UpperCamelCase ( snake_case__ : Optional[int] , snake_case__ : Any , snake_case__ : List[Any] , snake_case__ : List[str] , snake_case__ : Any , snake_case__ : Any , snake_case__ : List[Any] , snake_case__ : Optional[Any] , snake_case__ : Dict , snake_case__ : Optional[Any] , snake_case__ : Dict , snake_case__ : int , ) -> List[Any]:
UpperCamelCase : Optional[int] = {
'7z': seven_zip_file,
'bz2': bza_file,
'gzip': gz_file,
'lz4': lza_file,
'tar': tar_file,
'xz': xz_file,
'zip': zip_file,
'zstd': zstd_file,
}
UpperCamelCase : Optional[int] = input_paths[compression_format]
if input_path is None:
UpperCamelCase : Optional[Any] = F"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
UpperCamelCase : Union[str, Any] = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
UpperCamelCase : List[Any] = tmp_path / ('extracted' if is_archive else 'extracted.txt')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCamelCase : List[str] = file_path.read_text(encoding='utf-8' )
else:
UpperCamelCase : Union[str, Any] = output_path.read_text(encoding='utf-8' )
UpperCamelCase : int = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def UpperCamelCase ( snake_case__ : Tuple , snake_case__ : str ) -> List[str]:
import tarfile
UpperCamelCase : Optional[Any] = tmp_path / 'data_dot_dot'
directory.mkdir()
UpperCamelCase : Optional[int] = directory / 'tar_file_with_dot_dot.tar'
with tarfile.TarFile(snake_case__ , 'w' ) as f:
f.add(snake_case__ , arcname=os.path.join('..' , text_file.name ) )
return path
@pytest.fixture
def UpperCamelCase ( snake_case__ : Any ) -> Tuple:
import tarfile
UpperCamelCase : Dict = tmp_path / 'data_sym_link'
directory.mkdir()
UpperCamelCase : Tuple = directory / 'tar_file_with_sym_link.tar'
os.symlink('..' , directory / 'subdir' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , 'w' ) as f:
f.add(str(directory / 'subdir' ) , arcname='subdir' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'insecure_tar_file, error_log' , [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')] , )
def UpperCamelCase ( snake_case__ : str , snake_case__ : int , snake_case__ : Dict , snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ) -> Tuple:
UpperCamelCase : Optional[int] = {
'tar_file_with_dot_dot': tar_file_with_dot_dot,
'tar_file_with_sym_link': tar_file_with_sym_link,
}
UpperCamelCase : Optional[int] = insecure_tar_files[insecure_tar_file]
UpperCamelCase : List[Any] = tmp_path / 'extracted'
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def UpperCamelCase ( snake_case__ : List[str] ) -> str:
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
UpperCamelCase : Optional[Any] = tmpdir / 'not_a_zip_file'
# From: https://github.com/python/cpython/pull/5053
UpperCamelCase : int = (
B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
)
with not_a_zip_file.open('wb' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 119
|
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
'''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
'''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
'''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
'''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
'''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
'''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
'''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
'''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
'''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
'''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : List[str] = "codegen"
UpperCAmelCase__ : str = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self, SCREAMING_SNAKE_CASE_=5_0400, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=4096, SCREAMING_SNAKE_CASE_=28, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=64, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_="gelu_new", SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=1e-5, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=5_0256, SCREAMING_SNAKE_CASE_=5_0256, SCREAMING_SNAKE_CASE_=False, **SCREAMING_SNAKE_CASE_, ) -> Tuple:
UpperCamelCase : Tuple = vocab_size
UpperCamelCase : Optional[int] = n_ctx
UpperCamelCase : Optional[int] = n_positions
UpperCamelCase : List[str] = n_embd
UpperCamelCase : Dict = n_layer
UpperCamelCase : int = n_head
UpperCamelCase : Union[str, Any] = n_inner
UpperCamelCase : int = rotary_dim
UpperCamelCase : Optional[Any] = activation_function
UpperCamelCase : Optional[int] = resid_pdrop
UpperCamelCase : Union[str, Any] = embd_pdrop
UpperCamelCase : Optional[Any] = attn_pdrop
UpperCamelCase : List[str] = layer_norm_epsilon
UpperCamelCase : Union[str, Any] = initializer_range
UpperCamelCase : str = use_cache
UpperCamelCase : Dict = bos_token_id
UpperCamelCase : Union[str, Any] = eos_token_id
super().__init__(
bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, tie_word_embeddings=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = "default", SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = False, ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE_, task=SCREAMING_SNAKE_CASE_, patching_specs=SCREAMING_SNAKE_CASE_, use_past=SCREAMING_SNAKE_CASE_ )
if not getattr(self._config, 'pad_token_id', SCREAMING_SNAKE_CASE_ ):
# TODO: how to do that better?
UpperCamelCase : str = 0
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
UpperCamelCase : Tuple = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_, direction='inputs' )
UpperCamelCase : List[Any] = {0: 'batch', 1: 'past_sequence + sequence'}
else:
UpperCamelCase : Optional[int] = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def snake_case_ ( self ) -> int:
return self._config.n_layer
@property
def snake_case_ ( self ) -> int:
return self._config.n_head
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = -1, SCREAMING_SNAKE_CASE_ = -1, SCREAMING_SNAKE_CASE_ = False, SCREAMING_SNAKE_CASE_ = None, ) -> Mapping[str, Any]:
UpperCamelCase : Tuple = super(SCREAMING_SNAKE_CASE_, self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE_, batch_size=SCREAMING_SNAKE_CASE_, seq_length=SCREAMING_SNAKE_CASE_, is_pair=SCREAMING_SNAKE_CASE_, framework=SCREAMING_SNAKE_CASE_ )
# We need to order the input in the way they appears in the forward()
UpperCamelCase : Optional[int] = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
UpperCamelCase , UpperCamelCase : List[str] = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
UpperCamelCase : List[Any] = seqlen + 2
UpperCamelCase : List[str] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCamelCase : str = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
]
UpperCamelCase : List[Any] = common_inputs['attention_mask']
if self.use_past:
UpperCamelCase : Optional[Any] = ordered_inputs['attention_mask'].dtype
UpperCamelCase : List[Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, dtype=SCREAMING_SNAKE_CASE_ )], dim=1 )
return ordered_inputs
@property
def snake_case_ ( self ) -> int:
return 13
| 119
| 1
|
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
__snake_case : Any = logging.get_logger(__name__)
__snake_case : str = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__snake_case : Optional[int] = {
"vocab_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
},
"merges_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
},
"tokenizer_file": {
"Salesforce/codegen-350M-mono": (
"https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
),
},
}
__snake_case : Union[str, Any] = {
"Salesforce/codegen-350M-mono": 20_48,
}
class __SCREAMING_SNAKE_CASE ( __lowerCAmelCase):
_SCREAMING_SNAKE_CASE : Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : Any = ["input_ids", "attention_mask"]
_SCREAMING_SNAKE_CASE : Dict = CodeGenTokenizer
def __init__( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase="<|endoftext|>" , _UpperCamelCase="<|endoftext|>" , _UpperCamelCase="<|endoftext|>" , _UpperCamelCase=False , **_UpperCamelCase , ):
"""simple docstring"""
super().__init__(
lowerCamelCase__ , lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , unk_token=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
if kwargs.pop('add_bos_token' , lowerCamelCase__ ):
lowerCAmelCase__ = kwargs.pop('name_or_path' , '' )
raise ValueError(
'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
F"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
F"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
' so that the fast tokenizer works correctly.' )
lowerCAmelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowerCamelCase__ ) != add_prefix_space:
lowerCAmelCase__ = getattr(lowerCamelCase__ , pre_tok_state.pop('type' ) )
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = pre_tok_class(**lowerCamelCase__ )
lowerCAmelCase__ = add_prefix_space
def UpperCamelCase__ ( self , *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = kwargs.get('is_split_into_words' , lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCamelCase__ ( self , *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = kwargs.get('is_split_into_words' , lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = None ):
"""simple docstring"""
lowerCAmelCase__ = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = super().decode(
token_ids=lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ , **lowerCamelCase__ , )
if truncate_before_pattern is not None and len(lowerCamelCase__ ) > 0:
lowerCAmelCase__ = self.truncate(lowerCamelCase__ , lowerCamelCase__ )
return decoded_text
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
def find_re(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
lowerCAmelCase__ = pattern.search(lowerCamelCase__ , lowerCamelCase__ )
return m.start() if m else -1
lowerCAmelCase__ = [re.compile(lowerCamelCase__ , re.MULTILINE ) for pattern in truncate_before_pattern]
lowerCAmelCase__ = list(re.finditer('^print' , lowerCamelCase__ , re.MULTILINE ) )
if len(lowerCamelCase__ ) > 1:
lowerCAmelCase__ = completion[: prints[1].start()]
lowerCAmelCase__ = list(re.finditer('^def' , lowerCamelCase__ , re.MULTILINE ) )
if len(lowerCamelCase__ ) > 1:
lowerCAmelCase__ = completion[: defs[1].start()]
lowerCAmelCase__ = 0
lowerCAmelCase__ = [
pos for pos in [find_re(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) for terminal in terminals] if pos != -1
]
if len(lowerCamelCase__ ) > 0:
return completion[: min(lowerCamelCase__ )]
else:
return completion
| 357
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__snake_case : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 122
| 0
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__UpperCamelCase : Dict = None
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__UpperCamelCase : int = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
__UpperCamelCase : Tuple = {
"camembert-base": 512,
}
__UpperCamelCase : Dict = "▁"
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['''input_ids''', '''attention_mask''']
UpperCamelCase__ = CamembertTokenizer
def __init__( self :Dict , __magic_name__ :int=None , __magic_name__ :Optional[int]=None , __magic_name__ :List[str]="<s>" , __magic_name__ :Tuple="</s>" , __magic_name__ :str="</s>" , __magic_name__ :Optional[int]="<s>" , __magic_name__ :Dict="<unk>" , __magic_name__ :Tuple="<pad>" , __magic_name__ :Dict="<mask>" , __magic_name__ :Dict=["<s>NOTUSED", "</s>NOTUSED"] , **__magic_name__ :str , ):
'''simple docstring'''
a = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else mask_token
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , additional_special_tokens=__magic_name__ , **__magic_name__ , )
a = vocab_file
a = False if not self.vocab_file else True
def lowerCamelCase__ ( self :List[str] , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None ):
'''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 :Dict , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None ):
'''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]
def lowerCamelCase__ ( self :Tuple , __magic_name__ :str , __magic_name__ :Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
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__ ):
copyfile(self.vocab_file , __magic_name__ )
return (out_vocab_file,)
| 228
|
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 __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = CLIPTokenizer
UpperCamelCase__ = CLIPTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = {}
UpperCamelCase__ = False
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
super().setUp()
# fmt: off
a = ["""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
a = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) )
a = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
a = {"""unk_token""": """<unk>"""}
a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__magic_name__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__magic_name__ ) )
def lowerCamelCase__ ( self :Optional[Any] , **__magic_name__ :Union[str, Any] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__magic_name__ )
def lowerCamelCase__ ( self :int , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__magic_name__ )
def lowerCamelCase__ ( self :int , __magic_name__ :List[str] ):
'''simple docstring'''
a = """lower newer"""
a = """lower newer"""
return input_text, output_text
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
a = """lower newer"""
a = ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
a = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
a = tokens + [tokenizer.unk_token]
a = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ )
@require_ftfy
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
a = self.tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ )
a = self.rust_tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ )
a = """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
a = tokenizer_s.tokenize(__magic_name__ )
a = tokenizer_r.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
a = """xa\u0303y""" + """ """ + """x\xe3y"""
a = tokenizer_s.tokenize(__magic_name__ )
a = tokenizer_r.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
# Test that the tokenization is identical on unicode of space type
a = [
"""\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:
a = tokenizer_s.tokenize(__magic_name__ )
a = tokenizer_r.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
# Test that the tokenization is identical on unicode of line break type
a = [
"""\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:
a = tokenizer_s.tokenize(__magic_name__ )
a = tokenizer_r.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
a = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
a = F'{text_of_1_token} {text_of_1_token}'
a = self.rust_tokenizer_class.from_pretrained(
__magic_name__ , use_fast=__magic_name__ , )
a = tokenizer_r(__magic_name__ , return_offsets_mapping=__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__magic_name__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__magic_name__ ) + 1, len(__magic_name__ ) + 1 + len(__magic_name__ )) , )
a = F' {text}'
a = self.rust_tokenizer_class.from_pretrained(
__magic_name__ , use_fast=__magic_name__ , )
a = tokenizer_r(__magic_name__ , return_offsets_mapping=__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__magic_name__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__magic_name__ ) + 1, 1 + len(__magic_name__ ) + 1 + len(__magic_name__ )) , )
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
with self.assertRaises(__magic_name__ ) 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 lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
super().test_tokenization_python_rust_equals()
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
pass
| 228
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCamelCase : str = TypeVar('T')
class snake_case ( Generic[T] ):
def __init__( self : Optional[int] , A : List[Any] ):
'''simple docstring'''
a : Dict = data
a : Node[T] | None = None
def __str__( self : Dict ):
'''simple docstring'''
return F'''{self.data}'''
class snake_case ( Generic[T] ):
def __init__( self : Dict ):
'''simple docstring'''
a : Node[T] | None = None
def __iter__( self : List[str] ):
'''simple docstring'''
a : int = self.top
while node:
yield node.data
a : List[str] = node.next
def __str__( self : str ):
'''simple docstring'''
return "->".join([str(snake_case__ ) for item in self] )
def __len__( self : Tuple ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def lowerCamelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return self.top is None
def lowerCamelCase__ ( self : Any , A : List[Any] ):
'''simple docstring'''
a : List[str] = Node(snake_case__ )
if not self.is_empty():
a : Union[str, Any] = self.top
a : Optional[Any] = node
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case__ )
a : List[Any] = self.top
a : Dict = self.top.next
return pop_node.data
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
a : List[Any] = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 362
|
"""simple docstring"""
_UpperCamelCase : List[Any] = 8.31_44_62 # Unit - J mol-1 K-1
def snake_case (A_ :float , A_ :float , A_ :float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def snake_case (A_ :float , A_ :float , A_ :float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 186
| 0
|
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a : Optional[Any] = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __UpperCamelCase ( datasets.BuilderConfig ):
lowerCamelCase : Optional[datasets.Features] =None
def _SCREAMING_SNAKE_CASE ( _lowercase : "pyspark.sql.DataFrame" , _lowercase : List[int] , ) ->Union[str, Any]:
'''simple docstring'''
import pyspark
def generate_fn():
a : int = df.select("*" , pyspark.sql.functions.spark_partition_id().alias("part_id" ) )
for partition_id in partition_order:
a : Optional[int] = df_with_partition_id.select("*" ).where(F"""part_id = {partition_id}""" ).drop("part_id" )
a : Dict = partition_df.collect()
a : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class __UpperCamelCase ( _BaseExamplesIterable ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None , ) -> List[str]:
a : int = df
a : Tuple = partition_order or range(self.df.rdd.getNumPartitions() )
a : Dict = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self ) -> Dict:
yield from self.generate_examples_fn()
def __a ( self , lowerCAmelCase__ ) -> "SparkExamplesIterable":
a : Dict = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowerCAmelCase__ )
return SparkExamplesIterable(self.df , partition_order=lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> "SparkExamplesIterable":
a : Optional[Any] = self.split_shard_indices_by_worker(lowerCAmelCase__ , lowerCAmelCase__ )
return SparkExamplesIterable(self.df , partition_order=lowerCAmelCase__ )
@property
def __a ( self ) -> int:
return len(self.partition_order )
class __UpperCamelCase ( datasets.DatasetBuilder ):
lowerCamelCase : Dict =SparkConfig
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> Tuple:
import pyspark
a : List[str] = pyspark.sql.SparkSession.builder.getOrCreate()
a : List[Any] = df
a : Tuple = working_dir
super().__init__(
cache_dir=lowerCAmelCase__ , config_name=str(self.df.semanticHash() ) , **lowerCAmelCase__ , )
def __a ( self ) -> Tuple:
# Returns the path of the created file.
def create_cache_and_write_probe(lowerCAmelCase__ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=lowerCAmelCase__ )
a : Dict = os.path.join(self._cache_dir , "fs_test" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowerCAmelCase__ , "a" )
return [probe_file]
if self._spark.conf.get("spark.master" , "" ).startswith("local" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
a : Union[str, Any] = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(lowerCAmelCase__ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir" )
def __a ( self ) -> str:
return datasets.DatasetInfo(features=self.config.features )
def __a ( self , lowerCAmelCase__ ) -> Dict:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __a ( self , lowerCAmelCase__ ) -> List[str]:
import pyspark
def get_arrow_batch_size(lowerCAmelCase__ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )
a : Dict = self.df.count()
a : str = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
a : Optional[Any] = (
self.df.limit(lowerCAmelCase__ )
.repartition(1 )
.mapInArrow(lowerCAmelCase__ , "batch_bytes: long" )
.agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
a : Any = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
a : Any = min(lowerCAmelCase__ , int(approx_total_size / max_shard_size ) )
a : Union[str, Any] = self.df.repartition(lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
import pyspark
a : List[Any] = ParquetWriter if file_format == "parquet" else ArrowWriter
a : str = os.path.join(self._working_dir , os.path.basename(lowerCAmelCase__ ) ) if self._working_dir else fpath
a : List[Any] = file_format == "parquet"
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
a : Optional[Any] = self.config.features
a : Tuple = self._writer_batch_size
a : Union[str, Any] = self._fs.storage_options
def write_arrow(lowerCAmelCase__ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
a : Optional[int] = pyspark.TaskContext().taskAttemptId()
a : Dict = next(lowerCAmelCase__ , lowerCAmelCase__ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=["task_id", "num_examples", "num_bytes"] , )
a : List[str] = 0
a : Optional[int] = writer_class(
features=lowerCAmelCase__ , path=working_fpath.replace("SSSSS" , f"""{shard_id:05d}""" ).replace("TTTTT" , f"""{task_id:05d}""" ) , writer_batch_size=lowerCAmelCase__ , storage_options=lowerCAmelCase__ , embed_local_files=lowerCAmelCase__ , )
a : int = pa.Table.from_batches([first_batch] )
writer.write_table(lowerCAmelCase__ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
a, a : List[str] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )
shard_id += 1
a : List[str] = writer_class(
features=writer._features , path=working_fpath.replace("SSSSS" , f"""{shard_id:05d}""" ).replace("TTTTT" , f"""{task_id:05d}""" ) , writer_batch_size=lowerCAmelCase__ , storage_options=lowerCAmelCase__ , embed_local_files=lowerCAmelCase__ , )
a : List[str] = pa.Table.from_batches([batch] )
writer.write_table(lowerCAmelCase__ )
if writer._num_bytes > 0:
a, a : Any = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowerCAmelCase__ ) ):
a : Union[str, Any] = os.path.join(os.path.dirname(lowerCAmelCase__ ) , os.path.basename(lowerCAmelCase__ ) )
shutil.move(lowerCAmelCase__ , lowerCAmelCase__ )
a : Any = (
self.df.mapInArrow(lowerCAmelCase__ , "task_id: long, num_examples: long, num_bytes: long" )
.groupBy("task_id" )
.agg(
pyspark.sql.functions.sum("num_examples" ).alias("total_num_examples" ) , pyspark.sql.functions.sum("num_bytes" ).alias("total_num_bytes" ) , pyspark.sql.functions.count("num_bytes" ).alias("num_shards" ) , pyspark.sql.functions.collect_list("num_examples" ).alias("shard_lengths" ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = "arrow" , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> Optional[int]:
self._validate_cache_dir()
a : List[str] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowerCAmelCase__ )
a : Optional[int] = not is_remote_filesystem(self._fs )
a : List[str] = os.path.join if is_local else posixpath.join
a : Optional[Any] = "-TTTTT-SSSSS-of-NNNNN"
a : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
a : int = path_join(self._output_dir , lowerCAmelCase__ )
a : List[Any] = 0
a : Optional[Any] = 0
a : List[Any] = 0
a : int = []
a : List[Any] = []
for task_id, content in self._prepare_split_single(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
(
(
a
), (
a
), (
a
), (
a
),
) : Optional[int] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowerCAmelCase__ )
a : Tuple = total_num_examples
a : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
a : List[str] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
a : List[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
rename(
lowerCAmelCase__ , fpath.replace("SSSSS" , f"""{shard_id:05d}""" ).replace("TTTTT" , f"""{task_id:05d}""" ) , fpath.replace("TTTTT-SSSSS" , f"""{global_shard_id:05d}""" ).replace("NNNNN" , f"""{total_shards:05d}""" ) , )
a : Dict = []
a : List[Any] = 0
for i in range(len(lowerCAmelCase__ ) ):
a, a : Any = task_id_and_num_shards[i]
for shard_id in range(lowerCAmelCase__ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowerCAmelCase__ , len(lowerCAmelCase__ ) ).map(lambda lowerCAmelCase__ : _rename_shard(*lowerCAmelCase__ ) ).collect()
else:
# don't use any pattern
a : Optional[Any] = 0
a : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("SSSSS" , f"""{shard_id:05d}""" ).replace("TTTTT" , f"""{task_id:05d}""" ) , fpath.replace(lowerCAmelCase__ , "" ) , )
def __a ( self , lowerCAmelCase__ , ) -> SparkExamplesIterable:
return SparkExamplesIterable(self.df )
| 105
|
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] , _lowercase : int ) ->str:
'''simple docstring'''
a : Optional[Any] = Mock()
a : Dict = conn, Mock()
a : Union[str, Any] = iter([1, None] )
a : Optional[int] = lambda _lowercase : next(_lowercase )
# ===== invoke =====
send_file(filename="mytext.txt" , testing=_lowercase )
# ===== 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()
| 105
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __snake_case ( lowerCamelCase__ ):
"""simple docstring"""
_lowerCamelCase = """mobilenet_v1"""
def __init__( self , __lowerCamelCase=3 , __lowerCamelCase=224 , __lowerCamelCase=1.0 , __lowerCamelCase=8 , __lowerCamelCase="relu6" , __lowerCamelCase=True , __lowerCamelCase=0.9_9_9 , __lowerCamelCase=0.0_2 , __lowerCamelCase=0.0_0_1 , **__lowerCamelCase , ):
'''simple docstring'''
super().__init__(**__snake_case )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
__A : List[Any] = num_channels
__A : List[str] = image_size
__A : Union[str, Any] = depth_multiplier
__A : int = min_depth
__A : Optional[Any] = hidden_act
__A : Tuple = tf_padding
__A : Union[str, Any] = classifier_dropout_prob
__A : str = initializer_range
__A : Dict = layer_norm_eps
class __snake_case ( lowerCamelCase__ ):
"""simple docstring"""
_lowerCamelCase = version.parse("""1.11""" )
@property
def UpperCamelCase__( self ):
'''simple docstring'''
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def UpperCamelCase__( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def UpperCamelCase__( self ):
'''simple docstring'''
return 1e-4
| 358
|
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
a_ = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , **__lowerCamelCase ):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , __lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
return super().__call__(__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase__( self , **__lowerCamelCase ):
'''simple docstring'''
__A : Union[str, Any] = {}
if "candidate_labels" in kwargs:
__A : Tuple = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__A : List[str] = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase="This is a photo of {}." ):
'''simple docstring'''
__A : Optional[int] = load_image(__lowerCamelCase )
__A : Optional[int] = self.image_processor(images=[image] , return_tensors=self.framework )
__A : int = candidate_labels
__A : int = [hypothesis_template.format(__lowerCamelCase ) for x in candidate_labels]
__A : Dict = self.tokenizer(__lowerCamelCase , return_tensors=self.framework , padding=__lowerCamelCase )
__A : int = [text_inputs]
return inputs
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[int] = model_inputs.pop('''candidate_labels''' )
__A : str = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __lowerCamelCase ):
__A : Union[str, Any] = text_inputs[0]
else:
# Batching case.
__A : str = text_inputs[0][0]
__A : List[str] = self.model(**__lowerCamelCase , **__lowerCamelCase )
__A : Dict = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[int] = model_outputs.pop('''candidate_labels''' )
__A : int = model_outputs['''logits'''][0]
if self.framework == "pt":
__A : Union[str, Any] = logits.softmax(dim=-1 ).squeeze(-1 )
__A : Dict = probs.tolist()
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__A : List[Any] = [scores]
elif self.framework == "tf":
__A : List[Any] = stable_softmax(__lowerCamelCase , axis=-1 )
__A : str = probs.numpy().tolist()
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
__A : str = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__lowerCamelCase , __lowerCamelCase ) , key=lambda __lowerCamelCase : -x[0] )
]
return result
| 291
| 0
|
import logging
import os
from .state import PartialState
class __magic_name__ ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def _UpperCAmelCase ( lowercase_ ) -> Dict:
"""simple docstring"""
a__ =PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def _UpperCAmelCase ( self, lowercase_, lowercase_, *lowercase_, **lowercase_ ) -> Union[str, Any]:
"""simple docstring"""
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
a__ =kwargs.pop('''main_process_only''', lowercase_ )
a__ =kwargs.pop('''in_order''', lowercase_ )
if self.isEnabledFor(lowercase_ ):
if self._should_log(lowercase_ ):
a__, a__ =self.process(lowercase_, lowercase_ )
self.logger.log(lowercase_, lowercase_, *lowercase_, **lowercase_ )
elif in_order:
a__ =PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
a__, a__ =self.process(lowercase_, lowercase_ )
self.logger.log(lowercase_, lowercase_, *lowercase_, **lowercase_ )
state.wait_for_everyone()
def UpperCAmelCase__ ( _A : str , _A : str = None ):
'''simple docstring'''
if log_level is None:
a__ =os.environ.get('''ACCELERATE_LOG_LEVEL''' , _A )
a__ =logging.getLogger(_A )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_A , {} )
| 188
|
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = IFInpaintingSuperResolutionPipeline
lowerCamelCase__ : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowerCamelCase__ : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
lowerCamelCase__ : str = PipelineTesterMixin.required_optional_params - {'latents'}
def _UpperCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def _UpperCAmelCase ( self, lowercase_, lowercase_=0 ) -> Tuple:
"""simple docstring"""
if str(lowercase_ ).startswith('''mps''' ):
a__ =torch.manual_seed(lowercase_ )
else:
a__ =torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
a__ =floats_tensor((1, 3, 16, 16), rng=random.Random(lowercase_ ) ).to(lowercase_ )
a__ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowercase_ ) ).to(lowercase_ )
a__ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowercase_ ) ).to(lowercase_ )
a__ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available(), reason='''XFormers attention is only available with CUDA and `xformers` installed''', )
def _UpperCAmelCase ( self ) -> List[str]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _UpperCAmelCase ( self ) -> int:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''', reason='''float16 requires CUDA''' )
def _UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _UpperCAmelCase ( self ) -> int:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _UpperCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_save_load_local()
def _UpperCAmelCase ( self ) -> str:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2, )
| 188
| 1
|
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
UpperCAmelCase : List[str] = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def __lowerCamelCase ( lowerCamelCase__ : int ):
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Dict ):
'''simple docstring'''
if args.student_type == "roberta":
lowerCamelCase = False
elif args.student_type == "gpt2":
lowerCamelCase = False
def __lowerCamelCase ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[Any] ):
'''simple docstring'''
if args.student_type == "roberta":
lowerCamelCase = False
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser(description="""Training""" )
parser.add_argument("""--force""" , action="""store_true""" , help="""Overwrite dump_path if it already exists.""" )
parser.add_argument(
"""--dump_path""" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""The output directory (log, checkpoints, parameters, etc.)""" )
parser.add_argument(
"""--data_file""" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" , )
parser.add_argument(
"""--student_type""" , type=lowerCamelCase__ , choices=["""distilbert""", """roberta""", """gpt2"""] , required=lowerCamelCase__ , help="""The student type (DistilBERT, RoBERTa).""" , )
parser.add_argument("""--student_config""" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""Path to the student configuration.""" )
parser.add_argument(
"""--student_pretrained_weights""" , default=lowerCamelCase__ , type=lowerCamelCase__ , help="""Load student initialization checkpoint.""" )
parser.add_argument(
"""--teacher_type""" , choices=["""bert""", """roberta""", """gpt2"""] , required=lowerCamelCase__ , help="""Teacher type (BERT, RoBERTa).""" )
parser.add_argument("""--teacher_name""" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""The teacher model.""" )
parser.add_argument("""--temperature""" , default=2.0 , type=lowerCamelCase__ , help="""Temperature for the softmax temperature.""" )
parser.add_argument(
"""--alpha_ce""" , default=0.5 , type=lowerCamelCase__ , help="""Linear weight for the distillation loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_mlm""" , default=0.0 , type=lowerCamelCase__ , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" , )
parser.add_argument("""--alpha_clm""" , default=0.5 , type=lowerCamelCase__ , help="""Linear weight for the CLM loss. Must be >=0.""" )
parser.add_argument("""--alpha_mse""" , default=0.0 , type=lowerCamelCase__ , help="""Linear weight of the MSE loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_cos""" , default=0.0 , type=lowerCamelCase__ , help="""Linear weight of the cosine embedding loss. Must be >=0.""" )
parser.add_argument(
"""--mlm""" , action="""store_true""" , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.""" )
parser.add_argument(
"""--mlm_mask_prop""" , default=0.1_5 , type=lowerCamelCase__ , help="""Proportion of tokens for which we need to make a prediction.""" , )
parser.add_argument("""--word_mask""" , default=0.8 , type=lowerCamelCase__ , help="""Proportion of tokens to mask out.""" )
parser.add_argument("""--word_keep""" , default=0.1 , type=lowerCamelCase__ , help="""Proportion of tokens to keep.""" )
parser.add_argument("""--word_rand""" , default=0.1 , type=lowerCamelCase__ , help="""Proportion of tokens to randomly replace.""" )
parser.add_argument(
"""--mlm_smoothing""" , default=0.7 , type=lowerCamelCase__ , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" , )
parser.add_argument("""--token_counts""" , type=lowerCamelCase__ , help="""The token counts in the data_file for MLM.""" )
parser.add_argument(
"""--restrict_ce_to_mask""" , action="""store_true""" , help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" , )
parser.add_argument(
"""--freeze_pos_embs""" , action="""store_true""" , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" , )
parser.add_argument(
"""--freeze_token_type_embds""" , action="""store_true""" , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" , )
parser.add_argument("""--n_epoch""" , type=lowerCamelCase__ , default=3 , help="""Number of pass on the whole dataset.""" )
parser.add_argument("""--batch_size""" , type=lowerCamelCase__ , default=5 , help="""Batch size (for each process).""" )
parser.add_argument(
"""--group_by_size""" , action="""store_false""" , help="""If true, group sequences that have similar length into the same batch. Default is true.""" , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=lowerCamelCase__ , default=50 , help="""Gradient accumulation for larger training batches.""" , )
parser.add_argument("""--warmup_prop""" , default=0.0_5 , type=lowerCamelCase__ , help="""Linear warmup proportion.""" )
parser.add_argument("""--weight_decay""" , default=0.0 , type=lowerCamelCase__ , help="""Weight decay if we apply some.""" )
parser.add_argument("""--learning_rate""" , default=5E-4 , type=lowerCamelCase__ , help="""The initial learning rate for Adam.""" )
parser.add_argument("""--adam_epsilon""" , default=1E-6 , type=lowerCamelCase__ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , default=5.0 , type=lowerCamelCase__ , help="""Max gradient norm.""" )
parser.add_argument("""--initializer_range""" , default=0.0_2 , type=lowerCamelCase__ , help="""Random initialization range.""" )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=lowerCamelCase__ , default="""O1""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_gpu""" , type=lowerCamelCase__ , default=1 , help="""Number of GPUs in the node.""" )
parser.add_argument("""--local_rank""" , type=lowerCamelCase__ , default=-1 , help="""Distributed training - Local rank""" )
parser.add_argument("""--seed""" , type=lowerCamelCase__ , default=56 , help="""Random seed""" )
parser.add_argument("""--log_interval""" , type=lowerCamelCase__ , default=500 , help="""Tensorboard logging interval.""" )
parser.add_argument("""--checkpoint_interval""" , type=lowerCamelCase__ , default=4000 , help="""Checkpoint interval.""" )
lowerCamelCase = parser.parse_args()
sanity_checks(lowerCamelCase__ )
# ARGS #
init_gpu_params(lowerCamelCase__ )
set_seed(lowerCamelCase__ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
""" itUse `--force` if you want to overwrite it""" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )
# SAVE PARAMS #
logger.info(f'Param: {args}' )
with open(os.path.join(args.dump_path , """parameters.json""" ) , """w""" ) as f:
json.dump(vars(lowerCamelCase__ ) , lowerCamelCase__ , indent=4 )
git_log(args.dump_path )
lowerCamelCase , lowerCamelCase , lowerCamelCase = MODEL_CLASSES[args.student_type]
lowerCamelCase , lowerCamelCase , lowerCamelCase = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowerCamelCase = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowerCamelCase = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowerCamelCase = tokenizer.all_special_tokens.index(lowerCamelCase__ )
lowerCamelCase = tokenizer.all_special_ids[idx]
logger.info(f'Special tokens {special_tok_ids}' )
lowerCamelCase = special_tok_ids
lowerCamelCase = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'Loading data from {args.data_file}' )
with open(args.data_file , """rb""" ) as fp:
lowerCamelCase = pickle.load(lowerCamelCase__ )
if args.mlm:
logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
with open(args.token_counts , """rb""" ) as fp:
lowerCamelCase = pickle.load(lowerCamelCase__ )
lowerCamelCase = np.maximum(lowerCamelCase__ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowerCamelCase = 0.0 # do not predict special tokens
lowerCamelCase = torch.from_numpy(lowerCamelCase__ )
else:
lowerCamelCase = None
lowerCamelCase = LmSeqsDataset(params=lowerCamelCase__ , data=lowerCamelCase__ )
logger.info("""Data loader created.""" )
# STUDENT #
logger.info(f'Loading student config from {args.student_config}' )
lowerCamelCase = student_config_class.from_pretrained(args.student_config )
lowerCamelCase = True
if args.student_pretrained_weights is not None:
logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
lowerCamelCase = student_model_class.from_pretrained(args.student_pretrained_weights , config=lowerCamelCase__ )
else:
lowerCamelCase = student_model_class(lowerCamelCase__ )
if args.n_gpu > 0:
student.to(f'cuda:{args.local_rank}' )
logger.info("""Student loaded.""" )
# TEACHER #
lowerCamelCase = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=lowerCamelCase__ )
if args.n_gpu > 0:
teacher.to(f'cuda:{args.local_rank}' )
logger.info(f'Teacher loaded from {args.teacher_name}.' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(lowerCamelCase__ , lowerCamelCase__ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(lowerCamelCase__ , lowerCamelCase__ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowerCamelCase = Distiller(
params=lowerCamelCase__ , dataset=lowerCamelCase__ , token_probs=lowerCamelCase__ , student=lowerCamelCase__ , teacher=lowerCamelCase__ )
distiller.train()
logger.info("""Let's go get some drinks.""" )
if __name__ == "__main__":
main()
| 66
|
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
UpperCAmelCase : Dict = tf.data.AUTOTUNE
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser(description="""Train a masked language model on TPU.""" )
parser.add_argument(
"""--pretrained_model_config""" , type=lowerCamelCase__ , default="""roberta-base""" , help="""The model config to use. Note that we don't copy the model's weights, only the config!""" , )
parser.add_argument(
"""--tokenizer""" , type=lowerCamelCase__ , default="""unigram-tokenizer-wikitext""" , help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.""" , )
parser.add_argument(
"""--per_replica_batch_size""" , type=lowerCamelCase__ , default=8 , help="""Batch size per TPU core.""" , )
parser.add_argument(
"""--no_tpu""" , action="""store_true""" , help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.""" , )
parser.add_argument(
"""--tpu_name""" , type=lowerCamelCase__ , help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.""" , default="""local""" , )
parser.add_argument(
"""--tpu_zone""" , type=lowerCamelCase__ , help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.""" , )
parser.add_argument(
"""--gcp_project""" , type=lowerCamelCase__ , help="""Google cloud project name. Only used for non-Colab TPU nodes.""" )
parser.add_argument(
"""--bfloat16""" , action="""store_true""" , help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.""" , )
parser.add_argument(
"""--train_dataset""" , type=lowerCamelCase__ , help="""Path to training dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--shuffle_buffer_size""" , type=lowerCamelCase__ , default=2**18 , help="""Size of the shuffle buffer (in samples)""" , )
parser.add_argument(
"""--eval_dataset""" , type=lowerCamelCase__ , help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase__ , default=1 , help="""Number of epochs to train for.""" , )
parser.add_argument(
"""--learning_rate""" , type=lowerCamelCase__ , default=1E-4 , help="""Learning rate to use for training.""" , )
parser.add_argument(
"""--weight_decay_rate""" , type=lowerCamelCase__ , default=1E-3 , help="""Weight decay rate to use for training.""" , )
parser.add_argument(
"""--max_length""" , type=lowerCamelCase__ , default=512 , help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py""" , )
parser.add_argument(
"""--mlm_probability""" , type=lowerCamelCase__ , default=0.1_5 , help="""Fraction of tokens to mask during training.""" , )
parser.add_argument("""--output_dir""" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""Path to save model checkpoints to.""" )
parser.add_argument("""--hub_model_id""" , type=lowerCamelCase__ , help="""Model ID to upload to on the Hugging Face Hub.""" )
lowerCamelCase = parser.parse_args()
return args
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
try:
if args.tpu_name:
lowerCamelCase = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
lowerCamelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
"""Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
"""--gcp_project. When running on a TPU VM, use --tpu_name local.""" )
tf.config.experimental_connect_to_cluster(lowerCamelCase__ )
tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__ )
return tpu
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
lowerCamelCase = 0
for file in file_list:
lowerCamelCase = file.split("""/""" )[-1]
lowerCamelCase = re.search(R"""-\d+-(\d+)\.tfrecord""" , lowerCamelCase__ ).group(1 )
lowerCamelCase = int(lowerCamelCase__ )
num_samples += sample_count
return num_samples
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Any , lowerCamelCase__ : Dict , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : int=None ):
'''simple docstring'''
lowerCamelCase = count_samples(lowerCamelCase__ )
lowerCamelCase = tf.data.Dataset.from_tensor_slices(lowerCamelCase__ )
if shuffle:
lowerCamelCase = dataset.shuffle(len(lowerCamelCase__ ) )
lowerCamelCase = tf.data.TFRecordDataset(lowerCamelCase__ , num_parallel_reads=lowerCamelCase__ )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
lowerCamelCase = dataset.apply(tf.data.experimental.assert_cardinality(lowerCamelCase__ ) )
lowerCamelCase = dataset.map(lowerCamelCase__ , num_parallel_calls=lowerCamelCase__ )
if shuffle:
assert shuffle_buffer_size is not None
lowerCamelCase = dataset.shuffle(args.shuffle_buffer_size )
lowerCamelCase = dataset.batch(lowerCamelCase__ , drop_remainder=lowerCamelCase__ )
lowerCamelCase = dataset.map(lowerCamelCase__ , num_parallel_calls=lowerCamelCase__ )
lowerCamelCase = dataset.prefetch(lowerCamelCase__ )
return dataset
def __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
if not args.no_tpu:
lowerCamelCase = initialize_tpu(lowerCamelCase__ )
lowerCamelCase = tf.distribute.TPUStrategy(lowerCamelCase__ )
else:
lowerCamelCase = tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16""" )
lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer )
lowerCamelCase = AutoConfig.from_pretrained(args.pretrained_model_config )
lowerCamelCase = tokenizer.vocab_size
lowerCamelCase = tf.io.gfile.glob(os.path.join(args.train_dataset , """*.tfrecord""" ) )
if not training_records:
raise ValueError(f'No .tfrecord files found in {args.train_dataset}.' )
lowerCamelCase = tf.io.gfile.glob(os.path.join(args.eval_dataset , """*.tfrecord""" ) )
if not eval_records:
raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.' )
lowerCamelCase = count_samples(lowerCamelCase__ )
lowerCamelCase = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
lowerCamelCase = steps_per_epoch * args.num_epochs
with strategy.scope():
lowerCamelCase = TFAutoModelForMaskedLM.from_config(lowerCamelCase__ )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
lowerCamelCase , lowerCamelCase = create_optimizer(
num_train_steps=lowerCamelCase__ , num_warmup_steps=total_train_steps // 20 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=lowerCamelCase__ , metrics=["""accuracy"""] )
def decode_fn(lowerCamelCase__ : Optional[Any] ):
lowerCamelCase = {
"""input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
"""attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(lowerCamelCase__ , lowerCamelCase__ )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
lowerCamelCase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase__ , mlm_probability=args.mlm_probability , mlm=lowerCamelCase__ , return_tensors="""tf""" )
def mask_with_collator(lowerCamelCase__ : List[Any] ):
# TF really needs an isin() function
lowerCamelCase = (
~tf.cast(batch["""attention_mask"""] , tf.bool )
| (batch["""input_ids"""] == tokenizer.cls_token_id)
| (batch["""input_ids"""] == tokenizer.sep_token_id)
)
lowerCamelCase , lowerCamelCase = data_collator.tf_mask_tokens(
batch["""input_ids"""] , vocab_size=len(lowerCamelCase__ ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=lowerCamelCase__ , )
return batch
lowerCamelCase = args.per_replica_batch_size * strategy.num_replicas_in_sync
lowerCamelCase = prepare_dataset(
lowerCamelCase__ , decode_fn=lowerCamelCase__ , mask_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ , shuffle=lowerCamelCase__ , shuffle_buffer_size=args.shuffle_buffer_size , )
lowerCamelCase = prepare_dataset(
lowerCamelCase__ , decode_fn=lowerCamelCase__ , mask_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ , shuffle=lowerCamelCase__ , )
lowerCamelCase = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=lowerCamelCase__ ) )
model.fit(
lowerCamelCase__ , validation_data=lowerCamelCase__ , epochs=args.num_epochs , callbacks=lowerCamelCase__ , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
UpperCAmelCase : Tuple = parse_args()
main(args)
| 66
| 1
|
import random
from .binary_exp_mod import bin_exp_mod
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCamelCase : List[Any] = n - 1
lowerCamelCase : Dict = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowerCamelCase : Optional[Any] = 0
while count < prec:
lowerCamelCase : str = random.randint(2 ,n - 1 )
lowerCamelCase : Dict = bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if b != 1:
lowerCamelCase : str = True
for _ in range(_SCREAMING_SNAKE_CASE ):
if b == n - 1:
lowerCamelCase : Tuple = False
break
lowerCamelCase : int = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Optional[int] = abs(int(input('Enter bound : ').strip()))
print('Here\'s the list of primes:')
print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 48
|
_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
| 0
|
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
a : Optional[Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _A , _A , _A ):
a : Dict = 0
while b > 0:
if b & 1:
a : Any = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 96
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a__( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = CTRLTokenizer
lowercase__ = False
lowercase__ = False
def lowercase_ ( self : Dict ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a : Tuple = ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
a : Union[str, Any] = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
a : Union[str, Any] = ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
a : Optional[Any] = {'unk_token': '<unk>'}
a : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
a : Optional[Any] = 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(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def lowercase_ ( self : int , **__snake_case : str ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def lowercase_ ( self : Optional[int] , __snake_case : Any ):
a : int = 'adapt react readapt apt'
a : Any = 'adapt react readapt apt'
return input_text, output_text
def lowercase_ ( self : Dict ):
a : Dict = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
a : List[str] = 'adapt react readapt apt'
a : Dict = 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
a : Any = tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
a : Dict = tokens + [tokenizer.unk_token]
a : Optional[Any] = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
| 96
| 1
|
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Any = ''''''
_lowerCamelCase: int = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self : List[str] ,A_ : Optional[DatasetInfo] = None ,A_ : Optional[str] = None ,**A_ : Union[str, Any] ,) -> Dict:
super().__init__(self ,**A_ )
A = repo_info
A = token
A = None
def _SCREAMING_SNAKE_CASE ( self : int ) -> str:
if self.dir_cache is None:
A = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
A = {
'name': hf_file.rfilename,
'size': None,
'type': 'file',
}
self.dir_cache.update(
{
str(A_ ): {'name': str(A_ ), 'size': None, 'type': 'directory'}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : str ,A_ : str = "rb" ,**A_ : Dict ,) -> Union[str, Any]:
if not isinstance(self.repo_info ,A_ ):
raise NotImplementedError(F'Open is only implemented for dataset repositories, but got {self.repo_info}' )
A = hf_hub_url(self.repo_info.id ,A_ ,revision=self.repo_info.sha )
return fsspec.open(
A_ ,mode=A_ ,headers=get_authentication_headers_for_url(A_ ,use_auth_token=self.token ) ,client_kwargs={'trust_env': True} ,).open()
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : str ,**A_ : int ) -> Tuple:
self._get_dirs()
A = self._strip_protocol(A_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : Union[str, Any] ,A_ : str=False ,**A_ : Tuple ) -> str:
self._get_dirs()
A = PurePosixPath(path.strip('/' ) )
A = {}
for p, f in self.dir_cache.items():
A = PurePosixPath(p.strip('/' ) )
A = p.parent
if root == path:
A = f
A = list(paths.values() )
if detail:
return out
else:
return sorted(f['name'] for f in out )
| 74
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
),
}
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "pix2struct_text_model"
snake_case = ["past_key_values"]
snake_case = {
"hidden_size": "hidden_size",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , _SCREAMING_SNAKE_CASE=5_0244 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=1e-6 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE="gelu_new" , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , )->int:
'''simple docstring'''
A_ : Optional[int] = vocab_size
A_ : Any = hidden_size
A_ : Optional[Any] = d_kv
A_ : int = d_ff
A_ : int = num_layers
A_ : Dict = num_heads
A_ : Any = relative_attention_num_buckets
A_ : int = relative_attention_max_distance
A_ : Optional[Any] = dropout_rate
A_ : Optional[Any] = layer_norm_epsilon
A_ : List[Any] = initializer_factor
A_ : Optional[int] = use_cache
A_ : Optional[Any] = eos_token_id
A_ : List[Any] = decoder_start_token_id
# for backwards compatibility
A_ : int = dense_act_fn
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , tie_word_embeddings=_SCREAMING_SNAKE_CASE , is_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
@classmethod
def _snake_case ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->"PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE )
A_ , A_ : Union[str, Any] = cls.get_config_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
A_ : Any = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "pix2struct_vision_model"
def __init__( self , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE="gelu_new" , _SCREAMING_SNAKE_CASE=1e-6 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=1e-10 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=128 , **_SCREAMING_SNAKE_CASE , )->Optional[int]:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
A_ : Dict = hidden_size
A_ : Union[str, Any] = patch_embed_hidden_size
A_ : Optional[Any] = d_ff
A_ : Optional[Any] = dropout_rate
A_ : int = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : List[str] = initializer_range
A_ : List[str] = initializer_factor
A_ : Union[str, Any] = attention_dropout
A_ : Union[str, Any] = layer_norm_eps
A_ : Dict = dense_act_fn
A_ : Union[str, Any] = seq_len
A_ : Optional[Any] = relative_attention_num_buckets
A_ : Tuple = relative_attention_max_distance
A_ : List[Any] = d_kv
@classmethod
def _snake_case ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->"PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE )
A_ , A_ : str = cls.get_config_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
A_ : Any = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "pix2struct"
snake_case = True
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , )->Any:
'''simple docstring'''
super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE , is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text_config is None:
A_ : Dict = {}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
A_ : List[Any] = {}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
A_ : int = PixaStructTextConfig(**_SCREAMING_SNAKE_CASE )
A_ : Dict = PixaStructVisionConfig(**_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = self.text_config.decoder_start_token_id
A_ : Tuple = self.text_config.pad_token_id
A_ : Union[str, Any] = self.text_config.eos_token_id
A_ : str = initializer_factor
A_ : Tuple = initializer_range
A_ : List[str] = self.initializer_range
A_ : int = self.initializer_range
A_ : List[Any] = is_vqa
@classmethod
def _snake_case ( cls , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Dict:
'''simple docstring'''
A_ : List[Any] = copy.deepcopy(self.__dict__ )
A_ : Dict = self.text_config.to_dict()
A_ : int = self.vision_config.to_dict()
A_ : List[str] = self.__class__.model_type
return output
| 186
| 0
|
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 A (__A : List[str] ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
for line in lines:
UpperCAmelCase_ = re.sub(R'''#.*''' , '''''' , __A ) # remove comments
if line:
filtered_lines.append(__A )
UpperCAmelCase_ = '''\n'''.join(__A )
# Make a hash from all this code
UpperCAmelCase_ = full_str.encode('''utf-8''' )
return shaaaa(__A ).hexdigest()
# get importable module names and hash for caching
snake_case_ : Dict = {
"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
snake_case_ : 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})
snake_case_ : Tuple = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
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")
| 7
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = ["DeiTFeatureExtractor"]
snake_case_ : List[str] = ["DeiTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Dict = [
"TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
snake_case_ : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 7
| 1
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
lowerCAmelCase : str
lowerCAmelCase : List[str]
lowerCAmelCase : Optional[List[str]]
@dataclass
class __magic_name__ :
lowerCAmelCase : List[int]
lowerCAmelCase : List[int]
lowerCAmelCase : Optional[List[int]] = None
lowerCAmelCase : Optional[List[int]] = None
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : List[Any] = 'train'
lowerCAmelCase : List[str] = 'dev'
lowerCAmelCase : Optional[int] = 'test'
class __magic_name__ :
@staticmethod
def __lowercase ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Union[Split, str] ):
raise NotImplementedError
@staticmethod
def __lowercase ( _UpperCAmelCase : str ):
raise NotImplementedError
@staticmethod
def __lowercase ( _UpperCAmelCase : List[InputExample] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : int ,_UpperCAmelCase : PreTrainedTokenizer ,_UpperCAmelCase : Dict=False ,_UpperCAmelCase : int="[CLS]" ,_UpperCAmelCase : List[Any]=1 ,_UpperCAmelCase : Optional[int]="[SEP]" ,_UpperCAmelCase : Any=False ,_UpperCAmelCase : int=False ,_UpperCAmelCase : Tuple=0 ,_UpperCAmelCase : Any=0 ,_UpperCAmelCase : int=-100 ,_UpperCAmelCase : List[Any]=0 ,_UpperCAmelCase : str=True ,):
_a : str = {label: i for i, label in enumerate(_UpperCAmelCase )}
_a : str = []
for ex_index, example in enumerate(_UpperCAmelCase ):
if ex_index % 10000 == 0:
logger.info('Writing example %d of %d' ,_UpperCAmelCase ,len(_UpperCAmelCase ) )
_a : Optional[int] = []
_a : Union[str, Any] = []
for word, label in zip(example.words ,example.labels ):
_a : str = tokenizer.tokenize(_UpperCAmelCase )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(_UpperCAmelCase ) > 0:
tokens.extend(_UpperCAmelCase )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_UpperCAmelCase ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
_a : List[Any] = tokenizer.num_special_tokens_to_add()
if len(_UpperCAmelCase ) > max_seq_length - special_tokens_count:
_a : Optional[Any] = tokens[: (max_seq_length - special_tokens_count)]
_a : Optional[int] = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
_a : str = [sequence_a_segment_id] * len(_UpperCAmelCase )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
_a : List[Any] = [cls_token] + tokens
_a : Any = [pad_token_label_id] + label_ids
_a : List[str] = [cls_token_segment_id] + segment_ids
_a : Union[str, Any] = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
_a : Dict = [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase )
# Zero-pad up to the sequence length.
_a : int = max_seq_length - len(_UpperCAmelCase )
if pad_on_left:
_a : Optional[Any] = ([pad_token] * padding_length) + input_ids
_a : str = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
_a : str = ([pad_token_segment_id] * padding_length) + segment_ids
_a : Optional[int] = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(_UpperCAmelCase ) == max_seq_length
assert len(_UpperCAmelCase ) == max_seq_length
assert len(_UpperCAmelCase ) == max_seq_length
assert len(_UpperCAmelCase ) == max_seq_length
if ex_index < 5:
logger.info('*** Example ***' )
logger.info('guid: %s' ,example.guid )
logger.info('tokens: %s' ,' '.join([str(_UpperCAmelCase ) for x in tokens] ) )
logger.info('input_ids: %s' ,' '.join([str(_UpperCAmelCase ) for x in input_ids] ) )
logger.info('input_mask: %s' ,' '.join([str(_UpperCAmelCase ) for x in input_mask] ) )
logger.info('segment_ids: %s' ,' '.join([str(_UpperCAmelCase ) for x in segment_ids] ) )
logger.info('label_ids: %s' ,' '.join([str(_UpperCAmelCase ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
_a : Union[str, Any] = None
features.append(
InputFeatures(
input_ids=_UpperCAmelCase ,attention_mask=_UpperCAmelCase ,token_type_ids=_UpperCAmelCase ,label_ids=_UpperCAmelCase ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : List[InputFeatures]
lowerCAmelCase : int = nn.CrossEntropyLoss().ignore_index
def __init__( self : int ,_UpperCAmelCase : TokenClassificationTask ,_UpperCAmelCase : str ,_UpperCAmelCase : PreTrainedTokenizer ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : List[Any]=False ,_UpperCAmelCase : Split = Split.train ,):
# Load data features from cache or dataset file
_a : str = os.path.join(
_UpperCAmelCase ,'cached_{}_{}_{}'.format(mode.value ,tokenizer.__class__.__name__ ,str(_UpperCAmelCase ) ) ,)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_a : Tuple = cached_features_file + '.lock'
with FileLock(_UpperCAmelCase ):
if os.path.exists(_UpperCAmelCase ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
_a : int = torch.load(_UpperCAmelCase )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
_a : List[str] = token_classification_task.read_examples_from_file(_UpperCAmelCase ,_UpperCAmelCase )
# TODO clean up all this to leverage built-in features of tokenizers
_a : List[str] = token_classification_task.convert_examples_to_features(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,cls_token_at_end=bool(model_type in ['xlnet'] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=_UpperCAmelCase ,pad_on_left=bool(tokenizer.padding_side == 'left' ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
logger.info(F"""Saving features into cached file {cached_features_file}""" )
torch.save(self.features ,_UpperCAmelCase )
def __len__( self : Any ):
return len(self.features )
def __getitem__( self : Tuple ,_UpperCAmelCase : Dict ):
return self.features[i]
if is_tf_available():
import tensorflow as tf
class __magic_name__ :
lowerCAmelCase : List[InputFeatures]
lowerCAmelCase : int = -1_0_0
def __init__( self : str ,_UpperCAmelCase : TokenClassificationTask ,_UpperCAmelCase : str ,_UpperCAmelCase : PreTrainedTokenizer ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : int=False ,_UpperCAmelCase : Split = Split.train ,):
_a : List[Any] = token_classification_task.read_examples_from_file(_UpperCAmelCase ,_UpperCAmelCase )
# TODO clean up all this to leverage built-in features of tokenizers
_a : Dict = token_classification_task.convert_examples_to_features(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,cls_token_at_end=bool(model_type in ['xlnet'] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=_UpperCAmelCase ,pad_on_left=bool(tokenizer.padding_side == 'left' ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
_a : Optional[Any] = tf.data.Dataset.from_generator(
_UpperCAmelCase ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,(
{'input_ids': tf.TensorShape([None] ), 'attention_mask': tf.TensorShape([None] )},
tf.TensorShape([None] ),
) ,)
else:
_a : str = tf.data.Dataset.from_generator(
_UpperCAmelCase ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,(
{
'input_ids': tf.TensorShape([None] ),
'attention_mask': tf.TensorShape([None] ),
'token_type_ids': tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) ,)
def __lowercase ( self : str ):
_a : Optional[int] = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : Optional[Any] ):
return len(self.features )
def __getitem__( self : str ,_UpperCAmelCase : int ):
return self.features[i]
| 89
|
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) -> Dict:
lowerCamelCase = torch.load(snake_case__ , map_location="""cpu""" )
if "model" in sd.keys():
lowerCamelCase = torch.load(snake_case__ , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
lowerCamelCase = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(snake_case__ )
lowerCamelCase = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
lowerCamelCase = sd.pop(snake_case__ )
lowerCamelCase = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
lowerCamelCase = sd[key]
# We split QKV in separate Q,K,V
lowerCamelCase = key.replace(""".qkv_proj.""" , """.q_proj.""" )
lowerCamelCase = key.replace(""".qkv_proj.""" , """.k_proj.""" )
lowerCamelCase = key.replace(""".qkv_proj.""" , """.v_proj.""" )
lowerCamelCase = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
lowerCamelCase , lowerCamelCase , lowerCamelCase = torch.split(snake_case__ , depth // 3 , dim=0 )
lowerCamelCase = q
lowerCamelCase = k
lowerCamelCase = v
del sd[key]
return sd
@torch.no_grad()
def a__ ( snake_case__ , snake_case__ , snake_case__=None ) -> Tuple:
lowerCamelCase = load_checkpoint(snake_case__ )
if config is not None:
lowerCamelCase = OPTConfig.from_pretrained(snake_case__ )
else:
lowerCamelCase = OPTConfig()
lowerCamelCase = OPTModel(snake_case__ ).half().eval()
model.load_state_dict(snake_case__ )
# Check results
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
model.save_pretrained(snake_case__ )
if __name__ == "__main__":
lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 291
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : int = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Dict = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
a__ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 362
|
"""simple docstring"""
a__ : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = input("Enter message: " )
__SCREAMING_SNAKE_CASE = input("Enter key [alphanumeric]: " )
__SCREAMING_SNAKE_CASE = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
__SCREAMING_SNAKE_CASE = "encrypt"
__SCREAMING_SNAKE_CASE = encrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
elif mode.lower().startswith("d" ):
__SCREAMING_SNAKE_CASE = "decrypt"
__SCREAMING_SNAKE_CASE = decrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
print(f"""\n{mode.title()}ed message:""" )
print(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "encrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "decrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = key.upper()
for symbol in message:
__SCREAMING_SNAKE_CASE = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(lowerCAmelCase_ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = 0
else:
translated.append(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 195
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__a = 25_00_04
__a = 25_00_20
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : List[str] = MBartTokenizer
_A : str = MBartTokenizerFast
_A : Union[str, Any] = True
_A : List[Any] = True
def lowerCAmelCase_ ( self: Optional[Any] ) -> str:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ :Tuple = MBartTokenizer(snake_case , keep_accents=snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> List[str]:
snake_case_ :str = MBartTokenizer(snake_case , keep_accents=snake_case )
snake_case_ :int = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(snake_case , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ :Union[str, Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
snake_case , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
snake_case_ :Union[str, Any] = tokenizer.convert_tokens_to_ids(snake_case )
self.assertListEqual(
snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ :Dict = tokenizer.convert_ids_to_tokens(snake_case )
self.assertListEqual(
snake_case , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def lowerCAmelCase_ ( self: Optional[int] ) -> int:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ :List[str] = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case_ :Optional[int] = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
snake_case_ :Optional[Any] = self.tokenizer_class.from_pretrained(snake_case , **snake_case )
snake_case_ :Dict = tempfile.mkdtemp()
snake_case_ :str = tokenizer_r.save_pretrained(snake_case )
snake_case_ :int = tokenizer_p.save_pretrained(snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
snake_case_ :int = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(snake_case , snake_case )
# Checks everything loads correctly in the same way
snake_case_ :Dict = tokenizer_r.from_pretrained(snake_case )
snake_case_ :Optional[int] = tokenizer_p.from_pretrained(snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case , snake_case ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(snake_case )
# Save tokenizer rust, legacy_format=True
snake_case_ :str = tempfile.mkdtemp()
snake_case_ :Union[str, Any] = tokenizer_r.save_pretrained(snake_case , legacy_format=snake_case )
snake_case_ :Any = tokenizer_p.save_pretrained(snake_case )
# Checks it save with the same files
self.assertSequenceEqual(snake_case , snake_case )
# Checks everything loads correctly in the same way
snake_case_ :Union[str, Any] = tokenizer_r.from_pretrained(snake_case )
snake_case_ :Dict = tokenizer_p.from_pretrained(snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case , snake_case ) )
shutil.rmtree(snake_case )
# Save tokenizer rust, legacy_format=False
snake_case_ :Tuple = tempfile.mkdtemp()
snake_case_ :Tuple = tokenizer_r.save_pretrained(snake_case , legacy_format=snake_case )
snake_case_ :int = tokenizer_p.save_pretrained(snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ :Tuple = tokenizer_r.from_pretrained(snake_case )
snake_case_ :List[Any] = tokenizer_p.from_pretrained(snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case , snake_case ) )
shutil.rmtree(snake_case )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
_A : Optional[Any] = """facebook/mbart-large-en-ro"""
_A : str = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
_A : List[str] = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
_A : Tuple = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def lowerCAmelCase_ ( cls: Optional[int] ) -> str:
snake_case_ :MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
snake_case_ :List[Any] = 1
return cls
def lowerCAmelCase_ ( self: Optional[int] ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 250_001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 250_004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 250_020 )
def lowerCAmelCase_ ( self: Dict ) -> Dict:
snake_case_ :List[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Optional[int]:
self.assertIn(snake_case , self.tokenizer.all_special_ids )
snake_case_ :int = [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
snake_case_ :Any = self.tokenizer.decode(snake_case , skip_special_tokens=snake_case )
snake_case_ :Optional[int] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
self.assertNotIn(self.tokenizer.eos_token , snake_case )
def lowerCAmelCase_ ( self: Dict ) -> str:
snake_case_ :Optional[Any] = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , snake_case )
snake_case_ :Optional[int] = 10
snake_case_ :List[str] = self.tokenizer(snake_case , max_length=snake_case , truncation=snake_case ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , snake_case )
self.assertEqual(len(snake_case ) , snake_case )
def lowerCAmelCase_ ( self: Tuple ) -> Tuple:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [250_026, 250_001] )
def lowerCAmelCase_ ( self: Optional[int] ) -> Optional[Any]:
snake_case_ :List[Any] = tempfile.mkdtemp()
snake_case_ :int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case )
snake_case_ :Dict = MBartTokenizer.from_pretrained(snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case )
@require_torch
def lowerCAmelCase_ ( self: List[str] ) -> List[str]:
snake_case_ :str = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=snake_case , return_tensors="""pt""" )
snake_case_ :str = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def lowerCAmelCase_ ( self: List[Any] ) -> Any:
snake_case_ :Union[str, Any] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case , truncation=snake_case , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
snake_case_ :int = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
snake_case_ :List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def lowerCAmelCase_ ( self: Dict ) -> List[str]:
snake_case_ :Dict = self.tokenizer(self.src_text , padding=snake_case , truncation=snake_case , max_length=3 , return_tensors="""pt""" )
snake_case_ :Dict = self.tokenizer(
text_target=self.tgt_text , padding=snake_case , truncation=snake_case , max_length=10 , return_tensors="""pt""" )
snake_case_ :Any = targets["""input_ids"""]
snake_case_ :str = shift_tokens_right(snake_case , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCAmelCase_ ( self: Dict ) -> Tuple:
snake_case_ :Optional[Any] = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(snake_case ) , {
# A, test, EOS, en_XX
"""input_ids""": [[62, 3_034, 2, 250_004]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 250_001,
} , )
| 66
|
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: Any ) -> str:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case , )
assert hasattr(self , """env""" )
def lowerCAmelCase_ ( self: int , snake_case: Dict ) -> List[Any]:
# configuration for running training on smdistributed Model Parallel
snake_case_ :Tuple = {
"""enabled""": True,
"""processes_per_host""": 8,
}
snake_case_ :List[Any] = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
snake_case_ :Tuple = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
snake_case_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case , instance_type=self.instance_type , debugger_hook_config=snake_case , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case , py_version="""py36""" , )
def lowerCAmelCase_ ( self: Any , snake_case: Tuple ) -> List[str]:
TrainingJobAnalytics(snake_case ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def lowerCAmelCase_ ( self: Dict , snake_case: Dict ) -> List[Any]:
# create estimator
snake_case_ :List[Any] = self.create_estimator(snake_case )
# run training
estimator.fit()
# result dataframe
snake_case_ :Any = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
snake_case_ :Tuple = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
snake_case_ :Dict = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
snake_case_ :int = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case )
| 66
| 1
|
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _UpperCamelCase ( UpperCamelCase__ = "https://www.worldometers.info/coronavirus" ):
UpperCAmelCase__ : List[str] = BeautifulSoup(requests.get(__a ).text , """html.parser""" )
UpperCAmelCase__ : Dict = soup.findAll("""h1""" )
UpperCAmelCase__ : Union[str, Any] = soup.findAll("""div""" , {"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" , {"""class""": """panel-title"""} )
values += soup.findAll("""div""" , {"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(__a , __a )}
if __name__ == "__main__":
print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
for key, value in world_covidaa_stats().items():
print(f"""{key}\n{value}\n""")
| 355
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A ={
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2m_100': ['M2M100Tokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
'M2M100ForConditionalGeneration',
'M2M100Model',
'M2M100PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
__A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 283
| 0
|
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowercase__ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class lowerCAmelCase__ ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ = None
def _snake_case ( lowercase__ , lowercase__ , ):
import pyspark
def generate_fn():
_lowerCamelCase : Tuple = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
_lowerCamelCase : Optional[Any] = df_with_partition_id.select('*' ).where(f'''part_id = {partition_id}''' ).drop('part_id' )
_lowerCamelCase : int = partition_df.collect()
_lowerCamelCase : List[Any] = 0
for row in rows:
yield f'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class lowerCAmelCase__ ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self , lowercase , lowercase=None , ):
_lowerCamelCase : Any = df
_lowerCamelCase : List[Any] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowerCamelCase : Union[str, Any] = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def A_ ( self , lowercase ):
_lowerCamelCase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowercase )
return SparkExamplesIterable(self.df , partition_order=lowercase )
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : str = self.split_shard_indices_by_worker(lowercase , lowercase )
return SparkExamplesIterable(self.df , partition_order=lowercase )
@property
def A_ ( self ):
return len(self.partition_order )
class lowerCAmelCase__ ( datasets.DatasetBuilder ):
'''simple docstring'''
lowerCamelCase__ = SparkConfig
def __init__( self , lowercase , lowercase = None , lowercase = None , **lowercase , ):
import pyspark
_lowerCamelCase : Dict = pyspark.sql.SparkSession.builder.getOrCreate()
_lowerCamelCase : List[Any] = df
_lowerCamelCase : List[Any] = working_dir
super().__init__(
cache_dir=lowercase , config_name=str(self.df.semanticHash() ) , **lowercase , )
def A_ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(lowercase ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=lowercase )
_lowerCamelCase : List[str] = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowercase , 'a' )
return [probe_file]
if self._spark.conf.get('spark.master' , '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowerCamelCase : int = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(lowercase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def A_ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def A_ ( self , lowercase ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def A_ ( self , lowercase ):
import pyspark
def get_arrow_batch_size(lowercase ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
_lowerCamelCase : int = self.df.count()
_lowerCamelCase : Dict = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowerCamelCase : Optional[int] = (
self.df.limit(lowercase )
.repartition(1 )
.mapInArrow(lowercase , 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowerCamelCase : Tuple = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowerCamelCase : Dict = min(lowercase , int(approx_total_size / max_shard_size ) )
_lowerCamelCase : List[str] = self.df.repartition(lowercase )
def A_ ( self , lowercase , lowercase , lowercase , ):
import pyspark
_lowerCamelCase : List[Any] = ParquetWriter if file_format == 'parquet' else ArrowWriter
_lowerCamelCase : List[Any] = os.path.join(self._working_dir , os.path.basename(lowercase ) ) if self._working_dir else fpath
_lowerCamelCase : Union[str, Any] = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowerCamelCase : Optional[int] = self.config.features
_lowerCamelCase : Union[str, Any] = self._writer_batch_size
_lowerCamelCase : int = self._fs.storage_options
def write_arrow(lowercase ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowerCamelCase : Dict = pyspark.TaskContext().taskAttemptId()
_lowerCamelCase : List[Any] = next(lowercase , lowercase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , )
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : Tuple = writer_class(
features=lowercase , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=lowercase , storage_options=lowercase , embed_local_files=lowercase , )
_lowerCamelCase : int = pa.Table.from_batches([first_batch] )
writer.write_table(lowercase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowerCamelCase, _lowerCamelCase : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
shard_id += 1
_lowerCamelCase : int = writer_class(
features=writer._features , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=lowercase , storage_options=lowercase , embed_local_files=lowercase , )
_lowerCamelCase : Optional[int] = pa.Table.from_batches([batch] )
writer.write_table(lowercase )
if writer._num_bytes > 0:
_lowerCamelCase, _lowerCamelCase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowercase ) ):
_lowerCamelCase : Dict = os.path.join(os.path.dirname(lowercase ) , os.path.basename(lowercase ) )
shutil.move(lowercase , lowercase )
_lowerCamelCase : Any = (
self.df.mapInArrow(lowercase , 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def A_ ( self , lowercase , lowercase = "arrow" , lowercase = None , lowercase = None , **lowercase , ):
self._validate_cache_dir()
_lowerCamelCase : Optional[Any] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowercase )
_lowerCamelCase : Optional[Any] = not is_remote_filesystem(self._fs )
_lowerCamelCase : int = os.path.join if is_local else posixpath.join
_lowerCamelCase : Optional[Any] = '-TTTTT-SSSSS-of-NNNNN'
_lowerCamelCase : Any = F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_lowerCamelCase : Any = path_join(self._output_dir , lowercase )
_lowerCamelCase : Dict = 0
_lowerCamelCase : Dict = 0
_lowerCamelCase : Dict = 0
_lowerCamelCase : int = []
_lowerCamelCase : List[Any] = []
for task_id, content in self._prepare_split_single(lowercase , lowercase , lowercase ):
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : str = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowercase )
_lowerCamelCase : int = total_num_examples
_lowerCamelCase : Optional[int] = total_num_bytes
# should rename everything at the end
logger.debug(F'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_lowerCamelCase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowerCamelCase : Optional[int] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowercase , lowercase , lowercase , ):
rename(
lowercase , fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace('TTTTT-SSSSS' , F'''{global_shard_id:05d}''' ).replace('NNNNN' , F'''{total_shards:05d}''' ) , )
_lowerCamelCase : List[Any] = []
_lowerCamelCase : Union[str, Any] = 0
for i in range(len(lowercase ) ):
_lowerCamelCase, _lowerCamelCase : List[str] = task_id_and_num_shards[i]
for shard_id in range(lowercase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowercase , len(lowercase ) ).map(lambda lowercase : _rename_shard(*lowercase ) ).collect()
else:
# don't use any pattern
_lowerCamelCase : Dict = 0
_lowerCamelCase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace(lowercase , '' ) , )
def A_ ( self , lowercase , ):
return SparkExamplesIterable(self.df )
| 96
|
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor""", """tokenizer"""]
lowerCamelCase__ = """BlipImageProcessor"""
lowerCamelCase__ = """AutoTokenizer"""
def __init__( self , lowercase , lowercase , lowercase ):
super().__init__(lowercase , lowercase )
# add QFormer tokenizer
_lowerCamelCase : int = qformer_tokenizer
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError('You have to specify at least images or text.' )
_lowerCamelCase : int = BatchFeature()
if text is not None:
_lowerCamelCase : List[str] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
encoding.update(lowercase )
_lowerCamelCase : List[str] = self.qformer_tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
_lowerCamelCase : List[Any] = qformer_text_encoding.pop('input_ids' )
_lowerCamelCase : Tuple = qformer_text_encoding.pop('attention_mask' )
if images is not None:
_lowerCamelCase : int = self.image_processor(lowercase , return_tensors=lowercase )
encoding.update(lowercase )
return encoding
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A_ ( self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = self.tokenizer.model_input_names
_lowerCamelCase : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A_ ( self , lowercase , **lowercase ):
if os.path.isfile(lowercase ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase , exist_ok=lowercase )
_lowerCamelCase : Optional[Any] = os.path.join(lowercase , 'qformer_tokenizer' )
self.qformer_tokenizer.save_pretrained(lowercase )
return super().save_pretrained(lowercase , **lowercase )
@classmethod
def A_ ( cls , lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(lowercase , subfolder='qformer_tokenizer' )
_lowerCamelCase : Dict = cls._get_arguments_from_pretrained(lowercase , **lowercase )
args.append(lowercase )
return cls(*lowercase )
| 96
| 1
|
"""simple docstring"""
from math import factorial
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 20 )-> int:
_lowerCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_lowerCamelCase = n // 2
return int(factorial(snake_case ) / (factorial(snake_case ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
A_ : Optional[Any] =int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number.""")
| 363
|
"""simple docstring"""
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_500_000 )-> int:
_lowerCamelCase = defaultdict(snake_case )
_lowerCamelCase = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , snake_case , 2 ):
if gcd(snake_case , snake_case ) > 1:
continue
_lowerCamelCase = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(snake_case , limit + 1 , snake_case ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f'{solution() = }')
| 80
| 0
|
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 _snake_case( SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
'''simple docstring'''
A__ = []
for line in lines:
A__ = re.sub(R'#.*' , '' , SCREAMING_SNAKE_CASE__ ) # remove comments
if line:
filtered_lines.append(SCREAMING_SNAKE_CASE__ )
A__ = '\n'.join(SCREAMING_SNAKE_CASE__ )
# Make a hash from all this code
A__ = full_str.encode('utf-8' )
return shaaaa(SCREAMING_SNAKE_CASE__ ).hexdigest()
# get importable module names and hash for caching
lowercase_ = {
"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
lowercase_ = {
".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})
lowercase_ = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
lowercase_ = {}
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")
| 7
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'open-llama'
def __init__( self : Any,lowercase_ : Optional[int]=1_0_0_0_0_0,lowercase_ : Union[str, Any]=4_0_9_6,lowercase_ : Dict=1_1_0_0_8,lowercase_ : Dict=3_2,lowercase_ : Optional[int]=3_2,lowercase_ : Dict="silu",lowercase_ : Union[str, Any]=2_0_4_8,lowercase_ : Optional[int]=0.02,lowercase_ : Dict=1E-6,lowercase_ : Dict=True,lowercase_ : List[Any]=0,lowercase_ : Optional[int]=1,lowercase_ : str=2,lowercase_ : str=False,lowercase_ : str=True,lowercase_ : int=0.1,lowercase_ : List[Any]=0.1,lowercase_ : List[Any]=True,lowercase_ : Union[str, Any]=True,lowercase_ : Any=None,**lowercase_ : List[Any],)-> Tuple:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = intermediate_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = initializer_range
A__ = rms_norm_eps
A__ = use_cache
A__ = kwargs.pop(
'use_memorry_efficient_attention',lowercase_ )
A__ = hidden_dropout_prob
A__ = attention_dropout_prob
A__ = use_stable_embedding
A__ = shared_input_output_embedding
A__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)
def snake_case__ ( self : str )-> str:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling,lowercase_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'got {self.rope_scaling}' )
A__ = self.rope_scaling.get('type',lowercase_ )
A__ = self.rope_scaling.get('factor',lowercase_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 7
| 1
|
"""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
_UpperCamelCase: Optional[int] = logging.get_logger(__name__)
_UpperCamelCase: Union[str, 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'),
]
)
_UpperCamelCase: int = 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'),
]
)
_UpperCamelCase: 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'),
]
)
_UpperCamelCase: Any = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('bart', 'FlaxBartForConditionalGeneration'),
('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
('encoder-decoder', 'FlaxEncoderDecoderModel'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('marian', 'FlaxMarianMTModel'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('pegasus', 'FlaxPegasusForConditionalGeneration'),
('t5', 'FlaxT5ForConditionalGeneration'),
]
)
_UpperCamelCase: List[str] = OrderedDict(
[
# Model for Image-classsification
('beit', 'FlaxBeitForImageClassification'),
('regnet', 'FlaxRegNetForImageClassification'),
('resnet', 'FlaxResNetForImageClassification'),
('vit', 'FlaxViTForImageClassification'),
]
)
_UpperCamelCase: Tuple = OrderedDict(
[
('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
]
)
_UpperCamelCase: Dict = 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'),
]
)
_UpperCamelCase: Union[str, 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'),
]
)
_UpperCamelCase: int = 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'),
]
)
_UpperCamelCase: int = 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'),
]
)
_UpperCamelCase: 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'),
]
)
_UpperCamelCase: int = OrderedDict(
[
('bert', 'FlaxBertForNextSentencePrediction'),
]
)
_UpperCamelCase: Optional[int] = OrderedDict(
[
('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
]
)
_UpperCamelCase: int = OrderedDict(
[
('whisper', 'FlaxWhisperForAudioClassification'),
]
)
_UpperCamelCase: int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_UpperCamelCase: List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_UpperCamelCase: Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_UpperCamelCase: int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_UpperCamelCase: str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase: Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_UpperCamelCase: str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_UpperCamelCase: Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase: Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_UpperCamelCase: Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase: List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_UpperCamelCase: Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_UpperCamelCase: Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_UpperCamelCase: List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_MAPPING
_UpperCamelCase: Dict = auto_class_update(FlaxAutoModel)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
_UpperCamelCase: int = auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_UpperCamelCase: List[str] = auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
_UpperCamelCase: int = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCamelCase: Tuple = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_UpperCamelCase: int = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_UpperCamelCase: List[Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_UpperCamelCase: Optional[Any] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_UpperCamelCase: Union[str, Any] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_UpperCamelCase: Any = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_UpperCamelCase: Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='image classification'
)
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_UpperCamelCase: List[Any] = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class a__ ( _BaseAutoModelClass ):
_lowerCamelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_UpperCamelCase: Optional[Any] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)
| 53
|
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
_UpperCamelCase: Any = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
|
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 A_ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
return None
class A_ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
return None
class A_ ( unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = [
# (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 ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(snake_case , 'tf' , 12 , **snake_case )
@require_torch
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(snake_case , 'pt' , 12 , **snake_case )
@require_torch
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
from transformers import BertModel
lowercase = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(snake_case ) )
vocab_file.flush()
lowercase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowercase = BertModel(BertConfig(vocab_size=len(snake_case ) ) )
model.save_pretrained(snake_case )
self._test_export(snake_case , 'pt' , 12 , snake_case )
@require_tf
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase = self._test_export(snake_case , 'tf' , 12 , **snake_case )
lowercase = quantize(Path(snake_case ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(snake_case ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase = self._test_export(snake_case , 'pt' , 12 , **snake_case )
lowercase = quantize(snake_case )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(snake_case ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case=None , **snake_case ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowercase = Path(snake_case ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(snake_case , snake_case , snake_case , snake_case , snake_case , **snake_case )
return path
except Exception as e:
self.fail(snake_case )
@require_torch
@require_tokenizers
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
from transformers import BertModel
lowercase = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowercase = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(snake_case , snake_case , 'pt' )
@require_tf
@require_tokenizers
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
from transformers import TFBertModel
lowercase = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowercase = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(snake_case , snake_case , 'tf' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
lowercase = FeatureExtractionPipeline(snake_case , snake_case )
lowercase = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
lowercase , lowercase , lowercase , lowercase = infer_shapes(snake_case , snake_case )
# Assert all variables are present
self.assertEqual(len(snake_case ) , len(snake_case ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , snake_case )
self.assertSequenceEqual(variable_names[3:] , 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 ):
lowercase = ['input_ids', 'attention_mask', 'token_type_ids']
lowercase = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
lowercase , lowercase = ensure_valid_input(FuncContiguousArgs() , snake_case , snake_case )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(snake_case ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(snake_case ) , set(snake_case ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(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)
lowercase , lowercase = ensure_valid_input(FuncNonContiguousArgs() , snake_case , snake_case )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(snake_case ) , 1 )
self.assertEqual(len(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 ):
lowercase = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ) , '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx' , generated.as_posix() )
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| 0
|
import re
def UpperCAmelCase ( _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()
| 256
|
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 ):
A : Any = []
for line in lines:
A : List[str] = re.sub(R"#.*" , "" , _lowerCamelCase ) # remove comments
if line:
filtered_lines.append(_lowerCamelCase )
A : str = "\n".join(_lowerCamelCase )
# Make a hash from all this code
A : Any = full_str.encode("utf-8" )
return shaaaa(_lowerCamelCase ).hexdigest()
# get importable module names and hash for caching
__SCREAMING_SNAKE_CASE = {
"""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 = {
""".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 = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
__SCREAMING_SNAKE_CASE = {}
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""")
| 256
| 1
|
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_snake_case = get_logger(__name__)
_snake_case = Path(__file__).parent / '''model_card_template.md'''
_snake_case = uuida().hex
_snake_case = os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
_snake_case = os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
_snake_case = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''
def lowercase_( SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
lowerCamelCase : Tuple = f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"""; torch/{_torch_version}"""
if is_flax_available():
ua += f"""; jax/{_jax_version}"""
ua += f"""; flax/{_flax_version}"""
if is_onnx_available():
ua += f"""; onnxruntime/{_onnxruntime_version}"""
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" , "" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items() )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
ua += "; " + user_agent
return ua
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if token is None:
lowerCamelCase : List[str] = HfFolder.get_token()
if organization is None:
lowerCamelCase : Union[str, Any] = whoami(SCREAMING_SNAKE_CASE_ )["name"]
return f"""{username}/{model_id}"""
else:
return f"""{organization}/{model_id}"""
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(SCREAMING_SNAKE_CASE_ , "local_rank" ) and args.local_rank not in [-1, 0]:
return
lowerCamelCase : Optional[int] = args.hub_token if hasattr(SCREAMING_SNAKE_CASE_ , "hub_token" ) else None
lowerCamelCase : Dict = get_full_repo_name(SCREAMING_SNAKE_CASE_ , token=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : str = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" , license="apache-2.0" , library_name="diffusers" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=SCREAMING_SNAKE_CASE_ , model_name=SCREAMING_SNAKE_CASE_ , repo_name=SCREAMING_SNAKE_CASE_ , dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE_ , "dataset_name" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE_ , "gradient_accumulation_steps" ) else None
) , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_ , "adam_beta1" ) else None , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_ , "adam_beta2" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE_ , "adam_weight_decay" ) else None , adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE_ , "adam_epsilon" ) else None , lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE_ , "lr_scheduler" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE_ , "lr_warmup_steps" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE_ , "ema_inv_gamma" ) else None , ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE_ , "ema_power" ) else None , ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE_ , "ema_max_decay" ) else None , mixed_precision=args.mixed_precision , )
lowerCamelCase : Dict = os.path.join(args.output_dir , "README.md" )
model_card.save(SCREAMING_SNAKE_CASE_ )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
lowerCamelCase : str = str(Path(SCREAMING_SNAKE_CASE_ ).as_posix() )
lowerCamelCase : Optional[int] = re.search(r"snapshots/([^/]+)/" , SCREAMING_SNAKE_CASE_ )
if search is None:
return None
lowerCamelCase : str = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE_ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_snake_case = os.path.expanduser(
os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
_snake_case = os.path.join(hf_cache_home, '''diffusers''')
def lowercase_( SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if new_cache_dir is None:
lowerCamelCase : List[Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
lowerCamelCase : Any = old_diffusers_cache
lowerCamelCase : Union[str, Any] = Path(SCREAMING_SNAKE_CASE_ ).expanduser()
lowerCamelCase : str = Path(SCREAMING_SNAKE_CASE_ ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
lowerCamelCase : Dict = new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE_ )
new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
os.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
try:
os.symlink(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_snake_case = os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
_snake_case = 0
else:
with open(cache_version_file) as f:
try:
_snake_case = int(f.read())
except ValueError:
_snake_case = 0
if cache_version < 1:
_snake_case = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
'''existing cached models. This is a one-time operation, you can interrupt it or run it '''
'''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
)
try:
move_cache()
except Exception as e:
_snake_case = '''\n'''.join(traceback.format_tb(e.__traceback__))
logger.error(
f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
'''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
'''message and we will do our best to help.'''
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, '''w''') as f:
f.write('''1''')
except Exception:
logger.warning(
f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
'''the directory exists and can be written to.'''
)
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if variant is not None:
lowerCamelCase : Optional[int] = weights_name.split("." )
lowerCamelCase : Optional[int] = splits[:-1] + [variant] + splits[-1:]
lowerCamelCase : Optional[Any] = ".".join(SCREAMING_SNAKE_CASE_ )
return weights_name
def lowercase_( SCREAMING_SNAKE_CASE_ , *,
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , ):
'''simple docstring'''
lowerCamelCase : str = str(SCREAMING_SNAKE_CASE_ )
if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
return pretrained_model_name_or_path
elif os.path.isdir(SCREAMING_SNAKE_CASE_ ):
if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ):
# Load from a PyTorch checkpoint
lowerCamelCase : int = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ):
lowerCamelCase : str = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return model_file
else:
raise EnvironmentError(
f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.""" )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(SCREAMING_SNAKE_CASE_ ).base_version ) >= version.parse("0.20.0" )
):
try:
lowerCamelCase : List[str] = hf_hub_download(
SCREAMING_SNAKE_CASE_ , filename=_add_variant(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , cache_dir=SCREAMING_SNAKE_CASE_ , force_download=SCREAMING_SNAKE_CASE_ , proxies=SCREAMING_SNAKE_CASE_ , resume_download=SCREAMING_SNAKE_CASE_ , local_files_only=SCREAMING_SNAKE_CASE_ , use_auth_token=SCREAMING_SNAKE_CASE_ , user_agent=SCREAMING_SNAKE_CASE_ , subfolder=SCREAMING_SNAKE_CASE_ , revision=revision or commit_hash , )
warnings.warn(
f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.""" , SCREAMING_SNAKE_CASE_ , )
return model_file
except: # noqa: E722
warnings.warn(
f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )}' so that the correct variant file can be added.""" , SCREAMING_SNAKE_CASE_ , )
try:
# 2. Load model file as usual
lowerCamelCase : Any = hf_hub_download(
SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , force_download=SCREAMING_SNAKE_CASE_ , proxies=SCREAMING_SNAKE_CASE_ , resume_download=SCREAMING_SNAKE_CASE_ , local_files_only=SCREAMING_SNAKE_CASE_ , use_auth_token=SCREAMING_SNAKE_CASE_ , user_agent=SCREAMING_SNAKE_CASE_ , subfolder=SCREAMING_SNAKE_CASE_ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
"this model name. Check the model page at "
f"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.""" )
except EntryNotFoundError:
raise EnvironmentError(
f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.""" )
except HTTPError as err:
raise EnvironmentError(
f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}""" )
except ValueError:
raise EnvironmentError(
f"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
f""" directory containing a file named {weights_name} or"""
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
f"""containing a file named {weights_name}""" )
| 283
|
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 , 0 , -1 ):
lowerCamelCase : Tuple = False
for j in range(SCREAMING_SNAKE_CASE_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lowerCamelCase , lowerCamelCase : int = unsorted[j - 1], unsorted[j]
lowerCamelCase : Optional[int] = True
for j in range(SCREAMING_SNAKE_CASE_ ):
if unsorted[j] > unsorted[j + 1]:
lowerCamelCase , lowerCamelCase : Union[str, Any] = unsorted[j + 1], unsorted[j]
lowerCamelCase : Optional[Any] = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = input('''Enter numbers separated by a comma:\n''').strip()
_snake_case = [int(item) for item in user_input.split(''',''')]
print(f'''{cocktail_shaker_sort(unsorted) = }''')
| 283
| 1
|
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger()
def __lowercase ( lowerCamelCase : int , lowerCamelCase : str , lowerCamelCase : LevitConfig , lowerCamelCase : Path , lowerCamelCase : bool = True ):
print(F"Converting {name}..." )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
UpperCamelCase_ : Optional[Any] = timm.create_model('levit_128s' , pretrained=lowerCamelCase )
else:
UpperCamelCase_ : List[str] = timm.create_model('levit_128' , pretrained=lowerCamelCase )
if hidden_sizes == 192:
UpperCamelCase_ : Optional[Any] = timm.create_model('levit_192' , pretrained=lowerCamelCase )
if hidden_sizes == 256:
UpperCamelCase_ : Optional[Any] = timm.create_model('levit_256' , pretrained=lowerCamelCase )
if hidden_sizes == 384:
UpperCamelCase_ : Tuple = timm.create_model('levit_384' , pretrained=lowerCamelCase )
from_model.eval()
UpperCamelCase_ : str = LevitForImageClassificationWithTeacher(lowerCamelCase ).eval()
UpperCamelCase_ : Tuple = OrderedDict()
UpperCamelCase_ : List[Any] = from_model.state_dict()
UpperCamelCase_ : Any = list(from_model.state_dict().keys() )
UpperCamelCase_ : int = list(our_model.state_dict().keys() )
print(len(lowerCamelCase ) , len(lowerCamelCase ) )
for i in range(len(lowerCamelCase ) ):
UpperCamelCase_ : Tuple = weights[og_keys[i]]
our_model.load_state_dict(lowerCamelCase )
UpperCamelCase_ : str = torch.randn((2, 3, 224, 224) )
UpperCamelCase_ : List[Any] = from_model(lowerCamelCase )
UpperCamelCase_ : Tuple = our_model(lowerCamelCase ).logits
assert torch.allclose(lowerCamelCase , lowerCamelCase ), "The model logits don't match the original one."
UpperCamelCase_ : Union[str, Any] = name
print(lowerCamelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
UpperCamelCase_ : Union[str, Any] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F"Pushed {checkpoint_name}" )
def __lowercase ( lowerCamelCase : Path , lowerCamelCase : str = None , lowerCamelCase : bool = True ):
UpperCamelCase_ : int = 'imagenet-1k-id2label.json'
UpperCamelCase_ : Union[str, Any] = 1000
UpperCamelCase_ : Optional[int] = (1, num_labels)
UpperCamelCase_ : List[Any] = 'huggingface/label-files'
UpperCamelCase_ : Optional[Any] = num_labels
UpperCamelCase_ : Optional[Any] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type='dataset' ) , 'r' ) )
UpperCamelCase_ : int = {int(lowerCamelCase ): v for k, v in idalabel.items()}
UpperCamelCase_ : Tuple = idalabel
UpperCamelCase_ : Tuple = {v: k for k, v in idalabel.items()}
UpperCamelCase_ : str = partial(lowerCamelCase , num_labels=lowerCamelCase , idalabel=lowerCamelCase , labelaid=lowerCamelCase )
UpperCamelCase_ : Optional[int] = {
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
UpperCamelCase_ : List[Any] = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , lowerCamelCase , names_to_config[model_name] , lowerCamelCase , lowerCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
return config, expected_shape
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
a_ = parser.parse_args()
a_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 50
|
import math
import flax.linen as nn
import jax.numpy as jnp
def __lowercase ( lowerCamelCase : jnp.ndarray , lowerCamelCase : int , lowerCamelCase : float = 1 , lowerCamelCase : float = 1 , lowerCamelCase : float = 1.0e4 , lowerCamelCase : bool = False , lowerCamelCase : float = 1.0 , ):
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F"Embedding dimension {embedding_dim} should be even"
UpperCamelCase_ : Dict = float(embedding_dim // 2 )
UpperCamelCase_ : Dict = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
UpperCamelCase_ : Optional[int] = min_timescale * jnp.exp(jnp.arange(lowerCamelCase , dtype=jnp.floataa ) * -log_timescale_increment )
UpperCamelCase_ : int = jnp.expand_dims(lowerCamelCase , 1 ) * jnp.expand_dims(lowerCamelCase , 0 )
# scale embeddings
UpperCamelCase_ : Tuple = scale * emb
if flip_sin_to_cos:
UpperCamelCase_ : Tuple = jnp.concatenate([jnp.cos(lowerCamelCase ), jnp.sin(lowerCamelCase )] , axis=1 )
else:
UpperCamelCase_ : Optional[int] = jnp.concatenate([jnp.sin(lowerCamelCase ), jnp.cos(lowerCamelCase )] , axis=1 )
UpperCamelCase_ : Optional[Any] = jnp.reshape(lowerCamelCase , [jnp.shape(lowerCamelCase )[0], embedding_dim] )
return signal
class _lowercase ( nn.Module ):
lowercase = 3_2
lowercase = jnp.floataa
@nn.compact
def __call__( self : str , snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : str = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_1' )(snake_case )
UpperCamelCase_ : int = nn.silu(snake_case )
UpperCamelCase_ : Optional[Any] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_2' )(snake_case )
return temb
class _lowercase ( nn.Module ):
lowercase = 3_2
lowercase = False
lowercase = 1
@nn.compact
def __call__( self : int , snake_case : Any ) -> str:
"""simple docstring"""
return get_sinusoidal_embeddings(
snake_case , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 50
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
snake_case_ : Dict = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
snake_case_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 83
|
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a__ : Optional[int] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a__ : int = None
def _UpperCamelCase ( ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = 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 _UpperCamelCase ( __A ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
UpperCamelCase__ = bool(qa["answers"]["text"] )
return qid_to_has_ans
def _UpperCamelCase ( __A ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(__A ):
return ARTICLES_REGEX.sub(" " , __A )
def white_space_fix(__A ):
return " ".join(text.split() )
def remove_punc(__A ):
UpperCamelCase__ = 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 _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def _UpperCamelCase ( __A , __A ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def _UpperCamelCase ( __A , __A ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ = get_tokens(__A )
UpperCamelCase__ = get_tokens(__A )
UpperCamelCase__ = collections.Counter(__A ) & collections.Counter(__A )
UpperCamelCase__ = 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
UpperCamelCase__ = 1.0 * num_same / len(__A )
UpperCamelCase__ = 1.0 * num_same / len(__A )
UpperCamelCase__ = (2 * precision * recall) / (precision + recall)
return fa
def _UpperCamelCase ( __A , __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = {}
UpperCamelCase__ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
UpperCamelCase__ = qa["id"]
UpperCamelCase__ = [t for t in qa["answers"]["text"] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
UpperCamelCase__ = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
UpperCamelCase__ = preds[qid]
# Take max over all gold answers
UpperCamelCase__ = max(compute_exact(__A , __A ) for a in gold_answers )
UpperCamelCase__ = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def _UpperCamelCase ( __A , __A , __A , __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = {}
for qid, s in scores.items():
UpperCamelCase__ = na_probs[qid] > na_prob_thresh
if pred_na:
UpperCamelCase__ = float(not qid_to_has_ans[qid] )
else:
UpperCamelCase__ = s
return new_scores
def _UpperCamelCase ( __A , __A , __A=None ) -> List[Any]:
'''simple docstring'''
if not qid_list:
UpperCamelCase__ = len(__A )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
UpperCamelCase__ = len(__A )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def _UpperCamelCase ( __A , __A , __A ) -> Optional[int]:
'''simple docstring'''
for k in new_eval:
UpperCamelCase__ = new_eval[k]
def _UpperCamelCase ( __A , __A , __A , __A ) -> Optional[int]:
'''simple docstring'''
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 _UpperCamelCase ( __A , __A , __A , __A , __A=None , __A=None ) -> Any:
'''simple docstring'''
UpperCamelCase__ = sorted(__A , key=lambda __A : na_probs[k] )
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1.0
UpperCamelCase__ = 0.0
UpperCamelCase__ = [1.0]
UpperCamelCase__ = [0.0]
UpperCamelCase__ = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
UpperCamelCase__ = true_pos / float(i + 1 )
UpperCamelCase__ = 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": 100.0 * avg_prec}
def _UpperCamelCase ( __A , __A , __A , __A , __A , __A ) -> List[str]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
UpperCamelCase__ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
UpperCamelCase__ = 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" , )
UpperCamelCase__ = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
UpperCamelCase__ = {k: float(__A ) for k, v in qid_to_has_ans.items()}
UpperCamelCase__ = 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 _UpperCamelCase ( __A , __A , __A , __A ) -> List[str]:
'''simple docstring'''
if not qid_list:
return
UpperCamelCase__ = [na_probs[k] for k in qid_list]
UpperCamelCase__ = 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 _UpperCamelCase ( __A , __A , __A , __A ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
UpperCamelCase__ = num_no_ans
UpperCamelCase__ = cur_score
UpperCamelCase__ = 0.0
UpperCamelCase__ = 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]:
UpperCamelCase__ = scores[qid]
else:
if preds[qid]:
UpperCamelCase__ = -1
else:
UpperCamelCase__ = 0
cur_score += diff
if cur_score > best_score:
UpperCamelCase__ = cur_score
UpperCamelCase__ = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def _UpperCamelCase ( __A , __A , __A , __A , __A , __A ) -> Dict:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = find_best_thresh(__A , __A , __A , __A )
UpperCamelCase__ , UpperCamelCase__ = find_best_thresh(__A , __A , __A , __A )
UpperCamelCase__ = best_exact
UpperCamelCase__ = exact_thresh
UpperCamelCase__ = best_fa
UpperCamelCase__ = fa_thresh
def _UpperCamelCase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
UpperCamelCase__ = json.load(__A )
UpperCamelCase__ = dataset_json["data"]
with open(OPTS.pred_file ) as f:
UpperCamelCase__ = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
UpperCamelCase__ = json.load(__A )
else:
UpperCamelCase__ = {k: 0.0 for k in preds}
UpperCamelCase__ = make_qid_to_has_ans(__A ) # maps qid to True/False
UpperCamelCase__ = [k for k, v in qid_to_has_ans.items() if v]
UpperCamelCase__ = [k for k, v in qid_to_has_ans.items() if not v]
UpperCamelCase__ , UpperCamelCase__ = get_raw_scores(__A , __A )
UpperCamelCase__ = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
UpperCamelCase__ = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
UpperCamelCase__ = make_eval_dict(__A , __A )
if has_ans_qids:
UpperCamelCase__ = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , "HasAns" )
if no_ans_qids:
UpperCamelCase__ = 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__":
a__ : Optional[int] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| 80
| 0
|
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 : int = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[int] = [
"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 : int = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 354
|
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Tuple = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n author = \"Moosavi, Nafise Sadat and\n Strube, Michael\",\n booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = aug,\n year = \"2016\",\n address = \"Berlin, Germany\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P16-1060\",\n doi = \"10.18653/v1/P16-1060\",\n pages = \"632--642\",\n}\n\n"
_UpperCAmelCase : int = "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5 Part-of-Speech\n6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"
_UpperCAmelCase : Union[str, Any] = "\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting 'keep_singletons=False', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n 'mentions': mentions\n 'muc': MUC metric [Vilain et al, 1995]\n 'bcub': B-cubed [Bagga and Baldwin, 1998]\n 'ceafe': CEAFe [Luo et al., 2005]\n 'lea': LEA [Moosavi and Strube, 2016]\n 'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric('coval')\n >>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',\n ... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',\n ... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',\n ... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',\n ... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',\n ... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase=False, lowerCamelCase=False, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="dummy_doc" ):
lowercase :str = {doc: key_lines}
lowercase :Union[str, Any] = {doc: sys_lines}
lowercase :Tuple = {}
lowercase :Optional[Any] = 0
lowercase :str = 0
lowercase :Optional[Any] = 0
lowercase :str = 0
lowercase :Union[str, Any] = 0
lowercase :int = 0
lowercase , lowercase :str = reader.get_doc_mentions(lowerCamelCase, key_doc_lines[doc], lowerCamelCase )
key_singletons_num += singletons_num
if NP_only or min_span:
lowercase :Any = reader.set_annotated_parse_trees(lowerCamelCase, key_doc_lines[doc], lowerCamelCase, lowerCamelCase )
lowercase , lowercase :int = reader.get_doc_mentions(lowerCamelCase, sys_doc_lines[doc], lowerCamelCase )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowercase :str = reader.set_annotated_parse_trees(lowerCamelCase, key_doc_lines[doc], lowerCamelCase, lowerCamelCase )
if remove_nested:
lowercase , lowercase :List[str] = reader.remove_nested_coref_mentions(lowerCamelCase, lowerCamelCase )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowercase , lowercase :Any = reader.remove_nested_coref_mentions(lowerCamelCase, lowerCamelCase )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowercase :Optional[Any] = reader.get_mention_assignments(lowerCamelCase, lowerCamelCase )
lowercase :str = reader.get_mention_assignments(lowerCamelCase, lowerCamelCase )
lowercase :Optional[int] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"Number of removed nested coreferring mentions in the key "
F"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}" )
logger.info(
"Number of resulting singleton clusters in the key "
F"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}" )
if not keep_singletons:
logger.info(
F"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
"files, respectively" )
return doc_coref_infos
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
lowercase :Union[str, Any] = get_coref_infos(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
lowercase :List[str] = {}
lowercase :Dict = 0
lowercase :Tuple = 0
for name, metric in metrics:
lowercase , lowercase , lowercase :int = evaluator.evaluate_documents(lowerCamelCase, lowerCamelCase, beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F"{name}/recall": recall, F"{name}/precision": precision, F"{name}/f1": fa} )
logger.info(
name.ljust(10 ), F"Recall: {recall * 100:.2f}", F" Precision: {precision * 100:.2f}", F" F1: {fa * 100:.2f}", )
if conll_subparts_num == 3:
lowercase :Any = (conll / 3) * 100
logger.info(F"CoNLL score: {conll:.2f}" )
output_scores.update({"conll_score": conll} )
return output_scores
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :str = False
for line in key_lines:
if not line.startswith("#" ):
if len(line.split() ) > 6:
lowercase :Union[str, Any] = line.split()[5]
if not parse_col == "-":
lowercase :Optional[int] = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __lowerCAmelCase ( datasets.Metric):
def SCREAMING_SNAKE_CASE ( self: Any ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Sequence(datasets.Value("string" ) ),
} ) , codebase_urls=["https://github.com/ns-moosavi/coval"] , reference_urls=[
"https://github.com/ns-moosavi/coval",
"https://www.aclweb.org/anthology/P16-1060",
"http://www.conll.cemantix.org/2012/data.html",
] , )
def SCREAMING_SNAKE_CASE ( self: Any , _lowerCAmelCase: Dict , _lowerCAmelCase: Tuple , _lowerCAmelCase: Tuple=True , _lowerCAmelCase: Dict=False , _lowerCAmelCase: Optional[int]=False , _lowerCAmelCase: Dict=False ):
lowercase :Any = [
("mentions", evaluator.mentions),
("muc", evaluator.muc),
("bcub", evaluator.b_cubed),
("ceafe", evaluator.ceafe),
("lea", evaluator.lea),
]
if min_span:
lowercase :List[str] = util.check_gold_parse_annotation(_lowerCAmelCase )
if not has_gold_parse:
raise NotImplementedError("References should have gold parse annotation to use 'min_span'." )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowercase :List[str] = evaluate(
key_lines=_lowerCAmelCase , sys_lines=_lowerCAmelCase , metrics=_lowerCAmelCase , NP_only=_lowerCAmelCase , remove_nested=_lowerCAmelCase , keep_singletons=_lowerCAmelCase , min_span=_lowerCAmelCase , )
return score
| 158
| 0
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
pass
class snake_case :
"""simple docstring"""
def __init__( self : List[Any] , __A : Any ):
__UpperCamelCase = data
__UpperCamelCase = None
def __iter__( self : Optional[Any] ):
__UpperCamelCase = self
__UpperCamelCase = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(__A )
yield node.data
__UpperCamelCase = node.next_node
@property
def _lowerCamelCase ( self : List[str] ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
a__ : Dict =Node(1)
a__ : Optional[int] =Node(2)
a__ : List[str] =Node(3)
a__ : Optional[int] =Node(4)
print(root_node.has_loop) # False
a__ : str =root_node.next_node
print(root_node.has_loop) # True
a__ : Optional[int] =Node(5)
a__ : List[Any] =Node(6)
a__ : int =Node(5)
a__ : Tuple =Node(6)
print(root_node.has_loop) # False
a__ : str =Node(1)
print(root_node.has_loop) # False
| 53
|
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
a__ : Tuple ='''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 53
| 1
|
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Dict = botoa.client('''iam''' )
lowerCAmelCase__ : Optional[Any] = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=A_ , AssumeRolePolicyDocument=json.dumps(A_ , indent=2 ) )
lowerCAmelCase__ : Optional[int] = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{
'''Effect''': '''Allow''',
'''Action''': [
'''sagemaker:*''',
'''ecr:GetDownloadUrlForLayer''',
'''ecr:BatchGetImage''',
'''ecr:BatchCheckLayerAvailability''',
'''ecr:GetAuthorizationToken''',
'''cloudwatch:PutMetricData''',
'''cloudwatch:GetMetricData''',
'''cloudwatch:GetMetricStatistics''',
'''cloudwatch:ListMetrics''',
'''logs:CreateLogGroup''',
'''logs:CreateLogStream''',
'''logs:DescribeLogStreams''',
'''logs:PutLogEvents''',
'''logs:GetLogEvents''',
'''s3:CreateBucket''',
'''s3:ListBucket''',
'''s3:GetBucketLocation''',
'''s3:GetObject''',
'''s3:PutObject''',
],
'''Resource''': '''*''',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=A_ , PolicyName=f'{role_name}_policy_permission' , PolicyDocument=json.dumps(A_ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f'role {role_name} already exists. Using existing one' )
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Union[str, Any] = botoa.client('''iam''' )
return iam_client.get_role(RoleName=A_ )["Role"]["Arn"]
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : Dict = _ask_options(
'''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , A_ , )
lowerCAmelCase__ : int = None
if credentials_configuration == 0:
lowerCAmelCase__ : Tuple = _ask_field('''Enter your AWS Profile name: [default] ''' , default='''default''' )
lowerCAmelCase__ : Optional[int] = aws_profile
else:
print(
'''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
'''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`''' )
lowerCAmelCase__ : Optional[Any] = _ask_field('''AWS Access Key ID: ''' )
lowerCAmelCase__ : Union[str, Any] = aws_access_key_id
lowerCAmelCase__ : Dict = _ask_field('''AWS Secret Access Key: ''' )
lowerCAmelCase__ : str = aws_secret_access_key
lowerCAmelCase__ : Optional[Any] = _ask_field('''Enter your AWS Region: [us-east-1]''' , default='''us-east-1''' )
lowerCAmelCase__ : Optional[int] = aws_region
lowerCAmelCase__ : List[str] = _ask_options(
'''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?''' , ['''Provide IAM Role name''', '''Create new IAM role using credentials'''] , A_ , )
if role_management == 0:
lowerCAmelCase__ : Optional[Any] = _ask_field('''Enter your IAM role name: ''' )
else:
lowerCAmelCase__ : Optional[int] = '''accelerate_sagemaker_execution_role'''
print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials' )
_create_iam_role_for_sagemaker(A_ )
lowerCAmelCase__ : Optional[int] = _ask_field(
'''Do you want to use custom Docker image? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
lowerCAmelCase__ : Any = None
if is_custom_docker_image:
lowerCAmelCase__ : List[Any] = _ask_field('''Enter your Docker image: ''' , lambda A_ : str(A_ ).lower() )
lowerCAmelCase__ : List[Any] = _ask_field(
'''Do you want to provide SageMaker input channels with data locations? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
lowerCAmelCase__ : Tuple = None
if is_sagemaker_inputs_enabled:
lowerCAmelCase__ : Dict = _ask_field(
'''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ''' , lambda A_ : str(A_ ).lower() , )
lowerCAmelCase__ : List[Any] = _ask_field(
'''Do you want to enable SageMaker metrics? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
lowerCAmelCase__ : Optional[Any] = None
if is_sagemaker_metrics_enabled:
lowerCAmelCase__ : str = _ask_field(
'''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ''' , lambda A_ : str(A_ ).lower() , )
lowerCAmelCase__ : Optional[Any] = _ask_options(
'''What is the distributed mode?''' , ['''No distributed training''', '''Data parallelism'''] , _convert_sagemaker_distributed_mode , )
lowerCAmelCase__ : Tuple = {}
lowerCAmelCase__ : Dict = _ask_field(
'''Do you wish to optimize your script with torch dynamo?[yes/NO]:''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
if use_dynamo:
lowerCAmelCase__ : Optional[int] = '''dynamo_'''
lowerCAmelCase__ : int = _ask_options(
'''Which dynamo backend would you like to use?''' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
lowerCAmelCase__ : List[Any] = _ask_field(
'''Do you want to customize the defaults sent to torch.compile? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
if use_custom_options:
lowerCAmelCase__ : List[Any] = _ask_options(
'''Which mode do you want to use?''' , A_ , lambda A_ : TORCH_DYNAMO_MODES[int(A_ )] , default='''default''' , )
lowerCAmelCase__ : Optional[Any] = _ask_field(
'''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
lowerCAmelCase__ : str = _ask_field(
'''Do you want to enable dynamic shape tracing? [yes/NO]: ''' , _convert_yes_no_to_bool , default=A_ , error_message='''Please enter yes or no.''' , )
lowerCAmelCase__ : Optional[Any] = '''Which EC2 instance type you want to use for your training?'''
if distributed_type != SageMakerDistributedType.NO:
lowerCAmelCase__ : Optional[Any] = _ask_options(
A_ , A_ , lambda A_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(A_ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
lowerCAmelCase__ : Union[str, Any] = _ask_field(A_ , lambda A_ : str(A_ ).lower() , default='''ml.p3.2xlarge''' )
lowerCAmelCase__ : Union[str, Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
lowerCAmelCase__ : Any = _ask_field(
'''How many machines do you want use? [1]: ''' , A_ , default=1 , )
lowerCAmelCase__ : str = _ask_options(
'''Do you wish to use FP16 or BF16 (mixed precision)?''' , ['''no''', '''fp16''', '''bf16''', '''fp8'''] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.''' )
return SageMakerConfig(
image_uri=A_ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=A_ , use_cpu=A_ , dynamo_config=A_ , eca_instance_type=A_ , profile=A_ , region=A_ , iam_role_name=A_ , mixed_precision=A_ , num_machines=A_ , sagemaker_inputs_file=A_ , sagemaker_metrics_file=A_ , )
| 74
|
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__UpperCamelCase : Any = NewType('''DataClass''', Any)
__UpperCamelCase : List[str] = NewType('''DataClassType''', Any)
def __SCREAMING_SNAKE_CASE ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).' )
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : int = {str(A_ ): choice for choice in choices}
return lambda A_ : str_to_choice.get(A_ , A_ )
def __SCREAMING_SNAKE_CASE ( *,
A_ = None , A_ = None , A_ = dataclasses.MISSING , A_ = dataclasses.MISSING , A_ = None , **A_ , ):
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
lowerCAmelCase__ : Dict = {}
if aliases is not None:
lowerCAmelCase__ : int = aliases
if help is not None:
lowerCAmelCase__ : Optional[int] = help
return dataclasses.field(metadata=A_ , default=A_ , default_factory=A_ , **A_ )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = 42
def __init__( self : Dict ,lowercase_ : Union[DataClassType, Iterable[DataClassType]] ,**lowercase_ : str ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
lowerCAmelCase__ : Tuple = ArgumentDefaultsHelpFormatter
super().__init__(**lowercase_ )
if dataclasses.is_dataclass(lowercase_ ):
lowerCAmelCase__ : Tuple = [dataclass_types]
lowerCAmelCase__ : List[str] = list(lowercase_ )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(lowercase_ )
@staticmethod
def __lowerCAmelCase ( lowercase_ : ArgumentParser ,lowercase_ : dataclasses.Field ):
lowerCAmelCase__ : Dict = F'--{field.name}'
lowerCAmelCase__ : List[str] = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,lowercase_ ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
lowerCAmelCase__ : List[str] = kwargs.pop('''aliases''' ,[] )
if isinstance(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : Optional[Any] = [aliases]
lowerCAmelCase__ : Union[str, Any] = getattr(field.type ,'''__origin__''' ,field.type )
if origin_type is Union or (hasattr(lowercase_ ,'''UnionType''' ) and isinstance(lowercase_ ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(lowercase_ ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
F' Problem encountered in field \'{field.name}\'.' )
if type(lowercase_ ) not in field.type.__args__:
# filter `str` in Union
lowerCAmelCase__ : int = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
lowerCAmelCase__ : List[str] = getattr(field.type ,'''__origin__''' ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
lowerCAmelCase__ : Optional[int] = (
field.type.__args__[0] if isinstance(lowercase_ ,field.type.__args__[1] ) else field.type.__args__[1]
)
lowerCAmelCase__ : Optional[Any] = getattr(field.type ,'''__origin__''' ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
lowerCAmelCase__ : List[Any] = {}
if origin_type is Literal or (isinstance(field.type ,lowercase_ ) and issubclass(field.type ,lowercase_ )):
if origin_type is Literal:
lowerCAmelCase__ : Union[str, Any] = field.type.__args__
else:
lowerCAmelCase__ : Optional[Any] = [x.value for x in field.type]
lowerCAmelCase__ : List[str] = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
lowerCAmelCase__ : int = field.default
else:
lowerCAmelCase__ : Any = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
lowerCAmelCase__ : List[Any] = copy(lowercase_ )
# Hack because type=bool in argparse does not behave as we want.
lowerCAmelCase__ : Tuple = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
lowerCAmelCase__ : List[Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
lowerCAmelCase__ : Tuple = default
# This tells argparse we accept 0 or 1 value after --field_name
lowerCAmelCase__ : Union[str, Any] = '''?'''
# This is the value that will get picked if we do --field_name (without value)
lowerCAmelCase__ : Any = True
elif isclass(lowercase_ ) and issubclass(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : List[str] = field.type.__args__[0]
lowerCAmelCase__ : str = '''+'''
if field.default_factory is not dataclasses.MISSING:
lowerCAmelCase__ : Dict = field.default_factory()
elif field.default is dataclasses.MISSING:
lowerCAmelCase__ : str = True
else:
lowerCAmelCase__ : List[Any] = field.type
if field.default is not dataclasses.MISSING:
lowerCAmelCase__ : str = field.default
elif field.default_factory is not dataclasses.MISSING:
lowerCAmelCase__ : Any = field.default_factory()
else:
lowerCAmelCase__ : Optional[Any] = True
parser.add_argument(lowercase_ ,*lowercase_ ,**lowercase_ )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
lowerCAmelCase__ : Optional[Any] = False
parser.add_argument(F'--no_{field.name}' ,action='''store_false''' ,dest=field.name ,**lowercase_ )
def __lowerCAmelCase ( self : str ,lowercase_ : DataClassType ):
if hasattr(lowercase_ ,'''_argument_group_name''' ):
lowerCAmelCase__ : Optional[int] = self.add_argument_group(dtype._argument_group_name )
else:
lowerCAmelCase__ : List[str] = self
try:
lowerCAmelCase__ : Dict[str, type] = get_type_hints(lowercase_ )
except NameError:
raise RuntimeError(
F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(lowercase_ ):
lowerCAmelCase__ : int = '''.'''.join(map(lowercase_ ,sys.version_info[:3] ) )
raise RuntimeError(
F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(lowercase_ ):
if not field.init:
continue
lowerCAmelCase__ : Any = type_hints[field.name]
self._parse_dataclass_field(lowercase_ ,lowercase_ )
def __lowerCAmelCase ( self : Any ,lowercase_ : Optional[Any]=None ,lowercase_ : str=False ,lowercase_ : str=True ,lowercase_ : Any=None ,lowercase_ : List[str]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
lowerCAmelCase__ : int = []
if args_filename:
args_files.append(Path(lowercase_ ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
lowerCAmelCase__ : List[str] = ArgumentParser()
args_file_parser.add_argument(lowercase_ ,type=lowercase_ ,action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
lowerCAmelCase__ ,lowerCAmelCase__ : List[str] = args_file_parser.parse_known_args(args=lowercase_ )
lowerCAmelCase__ : int = vars(lowercase_ ).get(args_file_flag.lstrip('''-''' ) ,lowercase_ )
if cmd_args_file_paths:
args_files.extend([Path(lowercase_ ) for p in cmd_args_file_paths] )
lowerCAmelCase__ : Tuple = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
lowerCAmelCase__ : Dict = file_args + args if args is not None else file_args + sys.argv[1:]
lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = self.parse_known_args(args=lowercase_ )
lowerCAmelCase__ : Optional[Any] = []
for dtype in self.dataclass_types:
lowerCAmelCase__ : int = {f.name for f in dataclasses.fields(lowercase_ ) if f.init}
lowerCAmelCase__ : int = {k: v for k, v in vars(lowercase_ ).items() if k in keys}
for k in keys:
delattr(lowercase_ ,lowercase_ )
lowerCAmelCase__ : Optional[Any] = dtype(**lowercase_ )
outputs.append(lowercase_ )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(lowercase_ )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}' )
return (*outputs,)
def __lowerCAmelCase ( self : Any ,lowercase_ : Dict[str, Any] ,lowercase_ : bool = False ):
lowerCAmelCase__ : List[Any] = set(args.keys() )
lowerCAmelCase__ : Any = []
for dtype in self.dataclass_types:
lowerCAmelCase__ : Optional[Any] = {f.name for f in dataclasses.fields(lowercase_ ) if f.init}
lowerCAmelCase__ : Union[str, Any] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
lowerCAmelCase__ : Union[str, Any] = dtype(**lowercase_ )
outputs.append(lowercase_ )
if not allow_extra_keys and unused_keys:
raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(lowercase_ )}' )
return tuple(lowercase_ )
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : str ,lowercase_ : bool = False ):
with open(Path(lowercase_ ) ,encoding='''utf-8''' ) as open_json_file:
lowerCAmelCase__ : Union[str, Any] = json.loads(open_json_file.read() )
lowerCAmelCase__ : List[str] = self.parse_dict(lowercase_ ,allow_extra_keys=lowercase_ )
return tuple(lowercase_ )
def __lowerCAmelCase ( self : Dict ,lowercase_ : str ,lowercase_ : bool = False ):
lowerCAmelCase__ : Tuple = self.parse_dict(yaml.safe_load(Path(lowercase_ ).read_text() ) ,allow_extra_keys=lowercase_ )
return tuple(lowercase_ )
| 74
| 1
|
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase ( a__ : List[Any] ) -> Optional[int]:
_UpperCamelCase = {}
_UpperCamelCase = job['''started_at''']
_UpperCamelCase = job['''completed_at''']
_UpperCamelCase = date_parser.parse(a__ )
_UpperCamelCase = date_parser.parse(a__ )
_UpperCamelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCamelCase = start
_UpperCamelCase = end
_UpperCamelCase = duration_in_min
return job_info
def lowercase ( a__ : str , a__ : Dict=None ) -> Optional[int]:
_UpperCamelCase = None
if token is not None:
_UpperCamelCase = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}
_UpperCamelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCamelCase = requests.get(a__ , headers=a__ ).json()
_UpperCamelCase = {}
try:
job_time.update({job['''name''']: extract_time_from_single_job(a__ ) for job in result['''jobs''']} )
_UpperCamelCase = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(a__ ):
_UpperCamelCase = requests.get(url + F'''&page={i + 2}''' , headers=a__ ).json()
job_time.update({job['''name''']: extract_time_from_single_job(a__ ) for job in result['''jobs''']} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = get_job_time(args.workflow_run_id)
UpperCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 256
|
"""simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 256
| 1
|
"""simple docstring"""
import argparse
lowerCAmelCase__ = '''docs/source/_static/js/custom.js'''
def snake_case_ ( A_ : List[str] ):
'''simple docstring'''
with open(A_, encoding='''utf-8''', newline='''\n''' ) as f:
_lowerCamelCase : int = f.readlines()
_lowerCamelCase : List[str] = 0
# First let's put the right version
while not lines[index].startswith('''const stableVersion =''' ):
index += 1
_lowerCamelCase : List[Any] = F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith('''const versionMapping = {''' ):
index += 1
# We go until the end
while not lines[index].startswith('''}''' ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(A_, '''w''', encoding='''utf-8''', newline='''\n''' ) as f:
f.writelines(A_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--version''', help='''Release version.''')
lowerCAmelCase__ = parser.parse_args()
update_custom_js(args.version)
| 175
|
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : str = 0
_lowerCamelCase : Any = number
while duplicate > 0:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = divmod(A_, 10 )
fact_sum += factorial(A_ )
return fact_sum == number
if __name__ == "__main__":
print('''Program to check whether a number is a Krisnamurthy Number or not.''')
lowerCAmelCase__ = int(input('''Enter number: ''').strip())
print(
F"""{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number."""
)
| 175
| 1
|
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> None:
if index == len(_UpperCAmelCase ):
print(_UpperCAmelCase )
return
create_state_space_tree(_UpperCAmelCase , _UpperCAmelCase , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(_UpperCAmelCase , _UpperCAmelCase , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
_UpperCAmelCase : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["""A""", """B""", """C"""])
generate_all_subsequences(seq)
| 50
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Any = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : int = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int = {
"""google/realm-cc-news-pretrained-embedder""": 5_12,
"""google/realm-cc-news-pretrained-encoder""": 5_12,
"""google/realm-cc-news-pretrained-scorer""": 5_12,
"""google/realm-cc-news-pretrained-openqa""": 5_12,
"""google/realm-orqa-nq-openqa""": 5_12,
"""google/realm-orqa-nq-reader""": 5_12,
"""google/realm-orqa-wq-openqa""": 5_12,
"""google/realm-orqa-wq-reader""": 5_12,
}
_UpperCAmelCase : Any = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = RealmTokenizer
def __init__( self : Optional[int] , UpperCAmelCase : Tuple=None , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Optional[Any]="[UNK]" , UpperCAmelCase : Any="[SEP]" , UpperCAmelCase : Tuple="[PAD]" , UpperCAmelCase : List[Any]="[CLS]" , UpperCAmelCase : Union[str, Any]="[MASK]" , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Any=None , **UpperCAmelCase : Optional[int] , ) -> str:
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
lowerCamelCase__ : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , UpperCAmelCase ) != do_lower_case
or normalizer_state.get('strip_accents' , UpperCAmelCase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , UpperCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ : Optional[int] = getattr(UpperCAmelCase , normalizer_state.pop('type' ) )
lowerCamelCase__ : Optional[Any] = do_lower_case
lowerCamelCase__ : str = strip_accents
lowerCamelCase__ : Optional[Any] = tokenize_chinese_chars
lowerCamelCase__ : int = normalizer_class(**UpperCAmelCase )
lowerCamelCase__ : str = do_lower_case
def A_ ( self : Optional[int] , UpperCAmelCase : int , **UpperCAmelCase : int ) -> List[Any]:
lowerCamelCase__ : List[Any] = PaddingStrategy.MAX_LENGTH
lowerCamelCase__ : Optional[int] = text
lowerCamelCase__ : Dict = kwargs.pop('text_pair' , UpperCAmelCase )
lowerCamelCase__ : List[Any] = kwargs.pop('return_tensors' , UpperCAmelCase )
lowerCamelCase__ : List[Any] = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(UpperCAmelCase ):
if batch_text_pair is not None:
lowerCamelCase__ : Tuple = batch_text_pair[idx]
else:
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[int] = super().__call__(UpperCAmelCase , UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase__ : Any = encoded_candidates.get('input_ids' )
lowerCamelCase__ : Union[str, Any] = encoded_candidates.get('attention_mask' )
lowerCamelCase__ : Tuple = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(UpperCAmelCase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(UpperCAmelCase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(UpperCAmelCase )
lowerCamelCase__ : int = {key: item for key, item in output_data.items() if len(UpperCAmelCase ) != 0}
return BatchEncoding(UpperCAmelCase , tensor_type=UpperCAmelCase )
def A_ ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None ) -> List[str]:
lowerCamelCase__ : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : Tuple , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
lowerCamelCase__ : List[Any] = [self.sep_token_id]
lowerCamelCase__ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
lowerCamelCase__ : int = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 50
| 1
|
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : List[Any] =logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] ={
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
'''merges_file''': '''merges.txt''',
}
UpperCAmelCase__ : Any ={
'''vocab_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
),
},
'''tokenizer_config_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
),
},
'''merges_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
),
},
}
UpperCAmelCase__ : Dict ='''</w>'''
UpperCAmelCase__ : List[Any] ='''@@ '''
def _lowercase ( _UpperCAmelCase ) -> Any:
lowerCamelCase =set()
lowerCamelCase =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase =char
return pairs
# Speech2Text2 has no max input length
UpperCAmelCase__ : Optional[int] ={'''facebook/s2t-wav2vec2-large-en-de''': 10_24}
class __A ( a ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_="<s>" , UpperCAmelCase_="<pad>" , UpperCAmelCase_="</s>" , UpperCAmelCase_="<unk>" , UpperCAmelCase_=False , UpperCAmelCase_=None , **UpperCAmelCase_ , ):
super().__init__(
unk_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCamelCase =do_lower_case
with open(UpperCAmelCase_ , encoding="""utf-8""" ) as vocab_handle:
lowerCamelCase =json.load(UpperCAmelCase_ )
lowerCamelCase ={v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""" )
lowerCamelCase =None
lowerCamelCase =None
else:
with open(UpperCAmelCase_ , encoding="""utf-8""" ) as merges_handle:
lowerCamelCase =merges_handle.read().split("""\n""" )[:-1]
lowerCamelCase =[tuple(merge.split()[:2] ) for merge in merges]
lowerCamelCase =dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
lowerCamelCase ={}
@property
def _snake_case ( self ):
return len(self.decoder )
def _snake_case ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self , UpperCAmelCase_ ):
lowerCamelCase =tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
lowerCamelCase =get_pairs(UpperCAmelCase_ )
if not pairs:
return token
while True:
lowerCamelCase =min(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : self.bpe_ranks.get(UpperCAmelCase_ , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase , lowerCamelCase =bigram
lowerCamelCase =[]
lowerCamelCase =0
while i < len(UpperCAmelCase_ ):
try:
lowerCamelCase =word.index(UpperCAmelCase_ , UpperCAmelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase =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 =tuple(UpperCAmelCase_ )
lowerCamelCase =new_word
if len(UpperCAmelCase_ ) == 1:
break
else:
lowerCamelCase =get_pairs(UpperCAmelCase_ )
lowerCamelCase =""" """.join(UpperCAmelCase_ )
if word == "\n " + BPE_TOKEN_MERGES:
lowerCamelCase ="""\n""" + BPE_TOKEN_MERGES
if word.endswith(UpperCAmelCase_ ):
lowerCamelCase =word.replace(UpperCAmelCase_ , """""" )
lowerCamelCase =word.replace(""" """ , UpperCAmelCase_ )
lowerCamelCase =word
return word
def _snake_case ( self , UpperCAmelCase_ ):
if self.bpe_ranks is None:
raise ValueError(
"""This tokenizer was instantiated without a `merges.txt` file, so"""
""" that it can only be used for decoding, not for encoding."""
"""Make sure to provide `merges.txt` file at instantiation to enable """
"""encoding.""" )
if self.do_lower_case:
lowerCamelCase =text.lower()
lowerCamelCase =text.split()
lowerCamelCase =[]
for token in text:
if token:
split_tokens.extend(list(self.bpe(UpperCAmelCase_ ).split(""" """ ) ) )
return split_tokens
def _snake_case ( self , UpperCAmelCase_ ):
return self.encoder.get(UpperCAmelCase_ , self.encoder.get(self.unk_token ) )
def _snake_case ( self , UpperCAmelCase_ ):
lowerCamelCase =self.decoder.get(UpperCAmelCase_ , self.unk_token )
return result
def _snake_case ( self , UpperCAmelCase_ ):
lowerCamelCase =""" """.join(UpperCAmelCase_ )
# make sure @@ tokens are concatenated
lowerCamelCase ="""""".join(string.split(UpperCAmelCase_ ) )
return string
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase =os.path.join(
UpperCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCamelCase =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 =0
if self.bpe_ranks is None:
return (vocab_file,)
with open(UpperCAmelCase_ , """w""" , encoding="""utf-8""" ) as writer:
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 {merges_file}: BPE merge indices are not consecutive."""
""" Please check that the tokenizer is not corrupted!""" )
lowerCamelCase =token_index
writer.write(""" """.join(UpperCAmelCase_ ) + """\n""" )
index += 1
return (vocab_file, merges_file)
| 262
|
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __A ( unittest.TestCase ):
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
lowerCamelCase =Vector()
def _snake_case ( self ):
lowerCamelCase =Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(UpperCAmelCase_ ) , """(0,0,0,0,0,1)""" )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3, 4] )
self.assertEqual(len(UpperCAmelCase_ ) , 4 )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2] )
lowerCamelCase =Vector([1, 2, 3, 4, 5] )
lowerCamelCase =Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
lowerCamelCase =Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.2_3_6 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.4_1_6 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.6_1_6 , 3 )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3] )
lowerCamelCase =Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3] )
lowerCamelCase =Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3] )
lowerCamelCase =Vector([2, -1, 4] ) # for test of dot product
lowerCamelCase =Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , """(3.0,6.0,9.0)""" )
self.assertEqual((a * b) , 0 )
def _snake_case ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("""0""" ) , 10 )
def _snake_case ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , """(0,1,0)""" )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 2, 3] )
lowerCamelCase =Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , UpperCAmelCase_ , UpperCAmelCase_ ) ) , """(3,4,7)""" )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 0, 0, 0, 0, 0] )
lowerCamelCase =x.copy()
self.assertEqual(str(UpperCAmelCase_ ) , str(UpperCAmelCase_ ) )
def _snake_case ( self ):
lowerCamelCase =Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(UpperCAmelCase_ ) , """(0,1,0)""" )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(UpperCAmelCase_ ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCamelCase =[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCamelCase =[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
lowerCamelCase =Vector([1, 2, 3] )
self.assertEqual("""(14,32,50)""" , str(a * x ) )
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2 ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(UpperCAmelCase_ ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.0_1 )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCamelCase =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b ) )
def _snake_case ( self ):
lowerCamelCase =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCamelCase =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b ) )
def _snake_case ( self ):
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 262
| 1
|
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = argparse.ArgumentParser(
description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset." )
parser.add_argument(
"--dataset_name" , type=snake_case_ , default="wikitext" , help="Name of the training. Explore datasets at: hf.co/datasets." , )
parser.add_argument(
"--dataset_config" , type=snake_case_ , default="wikitext-103-raw-v1" , help="Configuration name of the dataset." )
parser.add_argument(
"--tokenizer_name_or_path" , type=snake_case_ , default="sayakpaul/unigram-tokenizer-wikitext" , help="Tokenizer identifier. Can be a local filepath or a Hub identifier." , )
parser.add_argument(
"--shard_size" , type=snake_case_ , default=10_00 , help="Number of entries to go in a single shard." , )
parser.add_argument("--split" , type=snake_case_ , default="train" , choices=["train", "test", "validation"] )
parser.add_argument(
"--limit" , default=snake_case_ , type=snake_case_ , help="Limit the number of shards (used for debugging)." , )
parser.add_argument(
"--max_length" , type=snake_case_ , default=5_12 , help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
" sequence length that is a multiple of 8." , )
parser.add_argument(
"--output_dir" , default="tf-tpu" , type=snake_case_ , help="Output directory where the TFRecord shards will be saved. If the"
" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
" shards will be directly saved to a Google Cloud Storage bucket." , )
UpperCAmelCase_ = parser.parse_args()
return args
def lowerCAmelCase_ ( snake_case_ : Dict ) -> str:
'''simple docstring'''
def fn(snake_case_ : Optional[Any] ):
return tokenizer(examples["text"] )
return fn
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = []
for i in range(len(tokenized_data["input_ids"] ) ):
UpperCAmelCase_ = {
"input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i] ) ),
"attention_mask": tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i] ) ),
}
UpperCAmelCase_ = tf.train.Features(feature=snake_case_ )
UpperCAmelCase_ = tf.train.Example(features=snake_case_ )
UpperCAmelCase_ = example.SerializeToString()
records.append(snake_case_ )
return records
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
UpperCAmelCase_ = min(len(snake_case_ ) , args.limit )
UpperCAmelCase_ = dataset.select(range(snake_case_ ) )
print(f"""Limiting the dataset to {args.limit} entries.""" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
UpperCAmelCase_ = os.path.join(args.output_dir , args.split )
if not os.path.exists(snake_case_ ):
os.makedirs(snake_case_ )
else:
UpperCAmelCase_ = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
UpperCAmelCase_ = tokenize_function(snake_case_ )
UpperCAmelCase_ = dataset.map(snake_case_ , batched=snake_case_ , num_proc=4 , remove_columns=["text"] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(snake_case_ : str ):
# Concatenate all texts.
UpperCAmelCase_ = {k: sum(examples[k] , [] ) for k in examples.keys()}
UpperCAmelCase_ = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
UpperCAmelCase_ = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
UpperCAmelCase_ = {
k: [t[i : i + args.max_length] for i in range(0 , snake_case_ , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
UpperCAmelCase_ = dataset_tokenized.map(snake_case_ , batched=snake_case_ , batch_size=10_00 , num_proc=4 )
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for shard in range(0 , len(snake_case_ ) , args.shard_size ):
UpperCAmelCase_ = grouped_dataset[shard : shard + args.shard_size]
UpperCAmelCase_ = len(dataset_snapshot["input_ids"] )
UpperCAmelCase_ = os.path.join(snake_case_ , f"""dataset-{shard_count}-{records_containing}.tfrecord""" )
UpperCAmelCase_ = get_serialized_examples(snake_case_ )
with tf.io.TFRecordWriter(snake_case_ ) as out_file:
for i in range(len(snake_case_ ) ):
UpperCAmelCase_ = serialized_examples[i]
out_file.write(snake_case_ )
print("Wrote file {} containing {} records".format(snake_case_ , snake_case_ ) )
shard_count += 1
total_records += records_containing
with open(f"""split-{args.split}-records-count.txt""" , "w" ) as f:
print(f"""Total {args.split} records: {total_records}""" , file=snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: List[str] =parse_args()
main(args)
| 1
|
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowerCAmelCase_ ( __magic_name__ ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=True , _lowerCAmelCase=None , **_lowerCAmelCase ) -> Optional[int]:
_lowerCAmelCase = parent
_lowerCAmelCase = config_class
_lowerCAmelCase = has_text_modality
_lowerCAmelCase = kwargs
_lowerCAmelCase = common_properties
def _snake_case ( self ) -> int:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
_lowerCAmelCase = (
["hidden_size", "num_attention_heads", "num_hidden_layers"]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["vocab_size"] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(_lowerCAmelCase , _lowerCAmelCase ) , msg=f'''`{prop}` does not exist''' )
# Test that config has the common properties as setter
for idx, name in enumerate(_lowerCAmelCase ):
try:
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.parent.assertEqual(
getattr(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , msg=f'''`{name} value {idx} expected, but was {getattr(_lowerCAmelCase , _lowerCAmelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(_lowerCAmelCase ):
try:
_lowerCAmelCase = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , msg=f'''`{name} value {idx} expected, but was {getattr(_lowerCAmelCase , _lowerCAmelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _snake_case ( self ) -> Optional[int]:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
_lowerCAmelCase = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , _lowerCAmelCase )
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase = os.path.join(_lowerCAmelCase , "config.json" )
config_first.to_json_file(_lowerCAmelCase )
_lowerCAmelCase = self.config_class.from_json_file(_lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _snake_case ( self ) -> str:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(_lowerCAmelCase )
_lowerCAmelCase = self.config_class.from_pretrained(_lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _snake_case ( self ) -> Dict:
_lowerCAmelCase = self.config_class(**self.inputs_dict )
_lowerCAmelCase = "test"
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase = os.path.join(_lowerCAmelCase , _lowerCAmelCase )
config_first.save_pretrained(_lowerCAmelCase )
_lowerCAmelCase = self.config_class.from_pretrained(_lowerCAmelCase , subfolder=_lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _snake_case ( self ) -> Optional[Any]:
_lowerCAmelCase = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_lowerCAmelCase = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _snake_case ( self ) -> List[Any]:
if self.config_class.is_composition:
return
_lowerCAmelCase = self.config_class()
self.parent.assertIsNotNone(_lowerCAmelCase )
def _snake_case ( self ) -> str:
_lowerCAmelCase = copy.deepcopy(_lowerCAmelCase )
_lowerCAmelCase = self.config_class(**_lowerCAmelCase )
_lowerCAmelCase = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) )
elif getattr(_lowerCAmelCase , _lowerCAmelCase ) != value:
wrong_values.append((key, getattr(_lowerCAmelCase , _lowerCAmelCase ), value) )
if len(_lowerCAmelCase ) > 0:
_lowerCAmelCase = "\n".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
raise ValueError(f'''The following keys were not properly set in the config:\n{errors}''' )
def _snake_case ( self ) -> List[str]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 158
| 0
|
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
A : List[str] = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_sentencepiece
@require_tokenizers
class A (SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = SpeechTaTokenizer
__lowerCamelCase : Tuple = False
__lowerCamelCase : int = True
def a_ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(__lowerCAmelCase )
A__ = AddedToken("""<mask>""" , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase )
A__ = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def a_ ( self : Tuple , __lowerCAmelCase : Dict ) -> Any:
"""simple docstring"""
A__ = """this is a test"""
A__ = """this is a test"""
return input_text, output_text
def a_ ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict=False , __lowerCAmelCase : List[Any]=20 , __lowerCAmelCase : str=5 ) -> Optional[int]:
"""simple docstring"""
A__ , A__ = self.get_input_output_texts(__lowerCAmelCase )
A__ = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
A__ = tokenizer.decode(__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
return text, ids
def a_ ( self : Optional[int] ) -> int:
"""simple docstring"""
A__ = """<pad>"""
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) , __lowerCAmelCase )
def a_ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(__lowerCAmelCase ) , 81 )
def a_ ( self : Tuple ) -> List[str]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def a_ ( self : Any ) -> Any:
"""simple docstring"""
A__ = self.get_tokenizers(do_lower_case=__lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
A__ = tokenizer.add_tokens(__lowerCAmelCase )
A__ = tokenizer.vocab_size
A__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , all_size + len(__lowerCAmelCase ) )
A__ = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
A__ = tokenizer.add_special_tokens(__lowerCAmelCase )
A__ = tokenizer.vocab_size
A__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , all_size_a + len(__lowerCAmelCase ) )
A__ = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def a_ ( self : List[str] ) -> int:
"""simple docstring"""
pass
def a_ ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def a_ ( self : int ) -> List[Any]:
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(__lowerCAmelCase , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__lowerCAmelCase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
A__ = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
# fmt: off
self.assertListEqual(__lowerCAmelCase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def a_ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
A__ = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
A__ = {
"""input_ids""": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCAmelCase , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=__lowerCAmelCase , )
| 276
|
import math
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
A__ = input("""Enter message: """ )
A__ = int(input(F'Enter key [2-{len(__a ) - 1}]: ' ) )
A__ = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
A__ = encrypt_message(__a , __a )
elif mode.lower().startswith("""d""" ):
A__ = decrypt_message(__a , __a )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(F'Output:\n{text + "|"}' )
def __lowerCamelCase ( __a :int , __a :str ) -> str:
"""simple docstring"""
A__ = [""""""] * key
for col in range(__a ):
A__ = col
while pointer < len(__a ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(__a )
def __lowerCamelCase ( __a :int , __a :str ) -> str:
"""simple docstring"""
A__ = math.ceil(len(__a ) / key )
A__ = key
A__ = (num_cols * num_rows) - len(__a )
A__ = [""""""] * num_cols
A__ = 0
A__ = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
A__ = 0
row += 1
return "".join(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 276
| 1
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_lowercase = '''examples/'''
_lowercase = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
_lowercase = {
'''init''': '''src/diffusers/__init__.py''',
'''setup''': '''setup.py''',
}
_lowercase = '''README.md'''
def _snake_case ( snake_case__ : str , snake_case__ : int , snake_case__ : Dict ):
with open(snake_case__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
A = f.read()
A , A = REPLACE_PATTERNS[pattern]
A = replace.replace('VERSION' , snake_case__ )
A = re_pattern.sub(snake_case__ , snake_case__ )
with open(snake_case__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(snake_case__ )
def _snake_case ( snake_case__ : Tuple ):
for folder, directories, fnames in os.walk(snake_case__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(snake_case__ , snake_case__ ) , snake_case__ , pattern='examples' )
def _snake_case ( snake_case__ : List[str] , snake_case__ : List[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(snake_case__ , snake_case__ , snake_case__ )
if not patch:
update_version_in_examples(snake_case__ )
def _snake_case ( ):
A = '🤗 Transformers currently provides the following architectures'
A = '1. Want to contribute a new model?'
with open(snake_case__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
A = f.readlines()
# Find the start of the list.
A = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
A = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
A = lines[index].replace(
'https://huggingface.co/docs/diffusers/main/model_doc' , 'https://huggingface.co/docs/diffusers/model_doc' , )
index += 1
with open(snake_case__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(snake_case__ )
def _snake_case ( ):
with open(REPLACE_FILES['init'] , 'r' ) as f:
A = f.read()
A = REPLACE_PATTERNS['init'][0].search(snake_case__ ).groups()[0]
return packaging.version.parse(snake_case__ )
def _snake_case ( snake_case__ : Dict=False ):
A = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
A = default_version.base_version
elif patch:
A = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
A = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
A = input(F'Which version are you releasing? [{default_version}]' )
if len(snake_case__ ) == 0:
A = default_version
print(F'Updating version to {version}.' )
global_version_update(snake_case__ , patch=snake_case__ )
def _snake_case ( ):
A = get_version()
A = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
A = current_version.base_version
# Check with the user we got that right.
A = input(F'Which version are we developing now? [{dev_version}]' )
if len(snake_case__ ) == 0:
A = dev_version
print(F'Updating version to {version}.' )
global_version_update(snake_case__ )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
_lowercase = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 74
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Tuple = '''dpt'''
def __init__( self : str ,A_ : Tuple=768 ,A_ : int=12 ,A_ : Optional[int]=12 ,A_ : Optional[int]=3072 ,A_ : List[str]="gelu" ,A_ : str=0.0 ,A_ : int=0.0 ,A_ : str=0.02 ,A_ : str=1e-12 ,A_ : str=384 ,A_ : Dict=16 ,A_ : Union[str, Any]=3 ,A_ : Dict=False ,A_ : Any=True ,A_ : Optional[int]=[2, 5, 8, 11] ,A_ : Optional[Any]="project" ,A_ : Tuple=[4, 2, 1, 0.5] ,A_ : int=[96, 192, 384, 768] ,A_ : int=256 ,A_ : str=-1 ,A_ : Optional[int]=False ,A_ : Optional[int]=True ,A_ : Union[str, Any]=0.4 ,A_ : Union[str, Any]=255 ,A_ : Union[str, Any]=0.1 ,A_ : List[str]=[1, 1024, 24, 24] ,A_ : List[str]=[0, 1] ,A_ : List[Any]=None ,**A_ : Tuple ,) -> Union[str, Any]:
super().__init__(**A_ )
A = hidden_size
A = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('Initializing the config with a `BiT` backbone.' )
A = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
}
A = BitConfig(**A_ )
elif isinstance(A_ ,A_ ):
logger.info('Initializing the config with a `BiT` backbone.' )
A = BitConfig(**A_ )
elif isinstance(A_ ,A_ ):
A = backbone_config
else:
raise ValueError(
F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.' )
A = backbone_featmap_shape
A = neck_ignore_stages
if readout_type != "project":
raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.' )
else:
A = None
A = None
A = []
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 = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']' )
A = readout_type
A = reassemble_factors
A = neck_hidden_sizes
A = fusion_hidden_size
A = head_in_index
A = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
A = use_auxiliary_head
A = auxiliary_loss_weight
A = semantic_loss_ignore_index
A = semantic_classifier_dropout
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> str:
A = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
A = self.backbone_config.to_dict()
A = self.__class__.model_type
return output
| 74
| 1
|
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> list:
if any(not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(lowerCAmelCase_ ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_ , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 107
|
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
for i in range(0 , lowerCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # printing stars
print('* ' , end='' )
print()
def __lowerCamelCase ( lowerCAmelCase_ ) -> Union[str, Any]:
for i in range(lowerCAmelCase_ , 0 , -1 ):
for _ in range(lowerCAmelCase_ , 0 , -1 ): # printing stars
print('* ' , end='' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(' ' , end='' )
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
if n <= 0:
print(' ... .... nothing printing :(' )
return
floyd(lowerCAmelCase_ ) # upper half
reverse_floyd(lowerCAmelCase_ ) # lower half
if __name__ == "__main__":
print(r'''| /\ | |- | |- |--| |\ /| |-''')
print(r'''|/ \| |- |_ |_ |__| | \/ | |_''')
__lowerCAmelCase = 1
while K:
__lowerCAmelCase = int(input('''enter the number and , and see the magic : '''))
print()
pretty_print(user_number)
__lowerCAmelCase = int(input('''press 0 to exit... and 1 to continue...'''))
print('''Good Bye...''')
| 107
| 1
|
from typing import Any
def __lowercase ( lowerCamelCase : list , lowerCamelCase : list , lowerCamelCase : dict , lowerCamelCase : dict , lowerCamelCase : dict , ):
_validation(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
# Creates data structures and fill initial step
UpperCamelCase_ : dict = {}
UpperCamelCase_ : dict = {}
for state in states_space:
UpperCamelCase_ : List[str] = observations_space[0]
UpperCamelCase_ : Dict = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
UpperCamelCase_ : Optional[int] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(lowerCamelCase ) ):
UpperCamelCase_ : str = observations_space[o]
UpperCamelCase_ : Optional[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
UpperCamelCase_ : Optional[int] = ''
UpperCamelCase_ : int = -1
for k_state in states_space:
UpperCamelCase_ : Dict = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
UpperCamelCase_ : Union[str, Any] = probability
UpperCamelCase_ : Dict = k_state
# Update probabilities and pointers dicts
UpperCamelCase_ : Tuple = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
UpperCamelCase_ : str = arg_max
# The final observation
UpperCamelCase_ : Optional[Any] = observations_space[len(lowerCamelCase ) - 1]
# argmax for given final observation
UpperCamelCase_ : Tuple = ''
UpperCamelCase_ : List[Any] = -1
for k_state in states_space:
UpperCamelCase_ : List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
UpperCamelCase_ : int = probability
UpperCamelCase_ : Any = k_state
UpperCamelCase_ : str = arg_max
# Process pointers backwards
UpperCamelCase_ : Optional[Any] = last_state
UpperCamelCase_ : Optional[Any] = []
for o in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
result.append(lowerCamelCase )
UpperCamelCase_ : str = pointers[previous, observations_space[o]]
result.reverse()
return result
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , ):
_validate_not_empty(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
_validate_lists(lowerCamelCase , lowerCamelCase )
_validate_dicts(
lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('There\'s an empty parameter' )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Any ):
_validate_list(lowerCamelCase , 'observations_space' )
_validate_list(lowerCamelCase , 'states_space' )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : str ):
if not isinstance(_object , lowerCamelCase ):
UpperCamelCase_ : List[str] = F"{var_name} must be a list"
raise ValueError(lowerCamelCase )
else:
for x in _object:
if not isinstance(lowerCamelCase , lowerCamelCase ):
UpperCamelCase_ : Any = F"{var_name} must be a list of strings"
raise ValueError(lowerCamelCase )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : Any , ):
_validate_dict(lowerCamelCase , 'initial_probabilities' , lowerCamelCase )
_validate_nested_dict(lowerCamelCase , 'transition_probabilities' )
_validate_nested_dict(lowerCamelCase , 'emission_probabilities' )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : str ):
_validate_dict(_object , lowerCamelCase , lowerCamelCase )
for x in _object.values():
_validate_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : str , lowerCamelCase : type , lowerCamelCase : bool = False ):
if not isinstance(_object , lowerCamelCase ):
UpperCamelCase_ : Optional[Any] = F"{var_name} must be a dict"
raise ValueError(lowerCamelCase )
if not all(isinstance(lowerCamelCase , lowerCamelCase ) for x in _object ):
UpperCamelCase_ : str = F"{var_name} all keys must be strings"
raise ValueError(lowerCamelCase )
if not all(isinstance(lowerCamelCase , lowerCamelCase ) for x in _object.values() ):
UpperCamelCase_ : Union[str, Any] = 'nested dictionary ' if nested else ''
UpperCamelCase_ : Union[str, Any] = F"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(lowerCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 175
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _lowercase ( snake_case_ ):
lowercase = 'megatron-bert'
def __init__( self : List[str] , snake_case : Tuple=2_9_0_5_6 , snake_case : Dict=1_0_2_4 , snake_case : Dict=2_4 , snake_case : Union[str, Any]=1_6 , snake_case : Optional[int]=4_0_9_6 , snake_case : Optional[int]="gelu" , snake_case : Any=0.1 , snake_case : Tuple=0.1 , snake_case : Optional[int]=5_1_2 , snake_case : List[Any]=2 , snake_case : Tuple=0.02 , snake_case : Optional[Any]=1e-12 , snake_case : str=0 , snake_case : Optional[int]="absolute" , snake_case : Union[str, Any]=True , **snake_case : Any , ) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=snake_case , **snake_case )
UpperCamelCase_ : Optional[Any] = vocab_size
UpperCamelCase_ : Any = hidden_size
UpperCamelCase_ : Union[str, Any] = num_hidden_layers
UpperCamelCase_ : List[Any] = num_attention_heads
UpperCamelCase_ : str = hidden_act
UpperCamelCase_ : List[str] = intermediate_size
UpperCamelCase_ : List[Any] = hidden_dropout_prob
UpperCamelCase_ : Any = attention_probs_dropout_prob
UpperCamelCase_ : Tuple = max_position_embeddings
UpperCamelCase_ : Dict = type_vocab_size
UpperCamelCase_ : Optional[int] = initializer_range
UpperCamelCase_ : Optional[Any] = layer_norm_eps
UpperCamelCase_ : Dict = position_embedding_type
UpperCamelCase_ : List[str] = use_cache
| 175
| 1
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
snake_case : Optional[int] = logging.get_logger(__name__)
logging.set_verbosity_info()
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
if "xprophetnet" in prophetnet_checkpoint_path:
a__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase )
a__ , a__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__lowerCAmelCase , output_loading_info=__lowerCAmelCase )
else:
a__ = ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase )
a__ , a__ = ProphetNetForConditionalGeneration.from_pretrained(
__lowerCAmelCase , output_loading_info=__lowerCAmelCase )
a__ = ['key_proj', 'value_proj', 'query_proj']
a__ = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
a__ = key.split('.' )
if attributes[0] == "lm_head":
a__ = prophet
a__ = prophet_old
else:
a__ = prophet.prophetnet
a__ = prophet_old.model
a__ = False
for attribute in attributes:
if attribute in mapping:
a__ = mapping[attribute]
if not hasattr(__lowerCAmelCase , __lowerCAmelCase ) and len(__lowerCAmelCase ) > 0:
a__ = attribute
elif hasattr(__lowerCAmelCase , __lowerCAmelCase ):
a__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
a__ = old_model.weight
logger.info(F'{attribute} is initialized.' )
a__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
a__ = old_model.bias
logger.info(F'{attribute} is initialized' )
a__ = True
break
elif attribute in special_keys and hasattr(__lowerCAmelCase , 'in_proj_weight' ):
a__ = old_model.in_proj_weight.shape[0] // 3
a__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
a__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
a__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
a__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
a__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
a__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
a__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
a__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
a__ = nn.Parameter(old_model.embed_positions.weight[:5_1_2, :] )
a__ = True
break
if attribute.isdigit():
a__ = model[int(__lowerCAmelCase )]
a__ = old_model[int(__lowerCAmelCase )]
else:
a__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
if old_attribute == "":
a__ = old_model
else:
if not hasattr(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(F'{old_model} does not have {old_attribute}' )
a__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
if not is_key_init:
raise ValueError(F'{key} was not correctly initialized!' )
print(F'Saving model to {pytorch_dump_folder_path}' )
prophet.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
snake_case : Tuple = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 109
|
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class snake_case_ (unittest.TestCase ):
def lowerCamelCase__( self :List[Any] ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase__( self :int ) -> Optional[Any]:
a__ , a__ = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' ,from_pt=__snake_case ,dtype=jnp.bfloataa )
a__ , a__ = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' ,controlnet=__snake_case ,from_pt=__snake_case ,dtype=jnp.bfloataa )
a__ = controlnet_params
a__ = 'bird'
a__ = jax.device_count()
a__ = pipe.prepare_text_inputs([prompts] * num_samples )
a__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
a__ = pipe.prepare_image_inputs([canny_image] * num_samples )
a__ = jax.random.PRNGKey(0 )
a__ = jax.random.split(__snake_case ,jax.device_count() )
a__ = replicate(__snake_case )
a__ = shard(__snake_case )
a__ = shard(__snake_case )
a__ = pipe(
prompt_ids=__snake_case ,image=__snake_case ,params=__snake_case ,prng_seed=__snake_case ,num_inference_steps=50 ,jit=__snake_case ,).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
a__ = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a__ = images[0, 2_53:2_56, 2_53:2_56, -1]
a__ = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a__ = jnp.array(
[0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCamelCase__( self :Optional[Any] ) -> Optional[Any]:
a__ , a__ = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' ,from_pt=__snake_case ,dtype=jnp.bfloataa )
a__ , a__ = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' ,controlnet=__snake_case ,from_pt=__snake_case ,dtype=jnp.bfloataa )
a__ = controlnet_params
a__ = 'Chef in the kitchen'
a__ = jax.device_count()
a__ = pipe.prepare_text_inputs([prompts] * num_samples )
a__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
a__ = pipe.prepare_image_inputs([pose_image] * num_samples )
a__ = jax.random.PRNGKey(0 )
a__ = jax.random.split(__snake_case ,jax.device_count() )
a__ = replicate(__snake_case )
a__ = shard(__snake_case )
a__ = shard(__snake_case )
a__ = pipe(
prompt_ids=__snake_case ,image=__snake_case ,params=__snake_case ,prng_seed=__snake_case ,num_inference_steps=50 ,jit=__snake_case ,).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
a__ = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a__ = images[0, 2_53:2_56, 2_53:2_56, -1]
a__ = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a__ = jnp.array(
[[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 109
| 1
|
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 262
|
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_UpperCAmelCase : str ="""docs/source/en"""
_UpperCAmelCase : Optional[int] ="""."""
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : int = f.readlines()
# Find the start prompt.
lowerCAmelCase_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase_ : List[str] = 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
# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase : Optional[Any] ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase : Optional[int] =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase : Dict =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase : Optional[Any] =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : Optional[int] =direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : Tuple = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
lowerCAmelCase_ : int = (width - text_length) // 2
lowerCAmelCase_ : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase ( )-> str:
lowerCAmelCase_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase_ : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCAmelCase_ : List[Any] = {name: config.replace('''Config''' , '''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
lowerCAmelCase_ : Tuple = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[Any] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
lowerCAmelCase_ : Union[str, Any] = slow_tokenizers
lowerCAmelCase_ : List[str] = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowerCAmelCase_ : int = fast_tokenizers
lowerCAmelCase_ : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = tf_models
lowerCAmelCase_ : str = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = flax_models
lowerCAmelCase_ : Union[str, Any] = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Any = pt_models
lowerCAmelCase_ : List[Any] = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCAmelCase_ : Union[str, Any] = True
break
# Try again after removing the last word in the name
lowerCAmelCase_ : Any = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
lowerCAmelCase_ : int = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCAmelCase_ : Tuple = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
lowerCAmelCase_ : Union[str, Any] = [len(lowerCAmelCase_ ) + 2 for c in columns]
lowerCAmelCase_ : Optional[Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
lowerCAmelCase_ : Dict = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowerCAmelCase_ : List[str] = {True: '''✅''', False: '''❌'''}
for name in model_names:
lowerCAmelCase_ : List[Any] = model_name_to_prefix[name]
lowerCAmelCase_ : Union[str, Any] = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def lowerCAmelCase ( lowerCAmelCase_=False )-> Tuple:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowerCAmelCase_ : Tuple = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''index.md''' ) , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Tuple =parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 262
| 1
|
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lowerCAmelCase__ = """FlavaImageProcessor"""
lowerCAmelCase__ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Optional[Any] , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : int):
'''simple docstring'''
__lowercase =None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _lowerCAmelCase , )
__lowercase =kwargs.pop('feature_extractor')
__lowercase =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__(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =self.image_processor
def __call__( self : List[Any] , _lowerCAmelCase : Optional[ImageInput] = None , _lowerCAmelCase : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , _lowerCAmelCase : bool = True , _lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , _lowerCAmelCase : Union[bool, str, TruncationStrategy] = False , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : int = 0 , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = True , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , **_lowerCAmelCase : List[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:
__lowercase =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 , )
if images is not None:
__lowercase =self.image_processor(
_lowerCAmelCase , return_image_mask=_lowerCAmelCase , return_codebook_pixels=_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase , )
if text is not None and images is not None:
encoding.update(_lowerCAmelCase)
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCAmelCase) , tensor_type=_lowerCAmelCase)
def __lowerCamelCase ( self : str , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[int]):
'''simple docstring'''
return self.tokenizer.batch_decode(*_lowerCAmelCase , **_lowerCAmelCase)
def __lowerCamelCase ( self : Optional[int] , *_lowerCAmelCase : Tuple , **_lowerCAmelCase : Optional[int]):
'''simple docstring'''
return self.tokenizer.decode(*_lowerCAmelCase , **_lowerCAmelCase)
@property
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.tokenizer.model_input_names
__lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _lowerCAmelCase , )
return self.image_processor_class
@property
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _lowerCAmelCase , )
return self.image_processor
| 48
|
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
lowerCamelCase = ["""\nclass""", """\ndef""", """\n#""", """\n@""", """\nprint""", """\nif"""]
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : Optional[int]=1):
'''simple docstring'''
__lowercase =tokenizer
__lowercase =dataset
__lowercase =len(_lowerCAmelCase) if n_tasks is None else n_tasks
__lowercase =n_copies
def __iter__( self : Union[str, Any]):
'''simple docstring'''
__lowercase =[]
for task in range(self.n_tasks):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip())
__lowercase =self.tokenizer(_lowerCAmelCase , padding=_lowerCAmelCase , return_tensors='pt')
for task in range(self.n_tasks):
for _ in range(self.n_copies):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[Any]):
'''simple docstring'''
__lowercase =start_length
__lowercase =eof_strings
__lowercase =tokenizer
def __call__( self : List[str] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , **_lowerCAmelCase : Any):
'''simple docstring'''
__lowercase =self.tokenizer.batch_decode(input_ids[:, self.start_length :])
__lowercase =[]
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings))
return all(_lowerCAmelCase)
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =re.split('(%s)' % '|'.join(_lowerCAmelCase ) , _lowerCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=20 , **_lowerCAmelCase ):
"""simple docstring"""
__lowercase =defaultdict(_lowerCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCAmelCase ) ):
with torch.no_grad():
__lowercase =batch['ids'].shape[-1]
__lowercase =accelerator.unwrap_model(_lowerCAmelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCAmelCase , **_lowerCAmelCase )
# each task is generated batch_size times
__lowercase =batch['task_id'].repeat(_lowerCAmelCase )
__lowercase =accelerator.pad_across_processes(
_lowerCAmelCase , dim=1 , pad_index=tokenizer.pad_token_id )
__lowercase , __lowercase =accelerator.gather((generated_tokens, generated_tasks) )
__lowercase =generated_tokens.cpu().numpy()
__lowercase =generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCAmelCase , _lowerCAmelCase ):
gen_token_dict[task].append(_lowerCAmelCase )
__lowercase =[[] for _ in range(_lowerCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__lowercase =tokenizer.decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
code_gens[task].append(remove_last_block(_lowerCAmelCase ) )
return code_gens
def _A ( ):
"""simple docstring"""
__lowercase =HfArgumentParser(_lowerCAmelCase )
__lowercase =parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__lowercase =args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__lowercase ='false'
if args.num_workers is None:
__lowercase =multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__lowercase =Accelerator()
set_seed(args.seed , device_specific=_lowerCAmelCase )
# Load model and tokenizer
__lowercase =AutoTokenizer.from_pretrained(args.model_ckpt )
__lowercase =tokenizer.eos_token
__lowercase =AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__lowercase ={
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCAmelCase , _lowerCAmelCase )] ),
}
# Load evaluation dataset and metric
__lowercase =load_dataset('openai_humaneval' )
__lowercase =load_metric('code_eval' )
__lowercase =args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
__lowercase =args.n_samples // args.batch_size
__lowercase =TokenizedDataset(_lowerCAmelCase , human_eval['test'] , n_copies=_lowerCAmelCase , n_tasks=_lowerCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
__lowercase =DataLoader(_lowerCAmelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__lowercase =code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
__lowercase , __lowercase =accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase )
__lowercase =complete_code(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , n_tasks=_lowerCAmelCase , batch_size=args.batch_size , **_lowerCAmelCase , )
if accelerator.is_main_process:
__lowercase =[]
for task in tqdm(range(_lowerCAmelCase ) ):
__lowercase =human_eval['test'][task]['test']
__lowercase =f"""check({human_eval['test'][task]['entry_point']})"""
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
__lowercase , __lowercase =code_eval_metric.compute(
references=_lowerCAmelCase , predictions=_lowerCAmelCase , num_workers=args.num_workers )
print(f"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 48
| 1
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
A__: Union[str, Any] = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
A__: List[Any] = requests.get(image_url).content
A__: List[str] = F"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(F"Done. Image saved to disk as {file_name}.")
| 276
|
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring'''
_a , _a : List[str] =text, pattern
_a , _a : Union[str, Any] =len(SCREAMING_SNAKE_CASE ), len(SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :str ) -> int:
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def __UpperCAmelCase ( self :Union[str, Any] , SCREAMING_SNAKE_CASE :int ) -> int:
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def __UpperCAmelCase ( self :Union[str, Any] ) -> list[int]:
'''simple docstring'''
# searches pattern in text and returns index positions
_a : Union[str, Any] =[]
for i in range(self.textLen - self.patLen + 1 ):
_a : Any =self.mismatch_in_text(SCREAMING_SNAKE_CASE )
if mismatch_index == -1:
positions.append(SCREAMING_SNAKE_CASE )
else:
_a : int =self.match_in_pattern(self.text[mismatch_index] )
_a : List[str] =(
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
A__: Any = '''ABAABA'''
A__: int = '''AB'''
A__: Optional[int] = BoyerMooreSearch(text, pattern)
A__: Optional[Any] = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 276
| 1
|
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class lowerCAmelCase_ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( *_UpperCAmelCase , **_UpperCAmelCase ):
pass
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> str:
"""simple docstring"""
snake_case_ = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
__snake_case = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = DepthEstimationPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , _UpperCAmelCase )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , _UpperCAmelCase , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def UpperCamelCase__ ( self ):
pass
@slow
@require_torch
def UpperCamelCase__ ( self ):
snake_case_ = '''Intel/dpt-large'''
snake_case_ = pipeline('''depth-estimation''' , model=_UpperCAmelCase )
snake_case_ = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case_ = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.662 )
@require_torch
def UpperCamelCase__ ( self ):
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 353
|
# Function to print upper half of diamond (pyramid)
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Dict:
"""simple docstring"""
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 , i + 1 ): # printing stars
print('''* ''' , end='''''' )
print()
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Dict:
"""simple docstring"""
for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ): # printing stars
print('''* ''' , end='''''' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(''' ''' , end='''''' )
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Any:
"""simple docstring"""
if n <= 0:
print(''' ... .... nothing printing :(''' )
return
floyd(SCREAMING_SNAKE_CASE ) # upper half
reverse_floyd(SCREAMING_SNAKE_CASE ) # lower half
if __name__ == "__main__":
print(r"""| /\ | |- | |- |--| |\ /| |-""")
print(r"""|/ \| |- |_ |_ |__| | \/ | |_""")
UpperCAmelCase = 1
while K:
UpperCAmelCase = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
UpperCAmelCase = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 267
| 0
|
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCAmelCase : str = TypeVar('T')
class snake_case__ (Generic[T] ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : bool = True ) -> None:
a = {} # dictionary of lists
a = directed
def __UpperCAmelCase ( self : str , __lowerCamelCase : T , __lowerCamelCase : T ) -> GraphAdjacencyList[T]:
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCamelCase )
self.adj_list[destination_vertex].append(__lowerCamelCase )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCamelCase )
a = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(__lowerCamelCase )
a = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
a = [destination_vertex]
a = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCamelCase )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCamelCase )
a = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
a = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
a = [destination_vertex]
a = []
return self
def __repr__( self : Tuple ) -> str:
return pformat(self.adj_list )
| 107
|
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : List[Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class snake_case__ (_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = GPTSwaTokenizer
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
SCREAMING_SNAKE_CASE_ : Any = False
def __UpperCAmelCase ( self : Tuple ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
a = GPTSwaTokenizer(__lowerCamelCase , eos_token="<unk>" , bos_token="<unk>" , pad_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[Any] ) -> Any:
a = "This is a test"
a = "This is a test"
return input_text, output_text
def __UpperCAmelCase ( self : List[Any] ) -> List[str]:
a = "<s>"
a = 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 : List[Any] ) -> int:
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(__lowerCamelCase ) , 20_00 )
def __UpperCAmelCase ( self : Optional[int] ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 20_00 )
def __UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
a = GPTSwaTokenizer(__lowerCamelCase )
a = tokenizer.tokenize("This is a test" )
self.assertListEqual(__lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [4_65, 2_87, 2_65, 6_31, 8_42] )
a = tokenizer.tokenize("I was born in 92000, and this is falsé." )
# fmt: off
self.assertListEqual(
__lowerCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] , )
# fmt: on
a = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] , )
a = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
# fmt: off
self.assertListEqual(
__lowerCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] )
# fmt: on
def __UpperCAmelCase ( self : List[Any] ) -> str:
a = GPTSwaTokenizer(__lowerCamelCase )
a = ["This is a test", "I was born in 92000, and this is falsé."]
a = [
[4_65, 2_87, 2_65, 6_31, 8_42],
[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertListEqual(tokenizer.encode_fast(__lowerCamelCase ) , __lowerCamelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertEqual(tokenizer.decode_fast(__lowerCamelCase ) , __lowerCamelCase )
@slow
def __UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
a = [
"<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')",
"Hey there, how are you doing this fine day?",
"This is a text with a trailing spaces followed by a dot .",
"Häj sväjs lillebrör! =)",
"Det är inget fel på Mr. Cool",
]
# fmt: off
a = {"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="AI-Sweden/gpt-sw3-126m" , sequences=__lowerCamelCase , )
| 107
| 1
|
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE = 5_0 ) -> Union[str, Any]:
__lowerCAmelCase: Dict = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 369
|
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class snake_case ( __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Tuple = PriorTransformer
SCREAMING_SNAKE_CASE_ : List[str] = """hidden_states"""
@property
def lowercase_ ( self : Dict)-> str:
'''simple docstring'''
__lowerCAmelCase: str = 4
__lowerCAmelCase: int = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: str = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Any = floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : str=0)-> str:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = 4
__lowerCAmelCase: Dict = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: List[str] = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Tuple = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: List[Any] = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowercase_ ( self : Dict)-> List[Any]:
'''simple docstring'''
return (4, 8)
@property
def lowercase_ ( self : Optional[int])-> int:
'''simple docstring'''
return (4, 8)
def lowercase_ ( self : Optional[int])-> Tuple:
'''simple docstring'''
__lowerCAmelCase: str = {
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_layers": 2,
"embedding_dim": 8,
"num_embeddings": 7,
"additional_embeddings": 4,
}
__lowerCAmelCase: Any = self.dummy_input
return init_dict, inputs_dict
def lowercase_ ( self : List[Any])-> int:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = PriorTransformer.from_pretrained(
"hf-internal-testing/prior-dummy" , output_loading_info=UpperCamelCase__)
self.assertIsNotNone(UpperCamelCase__)
self.assertEqual(len(loading_info["missing_keys"]) , 0)
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = model(**self.dummy_input)[0]
assert hidden_states is not None, "Make sure output is not None"
def lowercase_ ( self : List[str])-> Tuple:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = self.prepare_init_args_and_inputs_for_common()
__lowerCAmelCase: Tuple = self.model_class(**UpperCamelCase__)
__lowerCAmelCase: List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase: List[Any] = [*signature.parameters.keys()]
__lowerCAmelCase: Any = ["hidden_states", "timestep"]
self.assertListEqual(arg_names[:2] , UpperCamelCase__)
def lowercase_ ( self : Optional[int])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
__lowerCAmelCase: Union[str, Any] = model.to(UpperCamelCase__)
if hasattr(UpperCamelCase__ , "set_default_attn_processor"):
model.set_default_attn_processor()
__lowerCAmelCase: str = self.get_dummy_seed_input()
with torch.no_grad():
__lowerCAmelCase: Dict = model(**UpperCamelCase__)[0]
__lowerCAmelCase: Dict = output[0, :5].flatten().cpu()
print(UpperCamelCase__)
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
__lowerCAmelCase: List[str] = torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
self.assertTrue(torch_all_close(UpperCamelCase__ , UpperCamelCase__ , rtol=1e-2))
@slow
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : int , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : int=7_7 , UpperCamelCase__ : Any=0)-> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = batch_size
__lowerCAmelCase: Any = embedding_dim
__lowerCAmelCase: Dict = num_embeddings
__lowerCAmelCase: Dict = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: str = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: int = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : List[Any])-> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[1_3, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],
[3_7, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],
# fmt: on
])
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" , subfolder="prior")
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = self.get_dummy_seed_input(seed=UpperCamelCase__)
with torch.no_grad():
__lowerCAmelCase: Optional[Any] = model(**UpperCamelCase__)[0]
assert list(sample.shape) == [1, 7_6_8]
__lowerCAmelCase: Dict = sample[0, :8].flatten().cpu()
print(UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = torch.tensor(UpperCamelCase__)
assert torch_all_close(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3)
| 108
| 0
|
"""simple docstring"""
from collections import defaultdict
from math import gcd
def _snake_case ( UpperCamelCase : int = 1500000 ):
UpperCAmelCase : defaultdict = defaultdict(UpperCamelCase )
UpperCAmelCase : Union[str, Any] = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , UpperCamelCase , 2 ):
if gcd(UpperCamelCase , UpperCamelCase ) > 1:
continue
UpperCAmelCase : str = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(UpperCamelCase , limit + 1 , UpperCamelCase ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 109
|
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *UpperCamelCase : str , UpperCamelCase : Optional[Union[Dict, Any]] = None , UpperCamelCase : Tuple=True , UpperCamelCase : Optional[int]=2 ):
from .. import __version__
UpperCAmelCase : Tuple = take_from
UpperCAmelCase : Optional[Any] = ()
if not isinstance(args[0] , UpperCamelCase ):
UpperCAmelCase : List[str] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(UpperCamelCase ).base_version ) >= version.parse(UpperCamelCase ):
raise ValueError(
F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
F" version {__version__} is >= {version_name}" )
UpperCAmelCase : Optional[int] = None
if isinstance(UpperCamelCase , UpperCamelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(UpperCamelCase ),)
UpperCAmelCase : List[str] = F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(UpperCamelCase , UpperCamelCase ):
values += (getattr(UpperCamelCase , UpperCamelCase ),)
UpperCAmelCase : List[Any] = F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
UpperCAmelCase : int = F"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
UpperCAmelCase : Optional[Any] = warning + """ """ if standard_warn else """"""
warnings.warn(warning + message , UpperCamelCase , stacklevel=UpperCamelCase )
if isinstance(UpperCamelCase , UpperCamelCase ) and len(UpperCamelCase ) > 0:
UpperCAmelCase : Optional[int] = inspect.getouterframes(inspect.currentframe() )[1]
UpperCAmelCase : Union[str, Any] = call_frame.filename
UpperCAmelCase : List[Any] = call_frame.lineno
UpperCAmelCase : List[str] = call_frame.function
UpperCAmelCase , UpperCAmelCase : Optional[int] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`" )
if len(UpperCamelCase ) == 0:
return
elif len(UpperCamelCase ) == 1:
return values[0]
return values
| 109
| 1
|
"""simple docstring"""
import math
class _lowercase :
"""simple docstring"""
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : list[list[float]] , UpperCamelCase__ : list[int] ) -> int:
'''simple docstring'''
__UpperCamelCase =0.0
__UpperCamelCase =0.0
for i in range(len(UpperCamelCase__ ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def UpperCAmelCase_ ( self : str , UpperCamelCase__ : list[list[int | float]] , UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : float ) -> list[list[int | float]]:
'''simple docstring'''
for i in range(len(UpperCamelCase__ ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase =[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
__UpperCamelCase =[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
__UpperCamelCase =SelfOrganizingMap()
__UpperCamelCase =3
__UpperCamelCase =0.5
for _ in range(__UpperCamelCase ):
for j in range(len(__UpperCamelCase ) ):
# training sample
__UpperCamelCase =training_samples[j]
# Compute the winning vector
__UpperCamelCase =self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# Update the winning vector
__UpperCamelCase =self_organizing_map.update(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# classify test sample
__UpperCamelCase =[0, 0, 0, 1]
__UpperCamelCase =self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# results
print(F"""Clusters that the test sample belongs to : {winner}""" )
print(F"""Weights that have been trained : {weights}""" )
# running the main() function
if __name__ == "__main__":
main()
| 85
|
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase (__UpperCamelCase : dict , __UpperCamelCase : str , __UpperCamelCase : set , __UpperCamelCase : set , __UpperCamelCase : dict , __UpperCamelCase : dict , __UpperCamelCase : PriorityQueue , __UpperCamelCase : dict , __UpperCamelCase : float | int , ):
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__UpperCamelCase =cst_fwd.get(__UpperCamelCase , np.inf )
__UpperCamelCase =cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__UpperCamelCase =new_cost_f
__UpperCamelCase =v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__UpperCamelCase =cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : dict , __UpperCamelCase : dict ):
"""simple docstring"""
__UpperCamelCase =-1
__UpperCamelCase =set()
__UpperCamelCase =set()
__UpperCamelCase ={source: 0}
__UpperCamelCase ={destination: 0}
__UpperCamelCase ={source: None}
__UpperCamelCase ={destination: None}
__UpperCamelCase =PriorityQueue()
__UpperCamelCase =PriorityQueue()
__UpperCamelCase =np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__UpperCamelCase , __UpperCamelCase =queue_forward.get()
visited_forward.add(__UpperCamelCase )
__UpperCamelCase , __UpperCamelCase =queue_backward.get()
visited_backward.add(__UpperCamelCase )
__UpperCamelCase =pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
__UpperCamelCase =pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__UpperCamelCase =shortest_distance
return shortest_path_distance
__lowercase = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
__lowercase = {
'''B''': [['''E''', 1]],
'''C''': [['''B''', 1]],
'''D''': [['''C''', 1]],
'''F''': [['''D''', 1], ['''G''', 1]],
'''E''': [[None, np.inf]],
'''G''': [['''E''', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85
| 1
|
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def A ( _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Dict = SwinConfig(image_size=192 )
if "base" in model_name:
lowerCamelCase : int = 6
lowerCamelCase : str = 128
lowerCamelCase : int = (2, 2, 18, 2)
lowerCamelCase : Optional[int] = (4, 8, 16, 32)
elif "large" in model_name:
lowerCamelCase : Union[str, Any] = 12
lowerCamelCase : List[Any] = 192
lowerCamelCase : int = (2, 2, 18, 2)
lowerCamelCase : Optional[int] = (6, 12, 24, 48)
else:
raise ValueError("Model not supported, only supports base and large variants" )
lowerCamelCase : List[Any] = window_size
lowerCamelCase : List[Any] = embed_dim
lowerCamelCase : Tuple = depths
lowerCamelCase : Any = num_heads
return config
def A ( _SCREAMING_SNAKE_CASE ) -> str:
if "encoder.mask_token" in name:
lowerCamelCase : Optional[Any] = name.replace("encoder.mask_token" ,"embeddings.mask_token" )
if "encoder.patch_embed.proj" in name:
lowerCamelCase : Optional[int] = name.replace("encoder.patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
if "encoder.patch_embed.norm" in name:
lowerCamelCase : int = name.replace("encoder.patch_embed.norm" ,"embeddings.norm" )
if "attn.proj" in name:
lowerCamelCase : Dict = name.replace("attn.proj" ,"attention.output.dense" )
if "attn" in name:
lowerCamelCase : Tuple = name.replace("attn" ,"attention.self" )
if "norm1" in name:
lowerCamelCase : Any = name.replace("norm1" ,"layernorm_before" )
if "norm2" in name:
lowerCamelCase : List[str] = name.replace("norm2" ,"layernorm_after" )
if "mlp.fc1" in name:
lowerCamelCase : int = name.replace("mlp.fc1" ,"intermediate.dense" )
if "mlp.fc2" in name:
lowerCamelCase : Optional[Any] = name.replace("mlp.fc2" ,"output.dense" )
if name == "encoder.norm.weight":
lowerCamelCase : Optional[int] = "layernorm.weight"
if name == "encoder.norm.bias":
lowerCamelCase : Any = "layernorm.bias"
if "decoder" in name:
pass
else:
lowerCamelCase : Optional[int] = "swin." + name
return name
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
for key in orig_state_dict.copy().keys():
lowerCamelCase : List[Any] = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "attn_mask" in key:
pass
elif "qkv" in key:
lowerCamelCase : Optional[Any] = key.split("." )
lowerCamelCase : Union[str, Any] = int(key_split[2] )
lowerCamelCase : Any = int(key_split[4] )
lowerCamelCase : Any = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowerCamelCase : List[str] = val[:dim, :]
lowerCamelCase : List[str] = val[
dim : dim * 2, :
]
lowerCamelCase : List[str] = val[-dim:, :]
else:
lowerCamelCase : Tuple = val[
:dim
]
lowerCamelCase : List[Any] = val[
dim : dim * 2
]
lowerCamelCase : Dict = val[
-dim:
]
else:
lowerCamelCase : List[str] = val
return orig_state_dict
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Dict = torch.load(_SCREAMING_SNAKE_CASE ,map_location="cpu" )["model"]
lowerCamelCase : Dict = get_swin_config(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[Any] = SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE )
model.eval()
lowerCamelCase : Optional[int] = convert_state_dict(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Any = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase : str = ViTImageProcessor(size={"height": 192, "width": 192} )
lowerCamelCase : Optional[Any] = Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE ).raw )
lowerCamelCase : Tuple = image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="pt" )
with torch.no_grad():
lowerCamelCase : List[str] = model(**_SCREAMING_SNAKE_CASE ).logits
print(outputs.keys() )
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(_SCREAMING_SNAKE_CASE )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print(f'''Pushing model and image processor for {model_name} to hub''' )
model.push_to_hub(f'''microsoft/{model_name}''' )
image_processor.push_to_hub(f'''microsoft/{model_name}''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='swin-base-simmim-window6-192',
type=str,
choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
help='Name of the Swin SimMIM model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the 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.'
)
SCREAMING_SNAKE_CASE__ : str = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 48
|
import math
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> float:
if (
not isinstance(_SCREAMING_SNAKE_CASE ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value between -1 and 1." )
return apparent_power * power_factor
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> float:
if (
not isinstance(_SCREAMING_SNAKE_CASE ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value between -1 and 1." )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48
| 1
|
import torch
from diffusers import StableDiffusionPipeline
lowercase_ = """path-to-your-trained-model"""
lowercase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
lowercase_ = """A photo of sks dog in a bucket"""
lowercase_ = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 224
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowercase_ = namedtuple("""covid_data""", """cases deaths recovered""")
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/" ):
lowercase__ = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_ ).content ).xpath(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 224
| 1
|
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=2 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=2 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=36 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=6 , _UpperCAmelCase=6 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , _UpperCAmelCase=1000 , ):
lowercase__: Any = parent
lowercase__: Any = batch_size
lowercase__: Optional[Any] = num_channels
lowercase__: str = image_size
lowercase__: List[str] = patch_size
lowercase__: Union[str, Any] = text_seq_length
lowercase__: Dict = is_training
lowercase__: Union[str, Any] = use_input_mask
lowercase__: Optional[int] = use_token_type_ids
lowercase__: int = use_labels
lowercase__: Optional[int] = vocab_size
lowercase__: Optional[int] = hidden_size
lowercase__: Any = num_hidden_layers
lowercase__: Any = num_attention_heads
lowercase__: Optional[int] = intermediate_size
lowercase__: int = hidden_act
lowercase__: List[Any] = hidden_dropout_prob
lowercase__: str = attention_probs_dropout_prob
lowercase__: str = max_position_embeddings
lowercase__: List[str] = type_vocab_size
lowercase__: Dict = type_sequence_label_size
lowercase__: Optional[int] = initializer_range
lowercase__: int = coordinate_size
lowercase__: int = shape_size
lowercase__: Tuple = num_labels
lowercase__: Dict = num_choices
lowercase__: Tuple = scope
lowercase__: Tuple = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
lowercase__: Optional[int] = text_seq_length
lowercase__: int = (image_size // patch_size) ** 2 + 1
lowercase__: Optional[int] = self.text_seq_length + self.image_seq_length
def _snake_case ( self ):
lowercase__: Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
lowercase__: Optional[Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowercase__: Any = bbox[i, j, 3]
lowercase__: Any = bbox[i, j, 1]
lowercase__: Dict = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowercase__: str = bbox[i, j, 2]
lowercase__: Union[str, Any] = bbox[i, j, 0]
lowercase__: List[str] = t
lowercase__: Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__: Optional[int] = None
if self.use_input_mask:
lowercase__: Any = random_attention_mask([self.batch_size, self.text_seq_length] )
lowercase__: int = None
if self.use_token_type_ids:
lowercase__: Tuple = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
lowercase__: int = None
lowercase__: Any = None
if self.use_labels:
lowercase__: List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__: str = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
lowercase__: List[Any] = LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Tuple = LayoutLMvaModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
# text + image
lowercase__: int = model(__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE )
lowercase__: int = model(
__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )
lowercase__: List[str] = model(__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )
lowercase__: List[Any] = model(__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
lowercase__: Optional[int] = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
lowercase__: List[str] = model(pixel_values=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Tuple = self.num_labels
lowercase__: str = LayoutLMvaForSequenceClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowercase__: Optional[Any] = model(
__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Union[str, Any] = self.num_labels
lowercase__: Optional[int] = LayoutLMvaForTokenClassification(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowercase__: Tuple = model(
__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Dict = LayoutLMvaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowercase__: int = model(
__SCREAMING_SNAKE_CASE , bbox=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , start_positions=__SCREAMING_SNAKE_CASE , end_positions=__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 _snake_case ( self ):
lowercase__: Dict = self.prepare_config_and_inputs()
(
(
lowercase__
), (
lowercase__
), (
lowercase__
), (
lowercase__
), (
lowercase__
), (
lowercase__
), (
lowercase__
), (
lowercase__
),
): Any = config_and_inputs
lowercase__: Optional[int] = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase (_UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Any = False
_UpperCAmelCase :Optional[int] = False
_UpperCAmelCase :Dict = False
_UpperCAmelCase :List[Any] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
_UpperCAmelCase :int = (
{"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
if is_torch_available()
else {}
)
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
return True
def _snake_case ( self ):
lowercase__: Optional[int] = LayoutLMvaModelTester(self )
lowercase__: Optional[int] = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ):
lowercase__: Optional[Any] = copy.deepcopy(__SCREAMING_SNAKE_CASE )
if model_class in get_values(__SCREAMING_SNAKE_CASE ):
lowercase__: Dict = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__SCREAMING_SNAKE_CASE ):
lowercase__: str = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=__SCREAMING_SNAKE_CASE )
elif model_class in get_values(__SCREAMING_SNAKE_CASE ):
lowercase__: List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__SCREAMING_SNAKE_CASE )
lowercase__: Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__SCREAMING_SNAKE_CASE )
elif model_class in [
*get_values(__SCREAMING_SNAKE_CASE ),
]:
lowercase__: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__SCREAMING_SNAKE_CASE )
elif model_class in [
*get_values(__SCREAMING_SNAKE_CASE ),
]:
lowercase__: Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=__SCREAMING_SNAKE_CASE , )
return inputs_dict
def _snake_case ( self ):
self.config_tester.run_common_tests()
def _snake_case ( self ):
lowercase__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
lowercase__: List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__: List[str] = type
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
lowercase__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
lowercase__: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
lowercase__: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE )
@slow
def _snake_case ( self ):
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Tuple = LayoutLMvaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
lowercase__: Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self ):
return LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE ) if is_vision_available() else None
@slow
def _snake_case ( self ):
lowercase__: Optional[Any] = LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(__SCREAMING_SNAKE_CASE )
lowercase__: int = self.default_image_processor
lowercase__: Tuple = prepare_img()
lowercase__: str = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values.to(__SCREAMING_SNAKE_CASE )
lowercase__: Union[str, Any] = torch.tensor([[1, 2]] )
lowercase__: Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
lowercase__: str = model(
input_ids=input_ids.to(__SCREAMING_SNAKE_CASE ) , bbox=bbox.to(__SCREAMING_SNAKE_CASE ) , pixel_values=pixel_values.to(__SCREAMING_SNAKE_CASE ) , )
# verify the logits
lowercase__: List[str] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , __SCREAMING_SNAKE_CASE )
lowercase__: Tuple = torch.tensor(
[[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 177
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = [
'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
'SEWForCTC',
'SEWForSequenceClassification',
'SEWModel',
'SEWPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
UpperCAmelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 267
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 367
|
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 ):
'''simple docstring'''
a__ = ["image_processor", "tokenizer"]
a__ = "BridgeTowerImageProcessor"
a__ = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : Any ) -> Optional[int]:
super().__init__(__lowerCamelCase , __lowerCamelCase )
def __call__( self : Any , __lowerCamelCase : Any , __lowerCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __lowerCamelCase : bool = True , __lowerCamelCase : Union[bool, str, PaddingStrategy] = False , __lowerCamelCase : Union[bool, str, TruncationStrategy] = None , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : int = 0 , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[Union[str, TensorType]] = None , **__lowerCamelCase : Dict , ) -> BatchEncoding:
A : List[Any] = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_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
A : List[Any] = self.image_processor(
__lowerCamelCase , return_tensors=__lowerCamelCase , do_normalize=__lowerCamelCase , do_center_crop=__lowerCamelCase , **__lowerCamelCase )
encoding.update(__lowerCamelCase )
return encoding
def SCREAMING_SNAKE_CASE__ ( self : int , *__lowerCamelCase : List[str] , **__lowerCamelCase : str ) -> List[Any]:
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , *__lowerCamelCase : Optional[int] , **__lowerCamelCase : str ) -> Any:
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[int]:
A : Dict = self.tokenizer.model_input_names
A : Tuple = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 256
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct_text_model'''
__lowerCamelCase = ['''past_key_values''']
__lowerCamelCase = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _snake_case=50244 , _snake_case=768 , _snake_case=64 , _snake_case=2048 , _snake_case=12 , _snake_case=12 , _snake_case=32 , _snake_case=128 , _snake_case=0.1 , _snake_case=1e-6 , _snake_case=1.0 , _snake_case="gelu_new" , _snake_case=0 , _snake_case=False , _snake_case=0 , _snake_case=1 , _snake_case=False , _snake_case=True , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = d_kv
_lowerCAmelCase = d_ff
_lowerCAmelCase = num_layers
_lowerCAmelCase = num_heads
_lowerCAmelCase = relative_attention_num_buckets
_lowerCAmelCase = relative_attention_max_distance
_lowerCAmelCase = dropout_rate
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = use_cache
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = decoder_start_token_id
# for backwards compatibility
_lowerCAmelCase = dense_act_fn
super().__init__(
pad_token_id=_snake_case , eos_token_id=_snake_case , decoder_start_token_id=_snake_case , tie_word_embeddings=_snake_case , is_decoder=_snake_case , **_snake_case , )
@classmethod
def snake_case ( cls , _snake_case , **_snake_case ):
"""simple docstring"""
cls._set_token_in_kwargs(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_snake_case , **_snake_case )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_lowerCAmelCase = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_snake_case , **_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct_vision_model'''
def __init__( self , _snake_case=768 , _snake_case=768 , _snake_case=2048 , _snake_case=64 , _snake_case=12 , _snake_case=12 , _snake_case="gelu_new" , _snake_case=1e-6 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=1e-10 , _snake_case=1.0 , _snake_case=4096 , _snake_case=32 , _snake_case=128 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
_lowerCAmelCase = hidden_size
_lowerCAmelCase = patch_embed_hidden_size
_lowerCAmelCase = d_ff
_lowerCAmelCase = dropout_rate
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = initializer_range
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = dense_act_fn
_lowerCAmelCase = seq_len
_lowerCAmelCase = relative_attention_num_buckets
_lowerCAmelCase = relative_attention_max_distance
_lowerCAmelCase = d_kv
@classmethod
def snake_case ( cls , _snake_case , **_snake_case ):
"""simple docstring"""
cls._set_token_in_kwargs(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_snake_case , **_snake_case )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_lowerCAmelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_snake_case , **_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct'''
__lowerCamelCase = True
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=1.0 , _snake_case=0.02 , _snake_case=False , _snake_case=False , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(tie_word_embeddings=_snake_case , is_encoder_decoder=_snake_case , **_snake_case )
if text_config is None:
_lowerCAmelCase = {}
logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )
if vision_config is None:
_lowerCAmelCase = {}
logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )
_lowerCAmelCase = PixaStructTextConfig(**_snake_case )
_lowerCAmelCase = PixaStructVisionConfig(**_snake_case )
_lowerCAmelCase = self.text_config.decoder_start_token_id
_lowerCAmelCase = self.text_config.pad_token_id
_lowerCAmelCase = self.text_config.eos_token_id
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = initializer_range
_lowerCAmelCase = self.initializer_range
_lowerCAmelCase = self.initializer_range
_lowerCAmelCase = is_vqa
@classmethod
def snake_case ( cls , _snake_case , _snake_case , **_snake_case ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.text_config.to_dict()
_lowerCAmelCase = self.vision_config.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
| 82
|
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("Rate of interest must be >= 0" )
if years_to_repay <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise Exception("Years to repay must be an integer > 0" )
# Yearly rate is divided by 12 to get monthly rate
lowerCAmelCase : Tuple = rate_per_annum / 1_2
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
lowerCAmelCase : List[Any] = years_to_repay * 1_2
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108
| 0
|
def UpperCAmelCase_ (_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__UpperCamelCase : str = mf_knapsack(i - 1 , snake_case_ , snake_case_ , snake_case_ )
else:
__UpperCamelCase : Union[str, Any] = max(
mf_knapsack(i - 1 , snake_case_ , snake_case_ , snake_case_ ) , mf_knapsack(i - 1 , snake_case_ , snake_case_ , j - wt[i - 1] ) + val[i - 1] , )
__UpperCamelCase : List[str] = val
return f[i][j]
def UpperCAmelCase_ (_lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] ):
__UpperCamelCase : Optional[Any] = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
__UpperCamelCase : List[Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
__UpperCamelCase : Dict = dp[i - 1][w_]
return dp[n][w_], dp
def UpperCAmelCase_ (_lowerCAmelCase : int , _lowerCAmelCase : list , _lowerCAmelCase : list ):
if not (isinstance(snake_case_ , (list, tuple) ) and isinstance(snake_case_ , (list, tuple) )):
raise ValueError(
"Both the weights and values vectors must be either lists or tuples" )
__UpperCamelCase : int = len(snake_case_ )
if num_items != len(snake_case_ ):
__UpperCamelCase : Any = (
"""The number of weights must be the same as the number of values.\n"""
F'''But got {num_items} weights and {len(snake_case_ )} values'''
)
raise ValueError(snake_case_ )
for i in range(snake_case_ ):
if not isinstance(wt[i] , snake_case_ ):
__UpperCamelCase : List[Any] = (
"""All weights must be integers but got weight of """
F'''type {type(wt[i] )} at index {i}'''
)
raise TypeError(snake_case_ )
__UpperCamelCase : Union[str, Any] = knapsack(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
__UpperCamelCase : set = set()
_construct_solution(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
return optimal_val, example_optional_set
def UpperCAmelCase_ (_lowerCAmelCase : list , _lowerCAmelCase : list , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : set ):
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case_ , snake_case_ , i - 1 , snake_case_ , snake_case_ )
else:
optimal_set.add(snake_case_ )
_construct_solution(snake_case_ , snake_case_ , i - 1 , j - wt[i - 1] , snake_case_ )
if __name__ == "__main__":
lowercase : str = [3, 2, 4, 4]
lowercase : Optional[int] = [4, 3, 2, 3]
lowercase : Any = 4
lowercase : List[str] = 6
lowercase : Dict = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
lowercase : Any = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
lowercase : List[str] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("optimal_value = ", optimal_solution)
print("An optimal subset corresponding to the optimal value", optimal_subset)
| 355
|
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
lowercase : Any = logging.get_logger(__name__)
lowercase : Dict = {"vocab_file": "spm_char.model"}
lowercase : Tuple = {
"vocab_file": {
"microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
"microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
"microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
}
}
lowercase : Union[str, Any] = {
"microsoft/speecht5_asr": 1024,
"microsoft/speecht5_tts": 1024,
"microsoft/speecht5_vc": 1024,
}
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Optional[Any] = ['input_ids', 'attention_mask']
def __init__( self , __UpperCamelCase , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase = None , **__UpperCamelCase , ) -> None:
'''simple docstring'''
__UpperCamelCase : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCamelCase , )
__UpperCamelCase : List[Any] = vocab_file
__UpperCamelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCamelCase )
@property
def __lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def __lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : Optional[int] = {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> str:
'''simple docstring'''
__UpperCamelCase : Any = self.__dict__.copy()
__UpperCamelCase : Union[str, Any] = None
return state
def __setstate__( self , __UpperCamelCase ) -> List[str]:
'''simple docstring'''
__UpperCamelCase : Dict = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__UpperCamelCase : List[Any] = {}
__UpperCamelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCamelCase ( self , __UpperCamelCase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(__UpperCamelCase , out_type=__UpperCamelCase )
def __lowerCamelCase ( self , __UpperCamelCase ) -> str:
'''simple docstring'''
return self.sp_model.piece_to_id(__UpperCamelCase )
def __lowerCamelCase ( self , __UpperCamelCase ) -> int:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = self.sp_model.IdToPiece(__UpperCamelCase )
return token
def __lowerCamelCase ( self , __UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = []
__UpperCamelCase : Any = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__UpperCamelCase ) + token
__UpperCamelCase : Any = []
else:
current_sub_tokens.append(__UpperCamelCase )
out_string += self.sp_model.decode(__UpperCamelCase )
return out_string.strip()
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False ) -> List[int]:
'''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 )
__UpperCamelCase : str = [1]
if token_ids_a is None:
return ([0] * len(__UpperCamelCase )) + suffix_ones
return ([0] * len(__UpperCamelCase )) + ([0] * len(__UpperCamelCase )) + suffix_ones
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__UpperCamelCase : Optional[Any] = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCamelCase , "wb" ) as fi:
__UpperCamelCase : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(__UpperCamelCase )
return (out_vocab_file,)
| 171
| 0
|
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
_SCREAMING_SNAKE_CASE : int = parser.parse_args()
_SCREAMING_SNAKE_CASE : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor()
_SCREAMING_SNAKE_CASE : str = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
_SCREAMING_SNAKE_CASE : int = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 85
|
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
_SCREAMING_SNAKE_CASE : int = parser.parse_args()
_SCREAMING_SNAKE_CASE : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor()
_SCREAMING_SNAKE_CASE : str = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
_SCREAMING_SNAKE_CASE : int = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 85
| 1
|
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__A = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , __a , __a , __a=False , ) -> Optional[int]:
"""simple docstring"""
output_path.parent.mkdir(parents=lowercase__ , exist_ok=lowercase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowercase__ , lowercase__ , f=output_path.as_posix() , input_names=lowercase__ , output_names=lowercase__ , dynamic_axes=lowercase__ , do_constant_folding=lowercase__ , use_external_data_format=lowercase__ , enable_onnx_checker=lowercase__ , opset_version=lowercase__ , )
else:
export(
lowercase__ , lowercase__ , f=output_path.as_posix() , input_names=lowercase__ , output_names=lowercase__ , dynamic_axes=lowercase__ , do_constant_folding=lowercase__ , opset_version=lowercase__ , )
@torch.no_grad()
def lowerCAmelCase_ ( __a , __a , __a , __a = False ) -> Any:
"""simple docstring"""
lowerCamelCase__: Any =torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowerCamelCase__: Union[str, Any] ="""cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
else:
lowerCamelCase__: Any ="""cpu"""
lowerCamelCase__: Any =StableDiffusionPipeline.from_pretrained(lowercase__ , torch_dtype=lowercase__ ).to(lowercase__ )
lowerCamelCase__: int =Path(lowercase__ )
# TEXT ENCODER
lowerCamelCase__: int =pipeline.text_encoder.config.max_position_embeddings
lowerCamelCase__: int =pipeline.text_encoder.config.hidden_size
lowerCamelCase__: List[Any] =pipeline.tokenizer(
"A sample prompt" , padding="max_length" , max_length=pipeline.tokenizer.model_max_length , truncation=lowercase__ , return_tensors="pt" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=lowercase__ , dtype=torch.intaa )) , output_path=output_path / "text_encoder" / "model.onnx" , ordered_input_names=["input_ids"] , output_names=["last_hidden_state", "pooler_output"] , dynamic_axes={
"input_ids": {0: "batch", 1: "sequence"},
} , opset=lowercase__ , )
del pipeline.text_encoder
# UNET
lowerCamelCase__: Optional[Any] =pipeline.unet.config.in_channels
lowerCamelCase__: Union[str, Any] =pipeline.unet.config.sample_size
lowerCamelCase__: List[Any] =output_path / """unet""" / """model.onnx"""
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , lowercase__ , lowercase__ , lowercase__ ).to(device=lowercase__ , dtype=lowercase__ ),
torch.randn(2 ).to(device=lowercase__ , dtype=lowercase__ ),
torch.randn(2 , lowercase__ , lowercase__ ).to(device=lowercase__ , dtype=lowercase__ ),
False,
) , output_path=lowercase__ , ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"] , output_names=["out_sample"] , dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"timestep": {0: "batch"},
"encoder_hidden_states": {0: "batch", 1: "sequence"},
} , opset=lowercase__ , use_external_data_format=lowercase__ , )
lowerCamelCase__: Union[str, Any] =str(unet_path.absolute().as_posix() )
lowerCamelCase__: int =os.path.dirname(lowercase__ )
lowerCamelCase__: Tuple =onnx.load(lowercase__ )
# clean up existing tensor files
shutil.rmtree(lowercase__ )
os.mkdir(lowercase__ )
# collate external tensor files into one
onnx.save_model(
lowercase__ , lowercase__ , save_as_external_data=lowercase__ , all_tensors_to_one_file=lowercase__ , location="weights.pb" , convert_attribute=lowercase__ , )
del pipeline.unet
# VAE ENCODER
lowerCamelCase__: int =pipeline.vae
lowerCamelCase__: Tuple =vae_encoder.config.in_channels
lowerCamelCase__: List[str] =vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
lowerCamelCase__: List[str] =lambda __a , __a : vae_encoder.encode(lowercase__ , lowercase__ )[0].sample()
onnx_export(
lowercase__ , model_args=(
torch.randn(1 , lowercase__ , lowercase__ , lowercase__ ).to(device=lowercase__ , dtype=lowercase__ ),
False,
) , output_path=output_path / "vae_encoder" / "model.onnx" , ordered_input_names=["sample", "return_dict"] , output_names=["latent_sample"] , dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=lowercase__ , )
# VAE DECODER
lowerCamelCase__: int =pipeline.vae
lowerCamelCase__: int =vae_decoder.config.latent_channels
lowerCamelCase__: Dict =vae_decoder.config.out_channels
# forward only through the decoder part
lowerCamelCase__: Union[str, Any] =vae_encoder.decode
onnx_export(
lowercase__ , model_args=(
torch.randn(1 , lowercase__ , lowercase__ , lowercase__ ).to(device=lowercase__ , dtype=lowercase__ ),
False,
) , output_path=output_path / "vae_decoder" / "model.onnx" , ordered_input_names=["latent_sample", "return_dict"] , output_names=["sample"] , dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=lowercase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
lowerCamelCase__: Union[str, Any] =pipeline.safety_checker
lowerCamelCase__: Tuple =safety_checker.config.vision_config.num_channels
lowerCamelCase__: Union[str, Any] =safety_checker.config.vision_config.image_size
lowerCamelCase__: List[Any] =safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , lowercase__ , lowercase__ , lowercase__ , ).to(device=lowercase__ , dtype=lowercase__ ),
torch.randn(1 , lowercase__ , lowercase__ , lowercase__ ).to(device=lowercase__ , dtype=lowercase__ ),
) , output_path=output_path / "safety_checker" / "model.onnx" , ordered_input_names=["clip_input", "images"] , output_names=["out_images", "has_nsfw_concepts"] , dynamic_axes={
"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
} , opset=lowercase__ , )
del pipeline.safety_checker
lowerCamelCase__: Dict =OnnxRuntimeModel.from_pretrained(output_path / "safety_checker" )
lowerCamelCase__: Optional[int] =pipeline.feature_extractor
else:
lowerCamelCase__: Tuple =None
lowerCamelCase__: Tuple =None
lowerCamelCase__: str =OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / "unet" ) , scheduler=pipeline.scheduler , safety_checker=lowercase__ , feature_extractor=lowercase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(lowercase__ )
print("ONNX pipeline saved to" , lowercase__ )
del pipeline
del onnx_pipeline
lowerCamelCase__: int =OnnxStableDiffusionPipeline.from_pretrained(lowercase__ , provider="CPUExecutionProvider" )
print("ONNX pipeline is loadable" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
__A = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 356
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
__A = logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
lowercase_ = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = field(default=__SCREAMING_SNAKE_CASE , metadata={"help": "The input training data file (a text file)."} )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Overwrite the cached training and evaluation sets"} )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": (
"The maximum total input sequence length after tokenization. If passed, sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": (
"Whether to pad all samples to the maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU."
)
} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowercase_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Optional[int]:
'''simple docstring'''
if self.train_file is not None:
lowerCamelCase__: List[Any] =self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
lowerCamelCase__: List[Any] =self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = 42
lowercase_ = True
lowercase_ = None
lowercase_ = None
def __call__(self : Any , UpperCAmelCase_ : Dict) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: str ="label" if "label" in features[0].keys() else "labels"
lowerCamelCase__: Union[str, Any] =[feature.pop(UpperCAmelCase_) for feature in features]
lowerCamelCase__: Union[str, Any] =len(UpperCAmelCase_)
lowerCamelCase__: int =len(features[0]["input_ids"])
lowerCamelCase__: List[Any] =[
[{k: v[i] for k, v in feature.items()} for i in range(UpperCAmelCase_)] for feature in features
]
lowerCamelCase__: Dict =list(chain(*UpperCAmelCase_))
lowerCamelCase__: Tuple =self.tokenizer.pad(
UpperCAmelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
lowerCamelCase__: Optional[Any] ={k: v.view(UpperCAmelCase_ , UpperCAmelCase_ , -1) for k, v in batch.items()}
# Add back labels
lowerCamelCase__: Optional[Any] =torch.tensor(UpperCAmelCase_ , dtype=torch.intaa)
return batch
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__: str =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.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: List[str] =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Tuple =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_swag" , __a , __a )
# 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()
lowerCamelCase__: Dict =training_args.get_process_log_level()
logger.setLevel(__a )
datasets.utils.logging.set_verbosity(__a )
transformers.utils.logging.set_verbosity(__a )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCamelCase__: List[str] =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase__: Any =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
lowerCamelCase__: List[Any] ={}
if data_args.train_file is not None:
lowerCamelCase__: List[str] =data_args.train_file
if data_args.validation_file is not None:
lowerCamelCase__: Optional[Any] =data_args.validation_file
lowerCamelCase__: List[Any] =data_args.train_file.split("." )[-1]
lowerCamelCase__: int =load_dataset(
__a , data_files=__a , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
lowerCamelCase__: List[Any] =load_dataset(
"swag" , "regular" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase__: int =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase__: List[str] =AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase__: Dict =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
lowerCamelCase__: Optional[int] =[F"""ending{i}""" for i in range(4 )]
lowerCamelCase__: List[str] ="sent1"
lowerCamelCase__: List[str] ="sent2"
if data_args.max_seq_length is None:
lowerCamelCase__: Optional[int] =tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
lowerCamelCase__: Optional[int] =1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
lowerCamelCase__: Any =min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(__a ):
lowerCamelCase__: Tuple =[[context] * 4 for context in examples[context_name]]
lowerCamelCase__: List[Any] =examples[question_header_name]
lowerCamelCase__: Dict =[
[F"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(__a )
]
# Flatten out
lowerCamelCase__: str =list(chain(*__a ) )
lowerCamelCase__: str =list(chain(*__a ) )
# Tokenize
lowerCamelCase__: List[Any] =tokenizer(
__a , __a , truncation=__a , max_length=__a , padding="max_length" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(__a ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
lowerCamelCase__: List[Any] =raw_datasets["train"]
if data_args.max_train_samples is not None:
lowerCamelCase__: Dict =min(len(__a ) , data_args.max_train_samples )
lowerCamelCase__: Any =train_dataset.select(range(__a ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
lowerCamelCase__: Optional[Any] =train_dataset.map(
__a , batched=__a , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
lowerCamelCase__: str =raw_datasets["validation"]
if data_args.max_eval_samples is not None:
lowerCamelCase__: Any =min(len(__a ) , data_args.max_eval_samples )
lowerCamelCase__: List[Any] =eval_dataset.select(range(__a ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
lowerCamelCase__: Tuple =eval_dataset.map(
__a , batched=__a , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
lowerCamelCase__: Any =(
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=__a , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(__a ):
lowerCamelCase__ , lowerCamelCase__: List[str] =eval_predictions
lowerCamelCase__: Optional[int] =np.argmax(__a , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
lowerCamelCase__: Dict =Trainer(
model=__a , args=__a , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=__a , data_collator=__a , compute_metrics=__a , )
# Training
if training_args.do_train:
lowerCamelCase__: List[Any] =None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase__: int =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase__: str =last_checkpoint
lowerCamelCase__: Any =trainer.train(resume_from_checkpoint=__a )
trainer.save_model() # Saves the tokenizer too for easy upload
lowerCamelCase__: int =train_result.metrics
lowerCamelCase__: Optional[int] =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(__a )
)
lowerCamelCase__: Optional[int] =min(__a , len(__a ) )
trainer.log_metrics("train" , __a )
trainer.save_metrics("train" , __a )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowerCamelCase__: int =trainer.evaluate()
lowerCamelCase__: Dict =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__a )
lowerCamelCase__: Tuple =min(__a , len(__a ) )
trainer.log_metrics("eval" , __a )
trainer.save_metrics("eval" , __a )
lowerCamelCase__: str ={
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**__a )
else:
trainer.create_model_card(**__a )
def lowerCAmelCase_ ( __a ) -> Optional[int]:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 273
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : int = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
"""tokenization_deberta""": ["""DebertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[int] = ["""DebertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
"""DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DebertaForMaskedLM""",
"""DebertaForQuestionAnswering""",
"""DebertaForSequenceClassification""",
"""DebertaForTokenClassification""",
"""DebertaModel""",
"""DebertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : List[Any] = [
"""TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDebertaForMaskedLM""",
"""TFDebertaForQuestionAnswering""",
"""TFDebertaForSequenceClassification""",
"""TFDebertaForTokenClassification""",
"""TFDebertaModel""",
"""TFDebertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
lowercase__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 224
|
"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
"""This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
"""It takes two arguments named `image` which should be the original image, and `label` which should be a text """
"""describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
)
_SCREAMING_SNAKE_CASE = """CIDAS/clipseg-rd64-refined"""
_SCREAMING_SNAKE_CASE = """image_segmenter"""
_SCREAMING_SNAKE_CASE = CLIPSegForImageSegmentation
_SCREAMING_SNAKE_CASE = ["""image""", """text"""]
_SCREAMING_SNAKE_CASE = ["""image"""]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
requires_backends(self , ['vision'] )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : "Image" , SCREAMING_SNAKE_CASE_ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : int ):
with torch.no_grad():
lowerCAmelCase_ : List[str] = self.model(**SCREAMING_SNAKE_CASE_ ).logits
return logits
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase_ : Dict = outputs.cpu().detach().numpy()
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Optional[Any] = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 224
| 1
|
'''simple docstring'''
import numpy as np
def _snake_case ( _SCREAMING_SNAKE_CASE : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def _snake_case ( _SCREAMING_SNAKE_CASE : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return vector * sigmoid(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356
|
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
UpperCAmelCase = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def _snake_case ( _SCREAMING_SNAKE_CASE : Tuple ) -> List[Any]:
"""simple docstring"""
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def _snake_case ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
return max(metric_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for gt in ground_truths )
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = [line.strip() for line in open(_SCREAMING_SNAKE_CASE , """r""" ).readlines()]
lowerCAmelCase = []
if args.gold_data_mode == "qa":
lowerCAmelCase = pd.read_csv(_SCREAMING_SNAKE_CASE , sep="""\t""" , header=_SCREAMING_SNAKE_CASE )
for answer_list in data[1]:
lowerCAmelCase = ast.literal_eval(_SCREAMING_SNAKE_CASE )
answers.append(_SCREAMING_SNAKE_CASE )
else:
lowerCAmelCase = [line.strip() for line in open(_SCREAMING_SNAKE_CASE , """r""" ).readlines()]
lowerCAmelCase = [[reference] for reference in references]
lowerCAmelCase = lowerCAmelCase = lowerCAmelCase = 0
for prediction, ground_truths in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
total += 1
em += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = 100.0 * em / total
lowerCAmelCase = 100.0 * fa / total
logger.info(f'F1: {fa:.2f}' )
logger.info(f'EM: {em:.2f}' )
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = args.k
lowerCAmelCase = [line.strip() for line in open(_SCREAMING_SNAKE_CASE , """r""" ).readlines()]
lowerCAmelCase = [line.strip() for line in open(_SCREAMING_SNAKE_CASE , """r""" ).readlines()]
lowerCAmelCase = lowerCAmelCase = 0
for hypo, reference in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase = set(hypo.split("""\t""" )[:k] )
lowerCAmelCase = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
lowerCAmelCase = 100.0 * em / total
logger.info(f'Precision@{k}: {em: .2f}' )
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
def strip_title(_SCREAMING_SNAKE_CASE : Union[str, Any] ):
if title.startswith("""\"""" ):
lowerCAmelCase = title[1:]
if title.endswith("""\"""" ):
lowerCAmelCase = title[:-1]
return title
lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_SCREAMING_SNAKE_CASE , return_tensors="""pt""" , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , )["""input_ids"""].to(args.device )
lowerCAmelCase = rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = question_enc_outputs[0]
lowerCAmelCase = rag_model.retriever(
_SCREAMING_SNAKE_CASE , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="""pt""" , )
lowerCAmelCase = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
lowerCAmelCase = []
for docs in all_docs:
lowerCAmelCase = [strip_title(_SCREAMING_SNAKE_CASE ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(_SCREAMING_SNAKE_CASE ) )
return provenance_strings
def _snake_case ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : str ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_SCREAMING_SNAKE_CASE , return_tensors="""pt""" , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE )
lowerCAmelCase = inputs_dict.input_ids.to(args.device )
lowerCAmelCase = inputs_dict.attention_mask.to(args.device )
lowerCAmelCase = rag_model.generate( # rag_model overwrites generate
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=_SCREAMING_SNAKE_CASE , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
lowerCAmelCase = rag_model.retriever.generator_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
if args.print_predictions:
for q, a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info("""Q: {} - A: {}""".format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
return answers
def _snake_case ( ) -> Dict:
"""simple docstring"""
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" , choices=["""rag_sequence""", """rag_token""", """bart"""] , type=_SCREAMING_SNAKE_CASE , help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) , )
parser.add_argument(
"""--index_name""" , default=_SCREAMING_SNAKE_CASE , choices=["""exact""", """compressed""", """legacy"""] , type=_SCREAMING_SNAKE_CASE , help="""RAG model retriever type""" , )
parser.add_argument(
"""--index_path""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help="""Path to the retrieval index""" , )
parser.add_argument("""--n_docs""" , default=5 , type=_SCREAMING_SNAKE_CASE , help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--eval_mode""" , choices=["""e2e""", """retrieval"""] , default="""e2e""" , type=_SCREAMING_SNAKE_CASE , help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) , )
parser.add_argument("""--k""" , default=1 , type=_SCREAMING_SNAKE_CASE , help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""Path to a file containing evaluation samples""" , )
parser.add_argument(
"""--gold_data_path""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""Path to a tab-separated file with gold samples""" , )
parser.add_argument(
"""--gold_data_mode""" , default="""qa""" , type=_SCREAMING_SNAKE_CASE , choices=["""qa""", """ans"""] , help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) , )
parser.add_argument(
"""--predictions_path""" , type=_SCREAMING_SNAKE_CASE , default="""predictions.txt""" , help="""Name of the predictions file, to be stored in the checkpoints directory""" , )
parser.add_argument(
"""--eval_all_checkpoints""" , action="""store_true""" , help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" , )
parser.add_argument(
"""--eval_batch_size""" , default=8 , type=_SCREAMING_SNAKE_CASE , help="""Batch size per GPU/CPU for evaluation.""" , )
parser.add_argument(
"""--recalculate""" , help="""Recalculate predictions even if the prediction file exists""" , action="""store_true""" , )
parser.add_argument(
"""--num_beams""" , default=4 , type=_SCREAMING_SNAKE_CASE , help="""Number of beams to be used when generating answers""" , )
parser.add_argument("""--min_length""" , default=1 , type=_SCREAMING_SNAKE_CASE , help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" , default=50 , type=_SCREAMING_SNAKE_CASE , help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" , action="""store_true""" , help="""If True, prints predictions while evaluating.""" , )
parser.add_argument(
"""--print_docs""" , action="""store_true""" , help="""If True, prints docs retried while generating.""" , )
lowerCAmelCase = parser.parse_args()
lowerCAmelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def _snake_case ( _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = {}
if args.model_type is None:
lowerCAmelCase = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
lowerCAmelCase = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
lowerCAmelCase = args.n_docs
if args.index_name is not None:
lowerCAmelCase = args.index_name
if args.index_path is not None:
lowerCAmelCase = args.index_path
else:
lowerCAmelCase = BartForConditionalGeneration
lowerCAmelCase = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
lowerCAmelCase = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(_SCREAMING_SNAKE_CASE , args.predictions_path , args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(_SCREAMING_SNAKE_CASE ) )
logger.info(""" Batch size = %d""" , args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
lowerCAmelCase = RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , retriever=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
model.retriever.init_retrieval()
else:
lowerCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
model.to(args.device )
with open(args.evaluation_set , """r""" ) as eval_file, open(args.predictions_path , """w""" ) as preds_file:
lowerCAmelCase = []
for line in tqdm(_SCREAMING_SNAKE_CASE ):
questions.append(line.strip() )
if len(_SCREAMING_SNAKE_CASE ) == args.eval_batch_size:
lowerCAmelCase = evaluate_batch_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
preds_file.write("""\n""".join(_SCREAMING_SNAKE_CASE ) + """\n""" )
preds_file.flush()
lowerCAmelCase = []
if len(_SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase = evaluate_batch_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
preds_file.write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
preds_file.flush()
score_fn(_SCREAMING_SNAKE_CASE , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
UpperCAmelCase = get_args()
main(args)
| 187
| 0
|
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def snake_case ( UpperCAmelCase )-> Dict: # picklable for multiprocessing
"""simple docstring"""
return x.sum()
def snake_case ( UpperCAmelCase )-> Any: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@dataclass
class UpperCamelCase__ :
UpperCAmelCase__ : List[Any] = 4_2
UpperCAmelCase__ : Dict = 4_2
class UpperCamelCase__ ( _lowercase):
def lowercase_ ( self :List[Any] ) -> List[Any]:
'''simple docstring'''
__A = {}
__A = []
__A = 1
__A = [1, 2]
__A = {'a': 1, 'b': 2}
__A = {'a': [1, 2], 'b': [3, 4]}
__A = {'a': {'1': 1}, 'b': 2}
__A = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
__A = {}
__A = []
__A = 2
__A = [2, 3]
__A = {'a': 2, 'b': 3}
__A = {'a': [2, 3], 'b': [4, 5]}
__A = {'a': {'1': 2}, 'b': 3}
__A = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
__A = 2
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
__A = {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
__A = {'a': 2, 'b': 0, 'c': 2}
__A = {
'a': np.eye(2 ).astype(__UpperCamelCase ),
'b': np.zeros(3 ).astype(__UpperCamelCase ),
'c': np.ones(2 ).astype(__UpperCamelCase ),
}
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase , num_proc=__UpperCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(__UpperCamelCase ): # can't pickle a local lambda
map_nested(lambda _A : x + 1 , __UpperCamelCase , num_proc=__UpperCamelCase )
def lowercase_ ( self :Optional[Any] ) -> List[str]:
'''simple docstring'''
__A = {'a': 1, 'b': 2}
__A = {'a': 3, 'b': 4}
__A = {'a': 5, 'b': 6}
__A = sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) ) , __UpperCamelCase )
def lowercase_ ( self :str ) -> Tuple:
'''simple docstring'''
class UpperCamelCase__ :
UpperCAmelCase__ : Tuple = 'bar'
__A = Foo()
self.assertEqual(foo.my_attr , 'bar' )
with temporary_assignment(__UpperCamelCase , 'my_attr' , 'BAR' ):
self.assertEqual(foo.my_attr , 'BAR' )
self.assertEqual(foo.my_attr , 'bar' )
@pytest.mark.parametrize(
'iterable_length, num_proc, expected_num_proc' , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(1_6, 1_6, 1_6),
(1_6, 1_7, 1_6),
(1_7, 1_6, 1_6),
] , )
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> List[str]:
"""simple docstring"""
with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
__A = {f'{i}': i for i in range(a__ )}
__A = map_nested(lambda UpperCAmelCase : x + 1_0 , a__ , num_proc=a__ , parallel_min_length=1_6 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class UpperCamelCase__ ( _lowercase):
@require_tf
def lowercase_ ( self :List[str] ) -> List[str]:
'''simple docstring'''
import tensorflow as tf
from tensorflow.keras import layers
__A = layers.Dense(2 )
def gen_random_output():
__A = tf.random.uniform((1, 3) )
return model(__UpperCamelCase ).numpy()
with temp_seed(42 , set_tensorflow=__UpperCamelCase ):
__A = gen_random_output()
with temp_seed(42 , set_tensorflow=__UpperCamelCase ):
__A = gen_random_output()
__A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def lowercase_ ( self :List[Any] ) -> Tuple:
'''simple docstring'''
import torch
def gen_random_output():
__A = torch.nn.Linear(3 , 2 )
__A = torch.rand(1 , 3 )
return model(__UpperCamelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=__UpperCamelCase ):
__A = gen_random_output()
with temp_seed(42 , set_pytorch=__UpperCamelCase ):
__A = gen_random_output()
__A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def lowercase_ ( self :Tuple ) -> List[str]:
'''simple docstring'''
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
__A = gen_random_output()
with temp_seed(42 ):
__A = gen_random_output()
__A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize('input_data' , [{}] )
def snake_case ( UpperCAmelCase )-> List[str]:
"""simple docstring"""
__A = NestedDataStructure(a__ ).data
assert output_data == input_data
@pytest.mark.parametrize(
'data, expected_output' , [
({}, []),
([], []),
('foo', ['foo']),
(['foo', 'bar'], ['foo', 'bar']),
([['foo', 'bar']], ['foo', 'bar']),
([[['foo'], ['bar']]], ['foo', 'bar']),
([[['foo'], 'bar']], ['foo', 'bar']),
({'a': 1, 'b': 2}, [1, 2]),
({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
({'a': {'1': 1}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
] , )
def snake_case ( UpperCAmelCase , UpperCAmelCase )-> Tuple:
"""simple docstring"""
__A = NestedDataStructure(a__ ).flatten()
assert output == expected_output
def snake_case ( )-> Optional[Any]:
"""simple docstring"""
__A = A(x=1 , y='foobar' )
__A = {'x': 1, 'y': 'foobar'}
assert asdict(a__ ) == expected_output
__A = {'a': {'b': A(x=1_0 , y='foo' )}, 'c': [A(x=2_0 , y='bar' )]}
__A = {'a': {'b': {'x': 1_0, 'y': 'foo'}}, 'c': [{'x': 2_0, 'y': 'bar'}]}
assert asdict(a__ ) == expected_output
with pytest.raises(a__ ):
asdict([1, A(x=1_0 , y='foo' )] )
def snake_case ( UpperCAmelCase )-> Optional[Any]:
"""simple docstring"""
return text.split()
def snake_case ( UpperCAmelCase )-> Any:
"""simple docstring"""
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def snake_case ( )-> Any:
"""simple docstring"""
with Pool(2 ) as pool:
__A = list(iflatmap_unordered(a__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 1_0 ) )
assert out.count('hello' ) == 1_0
assert out.count('there' ) == 1_0
assert len(a__ ) == 2_0
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
__A = list(iflatmap_unordered(a__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 1_0 ) )
assert out.count('hello' ) == 1_0
assert out.count('there' ) == 1_0
assert len(a__ ) == 2_0
# check that we get items as fast as possible
with Pool(2 ) as pool:
__A = []
for yield_time, content in iflatmap_unordered(
a__ , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(a__ )
assert out.count('a' ) == 2
assert out.count('b' ) == 2
assert len(a__ ) == 4
| 161
|
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ ( _lowercase):
def __init__( self : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str]=13 , __UpperCamelCase : str=7 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : List[str]=True , __UpperCamelCase : int=True , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : List[Any]=99 , __UpperCamelCase : Dict=32 , __UpperCamelCase : int=5 , __UpperCamelCase : str=4 , __UpperCamelCase : Any=37 , __UpperCamelCase : Tuple="gelu" , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : Any=0.1 , __UpperCamelCase : Any=512 , __UpperCamelCase : List[str]=16 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : List[str]=0.0_2 , __UpperCamelCase : str=False , __UpperCamelCase : Dict=True , __UpperCamelCase : Tuple="None" , __UpperCamelCase : Dict=3 , __UpperCamelCase : Dict=4 , __UpperCamelCase : Any=None , ) -> Tuple:
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = relative_attention
_UpperCamelCase = position_biased_input
_UpperCamelCase = pos_att_type
_UpperCamelCase = scope
def _UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _UpperCamelCase ( self : Optional[int] ) -> List[Any]:
_UpperCamelCase = self.get_config()
_UpperCamelCase = 300
return config
def _UpperCamelCase ( self : int , __UpperCamelCase : List[Any] ) -> str:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def _UpperCamelCase ( self : Any , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] ) -> List[str]:
_UpperCamelCase = DebertaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCamelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )[0]
_UpperCamelCase = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )[0]
_UpperCamelCase = model(__UpperCamelCase )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def _UpperCamelCase ( self : Tuple , __UpperCamelCase : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : str , __UpperCamelCase : Optional[int] ) -> Tuple:
_UpperCamelCase = DebertaForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCamelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCamelCase ( self : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : Tuple ) -> List[Any]:
_UpperCamelCase = self.num_labels
_UpperCamelCase = DebertaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCamelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__UpperCamelCase )
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : str ) -> Dict:
_UpperCamelCase = self.num_labels
_UpperCamelCase = DebertaForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCamelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int , __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : List[Any] ) -> List[Any]:
_UpperCamelCase = DebertaForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCamelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _UpperCamelCase ( self : Any ) -> Union[str, Any]:
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _lowercase , _lowercase , unittest.TestCase):
snake_case__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = True
snake_case__ = False
snake_case__ = False
snake_case__ = False
snake_case__ = False
def _UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
_UpperCamelCase = DebertaModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def _UpperCamelCase ( self : Optional[int] ) -> int:
self.config_tester.run_common_tests()
def _UpperCamelCase ( self : Any ) -> List[str]:
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__UpperCamelCase )
def _UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__UpperCamelCase )
def _UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__UpperCamelCase )
def _UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__UpperCamelCase )
def _UpperCamelCase ( self : Dict ) -> Tuple:
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__UpperCamelCase )
@slow
def _UpperCamelCase ( self : Any ) -> Optional[Any]:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = DebertaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase):
@unittest.skip(reason='''Model not available yet''' )
def _UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
pass
@slow
def _UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
_UpperCamelCase = DebertaModel.from_pretrained('''microsoft/deberta-base''' )
_UpperCamelCase = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
_UpperCamelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCamelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
# compare the actual values for a slice.
_UpperCamelCase = torch.tensor(
[[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __UpperCamelCase , atol=1E-4 ) , F'''{output[:, 1:4, 1:4]}''' )
| 256
| 0
|
"""simple docstring"""
import operator as op
a : Dict = """scaler.pt"""
a : int = """pytorch_model"""
a : Tuple = """random_states"""
a : Dict = """optimizer"""
a : List[str] = """scheduler"""
a : Optional[Any] = """pytorch_model.bin"""
a : Optional[int] = """pytorch_model.bin.index.json"""
a : Any = """model.safetensors"""
a : Tuple = """model.safetensors.index.json"""
a : Optional[int] = """1.10.2"""
a : Optional[int] = """py38"""
a : Union[str, Any] = """4.17.0"""
a : Any = ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
a : Dict = ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
a : Dict = ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
a : Tuple = ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
a : Union[str, Any] = ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
a : List[str] = """2.0.1"""
a : Dict = ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
a : Optional[int] = ["""default""", """reduce-overhead""", """max-autotune"""]
a : Union[str, Any] = {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
a : str = [
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
a : str = ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
a : Optional[Any] = ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 363
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = [
"""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
a : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 150
| 0
|
import requests
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : str ) -> None:
__snake_case = {'''Content-Type''': '''application/json'''}
__snake_case = requests.post(snake_case_ , json={'''text''': message_body} , headers=snake_case_ )
if response.status_code != 200:
__snake_case = (
'''Request to slack returned an error '''
f"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(snake_case_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 24
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=32 , _lowerCamelCase=2 , _lowerCamelCase=3 , _lowerCamelCase=16 , _lowerCamelCase=[1, 2, 1] , _lowerCamelCase=[2, 2, 4] , _lowerCamelCase=2 , _lowerCamelCase=2.0 , _lowerCamelCase=True , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.1 , _lowerCamelCase="gelu" , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=0.02 , _lowerCamelCase=1e-5 , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase=10 , _lowerCamelCase=8 , _lowerCamelCase=["stage1", "stage2", "stage3"] , _lowerCamelCase=[1, 2, 3] , ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : Optional[int] = image_size
UpperCAmelCase__ : Tuple = patch_size
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Dict = embed_dim
UpperCAmelCase__ : List[Any] = depths
UpperCAmelCase__ : Dict = num_heads
UpperCAmelCase__ : Any = window_size
UpperCAmelCase__ : str = mlp_ratio
UpperCAmelCase__ : str = qkv_bias
UpperCAmelCase__ : int = hidden_dropout_prob
UpperCAmelCase__ : Tuple = attention_probs_dropout_prob
UpperCAmelCase__ : Dict = drop_path_rate
UpperCAmelCase__ : Union[str, Any] = hidden_act
UpperCAmelCase__ : Union[str, Any] = use_absolute_embeddings
UpperCAmelCase__ : Optional[int] = patch_norm
UpperCAmelCase__ : Any = layer_norm_eps
UpperCAmelCase__ : Dict = initializer_range
UpperCAmelCase__ : Tuple = is_training
UpperCAmelCase__ : Union[str, Any] = scope
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Optional[int] = type_sequence_label_size
UpperCAmelCase__ : Tuple = encoder_stride
UpperCAmelCase__ : Optional[int] = out_features
UpperCAmelCase__ : str = out_indices
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Dict = None
if self.use_labels:
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Tuple = self.get_config()
return config, pixel_values, labels
def _a (self ):
"""simple docstring"""
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = MaskFormerSwinModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase__ : Dict = model(_lowerCamelCase )
UpperCAmelCase__ : Any = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
UpperCAmelCase__ : List[str] = 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 _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = MaskFormerSwinBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase__ : Tuple = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCamelCase ):
UpperCAmelCase__ : Union[str, Any] = ["""stem"""]
UpperCAmelCase__ : List[Any] = MaskFormerSwinBackbone(config=_lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : str = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = config_and_inputs
UpperCAmelCase__ : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = MaskFormerSwinModelTester(self )
UpperCAmelCase__ : Union[str, Any] = ConfigTester(self , config_class=_lowerCamelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a (self ):
"""simple docstring"""
return
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear ) )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(_lowerCamelCase )
UpperCAmelCase__ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Any = [*signature.parameters.keys()]
UpperCAmelCase__ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a (self ):
"""simple docstring"""
pass
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
UpperCAmelCase__ : str = outputs.hidden_states
UpperCAmelCase__ : str = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCamelCase ) , _lowerCamelCase )
# Swin has a different seq_length
UpperCAmelCase__ : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ : Tuple = (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] , )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : int = (
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:
UpperCAmelCase__ : int = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : Optional[Any] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Tuple = 3
UpperCAmelCase__ : Tuple = (
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)
)
UpperCAmelCase__ : Optional[int] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
UpperCAmelCase__ : List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : List[Any] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCamelCase ):
UpperCAmelCase__ : Optional[int] = 0
return t
def check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase={} ):
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase )
UpperCAmelCase__ : str = model(**_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase ).to_tuple()
def recursive_check(_lowerCamelCase , _lowerCamelCase ):
if isinstance(_lowerCamelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase , _lowerCamelCase ):
recursive_check(_lowerCamelCase , _lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCamelCase , _lowerCamelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCamelCase ) , set_nan_tensor_to_zero(_lowerCamelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
F""" {torch.isnan(_lowerCamelCase ).any()} and `inf`: {torch.isinf(_lowerCamelCase )}. Dict has"""
F""" `nan`: {torch.isnan(_lowerCamelCase ).any()} and `inf`: {torch.isinf(_lowerCamelCase )}."""
) , )
recursive_check(_lowerCamelCase , _lowerCamelCase )
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase__ : Any = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Dict = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Tuple = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
UpperCAmelCase__ : Any = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Union[str, Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Optional[int] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"""output_hidden_states""": True} )
UpperCAmelCase__ : str = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
UpperCAmelCase__ : List[str] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"""output_hidden_states""": True} )
@require_torch
class lowerCamelCase ( unittest.TestCase , lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (MaskFormerSwinBackbone,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = MaskFormerSwinConfig
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = MaskFormerSwinModelTester(self )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[int] = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
UpperCAmelCase__ : Tuple = backbone_class(_lowerCamelCase )
backbone.to(_lowerCamelCase )
backbone.eval()
UpperCAmelCase__ : int = backbone(**_lowerCamelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCamelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
UpperCAmelCase__ : List[str] = backbone(**_lowerCamelCase , output_hidden_states=_lowerCamelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
UpperCAmelCase__ : List[str] = backbone(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertIsNotNone(outputs.attentions )
| 171
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|
from manim import *
class __lowerCAmelCase ( UpperCamelCase__):
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
a__ : int =Rectangle(height=0.5 , width=0.5 )
a__ : str =Rectangle(height=0.25 , width=0.25 )
a__ : Optional[int] =Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
a__ : str =[mem.copy() for i in range(6 )]
a__ : int =[mem.copy() for i in range(6 )]
a__ : Any =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Optional[int] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Optional[Any] =VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : str =Text("CPU" , font_size=2_4 )
a__ : int =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCAmelCase__ )
a__ : List[str] =[mem.copy() for i in range(4 )]
a__ : List[Any] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : int =Text("GPU" , font_size=2_4 )
a__ : Optional[int] =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
gpu.move_to([-1, -1, 0] )
self.add(lowerCAmelCase__ )
a__ : Optional[int] =[mem.copy() for i in range(6 )]
a__ : Optional[int] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Optional[Any] =Text("Model" , font_size=2_4 )
a__ : Optional[Any] =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
model.move_to([3, -1.0, 0] )
self.add(lowerCAmelCase__ )
a__ : Any =[]
a__ : List[str] =[]
a__ : List[str] =[]
for i, rect in enumerate(lowerCAmelCase__ ):
rect.set_stroke(lowerCAmelCase__ )
a__ : Dict =Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowerCAmelCase__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=lowerCAmelCase__ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=lowerCAmelCase__ , buff=0.0 )
self.add(lowerCAmelCase__ )
model_cpu_arr.append(lowerCAmelCase__ )
self.add(*lowerCAmelCase__ , *lowerCAmelCase__ , *lowerCAmelCase__ )
a__ : Dict =[mem.copy() for i in range(6 )]
a__ : Dict =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Dict =Text("Loaded Checkpoint" , font_size=2_4 )
a__ : Dict =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(lowerCAmelCase__ )
a__ : Tuple =[]
a__ : Tuple =[]
for i, rect in enumerate(lowerCAmelCase__ ):
a__ : Optional[Any] =fill.copy().set_fill(lowerCAmelCase__ , opacity=0.7 )
target.move_to(lowerCAmelCase__ )
ckpt_arr.append(lowerCAmelCase__ )
a__ : Tuple =target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(lowerCAmelCase__ )
self.add(*lowerCAmelCase__ , *lowerCAmelCase__ )
a__ : str =Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
a__ : Dict =MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : List[str] =MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )
blue_text.next_to(lowerCAmelCase__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(lowerCAmelCase__ )
a__ : List[Any] =MarkupText(
F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
a__ : List[str] =[meta_mem.copy() for i in range(6 )]
a__ : Dict =[meta_mem.copy() for i in range(6 )]
a__ : Optional[int] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Any =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : List[str] =VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
a__ : Union[str, Any] =Text("Disk" , font_size=2_4 )
a__ : Union[str, Any] =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) , Write(lowerCAmelCase__ , run_time=1 ) , Create(lowerCAmelCase__ , run_time=1 ) )
a__ : Optional[Any] =[]
for i, rect in enumerate(lowerCAmelCase__ ):
a__ : Dict =rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(lowerCAmelCase__ , run_time=1.5 ) )
self.play(*lowerCAmelCase__ )
self.play(FadeOut(lowerCAmelCase__ ) )
a__ : Dict =MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=2_4 )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) )
self.play(
FadeOut(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , *lowerCAmelCase__ ) , )
self.wait()
| 148
|
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
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCAmelCase : Dict = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __lowerCAmelCase :
_lowercase : str = field(
default=UpperCamelCase__ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(UpperCamelCase__)})
_lowercase : str = field(
default=UpperCamelCase__ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""})
_lowercase : int = 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."""
)
} , )
_lowercase : int = field(
default=128 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
_lowercase : int = field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
_lowercase : int = 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."""
)
} , )
_lowercase : bool = field(
default=UpperCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
_lowercase : bool = field(
default=UpperCamelCase__ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""})
_lowercase : float = field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""})
_lowercase : int = field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""})
_lowercase : int = field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
_lowercase : int = field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""})
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : List[Any] = """train"""
_lowercase : Any = """dev"""
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = Split.train , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = "pt" , ) -> str:
'''simple docstring'''
a__ : List[Any] =args
a__ : int =is_language_sensitive
a__ : List[str] =SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
try:
a__ : str =Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
a__ : Any =mode
# Load data features from cache or dataset file
a__ : str ="v2" if args.version_2_with_negative else "v1"
a__ : str =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.
a__ : Dict =cached_features_file + ".lock"
with FileLock(lowerCAmelCase__ ):
if os.path.exists(lowerCAmelCase__ ) and not args.overwrite_cache:
a__ : Any =time.time()
a__ : List[Any] =torch.load(lowerCAmelCase__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
a__ : str =self.old_features["features"]
a__ : str =self.old_features.get("dataset" , lowerCAmelCase__ )
a__ : Optional[int] =self.old_features.get("examples" , lowerCAmelCase__ )
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:
a__ : Dict =self.processor.get_dev_examples(args.data_dir )
else:
a__ : str =self.processor.get_train_examples(args.data_dir )
a__ , a__ : Union[str, Any] =squad_convert_examples_to_features(
examples=self.examples , tokenizer=lowerCAmelCase__ , 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=lowerCAmelCase__ , )
a__ : Any =time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , lowerCAmelCase__ , )
# ^ 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 ) -> Tuple:
'''simple docstring'''
return len(self.features )
def __getitem__( self , lowerCAmelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
a__ : str =self.features[i]
a__ : Optional[Any] =torch.tensor(feature.input_ids , dtype=torch.long )
a__ : List[Any] =torch.tensor(feature.attention_mask , dtype=torch.long )
a__ : int =torch.tensor(feature.token_type_ids , dtype=torch.long )
a__ : List[str] =torch.tensor(feature.cls_index , dtype=torch.long )
a__ : int =torch.tensor(feature.p_mask , dtype=torch.float )
a__ : Tuple =torch.tensor(feature.is_impossible , dtype=torch.float )
a__ : Tuple ={
"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:
a__ : int =torch.tensor(feature.start_position , dtype=torch.long )
a__ : Any =torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 148
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
def UpperCamelCase_( lowerCamelCase_ ) -> List[List[ImageInput]]:
if isinstance(lowerCamelCase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCamelCase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCamelCase_ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class _lowerCamelCase( _a ):
lowercase_ : Union[str, Any] = ["""pixel_values"""]
def __init__( self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, ) -> None:
"""simple docstring"""
super().__init__(**lowerCamelCase)
_lowercase : str = size if size is not None else {'shortest_edge': 2_56}
_lowercase : Any = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
_lowercase : List[str] = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
_lowercase : Optional[Any] = get_size_dict(lowerCamelCase, param_name='crop_size')
_lowercase : Optional[int] = do_resize
_lowercase : Tuple = size
_lowercase : Any = do_center_crop
_lowercase : Any = crop_size
_lowercase : int = resample
_lowercase : int = do_rescale
_lowercase : str = rescale_factor
_lowercase : Tuple = offset
_lowercase : List[str] = do_normalize
_lowercase : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
_lowercase : Union[str, Any] = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
if "shortest_edge" in size:
_lowercase : Dict = get_resize_output_image_size(lowerCamelCase, size['shortest_edge'], default_to_square=lowerCamelCase)
elif "height" in size and "width" in size:
_lowercase : List[Any] = (size['height'], size['width'])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
_lowercase : Dict = get_size_dict(lowerCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
return center_crop(lowerCamelCase, size=(size['height'], size['width']), data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = True, lowerCamelCase = None, **lowerCamelCase, ) -> Dict:
"""simple docstring"""
_lowercase : str = image.astype(np.floataa)
if offset:
_lowercase : List[str] = image - (scale / 2)
return rescale(lowerCamelCase, scale=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
return normalize(lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, ) -> np.ndarray:
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.')
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.')
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.')
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.')
if offset and not do_rescale:
raise ValueError('For offset, do_rescale must also be set to True.')
# All transformations expect numpy arrays.
_lowercase : Tuple = to_numpy_array(lowerCamelCase)
if do_resize:
_lowercase : Optional[Any] = self.resize(image=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase)
if do_center_crop:
_lowercase : Dict = self.center_crop(lowerCamelCase, size=lowerCamelCase)
if do_rescale:
_lowercase : Dict = self.rescale(image=lowerCamelCase, scale=lowerCamelCase, offset=lowerCamelCase)
if do_normalize:
_lowercase : Optional[Any] = self.normalize(image=lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase)
_lowercase : Tuple = to_channel_dimension_format(lowerCamelCase, lowerCamelCase)
return image
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, ) -> PIL.Image.Image:
"""simple docstring"""
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : Optional[int] = resample if resample is not None else self.resample
_lowercase : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowercase : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : Optional[int] = offset if offset is not None else self.offset
_lowercase : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Any = image_mean if image_mean is not None else self.image_mean
_lowercase : Union[str, Any] = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = size if size is not None else self.size
_lowercase : Any = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
_lowercase : List[str] = crop_size if crop_size is not None else self.crop_size
_lowercase : Any = get_size_dict(lowerCamelCase, param_name='crop_size')
if not valid_images(lowerCamelCase):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.')
_lowercase : List[str] = make_batched(lowerCamelCase)
_lowercase : Any = [
[
self._preprocess_image(
image=lowerCamelCase, do_resize=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, do_center_crop=lowerCamelCase, crop_size=lowerCamelCase, do_rescale=lowerCamelCase, rescale_factor=lowerCamelCase, offset=lowerCamelCase, do_normalize=lowerCamelCase, image_mean=lowerCamelCase, image_std=lowerCamelCase, data_format=lowerCamelCase, )
for img in video
]
for video in videos
]
_lowercase : Dict = {'pixel_values': videos}
return BatchFeature(data=lowerCamelCase, tensor_type=lowerCamelCase)
| 21
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class A_ (a_ ):
UpperCAmelCase__ = '''big_bird'''
def __init__( self , _A=5_0_3_5_8 , _A=7_6_8 , _A=1_2 , _A=1_2 , _A=3_0_7_2 , _A="gelu_new" , _A=0.1 , _A=0.1 , _A=4_0_9_6 , _A=2 , _A=0.02 , _A=1E-12 , _A=True , _A=0 , _A=1 , _A=2 , _A=6_6 , _A="block_sparse" , _A=True , _A=False , _A=6_4 , _A=3 , _A=None , **_A , ):
'''simple docstring'''
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , sep_token_id=_A , **_A , )
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
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 = type_vocab_size
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = use_cache
UpperCAmelCase = rescale_embeddings
UpperCAmelCase = attention_type
UpperCAmelCase = use_bias
UpperCAmelCase = block_size
UpperCAmelCase = num_random_blocks
UpperCAmelCase = classifier_dropout
class A_ (a_ ):
@property
def _lowercase ( self ):
'''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),
] )
| 273
| 0
|
"""simple docstring"""
__lowercase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def lowerCAmelCase (__UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int ):
"""simple docstring"""
assert len(str(__UpperCamelCase ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 1_2, "month should be between 1 to 12"
assert 1 <= day <= 3_1, "day should be between 1 to 31"
# Doomsday algorithm:
__UpperCamelCase =year // 1_0_0
__UpperCamelCase =(5 * (century % 4) + 2) % 7
__UpperCamelCase =year % 1_0_0
__UpperCamelCase =centurian % 1_2
__UpperCamelCase =(
(centurian // 1_2) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__UpperCamelCase =(
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_0_0) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__UpperCamelCase =(dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = '''albert'''
def __init__( self : List[Any] , UpperCamelCase__ : List[Any]=30000 , UpperCamelCase__ : int=128 , UpperCamelCase__ : str=4096 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : Union[str, Any]=64 , UpperCamelCase__ : Any=16384 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Optional[int]="gelu_new" , UpperCamelCase__ : int=0 , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : Dict=512 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : str=0.02 , UpperCamelCase__ : Tuple=1E-12 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Dict="absolute" , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : int=2 , UpperCamelCase__ : Optional[Any]=3 , **UpperCamelCase__ : List[str] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
__UpperCamelCase =vocab_size
__UpperCamelCase =embedding_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_hidden_groups
__UpperCamelCase =num_attention_heads
__UpperCamelCase =inner_group_num
__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 =classifier_dropout_prob
__UpperCamelCase =position_embedding_type
class _lowercase ( __a ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : Union[str, 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),
('''token_type_ids''', dynamic_axis),
] )
| 85
| 1
|
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowerCamelCase : int ="bert-base-cased"
lowerCamelCase : Union[str, Any] ="google/pegasus-xsum"
lowerCamelCase : Any =[" Sam ate lunch today.", "Sams lunch ingredients."]
lowerCamelCase : Dict =["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowerCamelCase : List[str] ="patrickvonplaten/t5-tiny-random"
lowerCamelCase : str ="sshleifer/bart-tiny-random"
lowerCamelCase : Union[str, Any] ="sshleifer/tiny-mbart"
lowerCamelCase : Optional[int] ="sshleifer/tiny-marian-en-de"
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
UpperCamelCase__ : Optional[Any] = "\n".join(_A )
Path(_A ).open("w" ).writelines(_A )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Any:
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(_A , f'{split}.source' ) , _A )
_dump_articles(os.path.join(_A , f'{split}.target' ) , _A )
return tmp_dir
class __a ( UpperCAmelCase__ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Any = AutoTokenizer.from_pretrained(__lowercase )
UpperCamelCase__ : List[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCamelCase__ : int = max(len(tokenizer.encode(__lowercase ) ) for a in ARTICLES )
UpperCamelCase__ : Union[str, Any] = max(len(tokenizer.encode(__lowercase ) ) for a in SUMMARIES )
UpperCamelCase__ : Any = 4
UpperCamelCase__ : Optional[int] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
UpperCamelCase__ , UpperCamelCase__ : Optional[Any] = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
UpperCamelCase__ : Optional[int] = SeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path="train" , max_source_length=__lowercase , max_target_length=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , )
UpperCamelCase__ : str = DataLoader(__lowercase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(__lowercase , __lowercase )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
UpperCamelCase__ : Optional[int] = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = AutoTokenizer.from_pretrained(__lowercase )
UpperCamelCase__ : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCamelCase__ : List[str] = max(len(tokenizer.encode(__lowercase ) ) for a in ARTICLES )
UpperCamelCase__ : Tuple = max(len(tokenizer.encode(__lowercase ) ) for a in SUMMARIES )
UpperCamelCase__ : Optional[int] = 4
UpperCamelCase__ : str = LegacySeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path="train" , max_source_length=20 , max_target_length=__lowercase , )
UpperCamelCase__ : Dict = DataLoader(__lowercase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : str = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
UpperCamelCase__ : Union[str, Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
UpperCamelCase__ : List[Any] = tmp_dir.joinpath("train.source" ).open().readlines()
UpperCamelCase__ : Union[str, Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(__lowercase , __lowercase , 1_28 , __lowercase )
UpperCamelCase__ : Any = {x.name for x in tmp_dir.iterdir()}
UpperCamelCase__ : Tuple = {x.name for x in save_dir.iterdir()}
UpperCamelCase__ : Optional[Any] = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(__lowercase ) < len(__lowercase )
assert len(__lowercase ) == 1
assert len(packed_examples[0] ) == sum(len(__lowercase ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = self._get_dataset(max_len=64 )
UpperCamelCase__ : str = 64
UpperCamelCase__ : Optional[int] = ds.make_dynamic_sampler(__lowercase , required_batch_size_multiple=__lowercase )
UpperCamelCase__ : Dict = [len(__lowercase ) for x in batch_sampler]
assert len(set(__lowercase ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(__lowercase ) == len(__lowercase ) # no dropped or added examples
UpperCamelCase__ : Optional[Any] = DataLoader(__lowercase , batch_sampler=__lowercase , collate_fn=ds.collate_fn , num_workers=2 )
UpperCamelCase__ : Optional[int] = []
UpperCamelCase__ : List[str] = []
for batch in data_loader:
UpperCamelCase__ : Any = batch["input_ids"].shape
UpperCamelCase__ : Dict = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
UpperCamelCase__ : str = np.product(batch["input_ids"].shape )
num_src_per_batch.append(__lowercase )
if num_src_tokens > (max_tokens * 1.1):
failures.append(__lowercase )
assert num_src_per_batch[0] == max(__lowercase )
if failures:
raise AssertionError(F'too many tokens in {len(__lowercase )} batches' )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = self._get_dataset(max_len=5_12 )
UpperCamelCase__ : Optional[Any] = 2
UpperCamelCase__ : Tuple = ds.make_sortish_sampler(__lowercase , shuffle=__lowercase )
UpperCamelCase__ : Optional[Any] = DataLoader(__lowercase , batch_size=__lowercase , collate_fn=ds.collate_fn , num_workers=2 )
UpperCamelCase__ : Union[str, Any] = DataLoader(__lowercase , batch_size=__lowercase , collate_fn=ds.collate_fn , num_workers=2 , sampler=__lowercase )
UpperCamelCase__ : List[str] = tokenizer.pad_token_id
def count_pad_tokens(SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str]="input_ids" ):
return [batch[k].eq(__lowercase ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(__lowercase , k="labels" ) ) < sum(count_pad_tokens(__lowercase , k="labels" ) )
assert sum(count_pad_tokens(__lowercase ) ) < sum(count_pad_tokens(__lowercase ) )
assert len(__lowercase ) == len(__lowercase )
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : Tuple=10_00 , SCREAMING_SNAKE_CASE : Dict=1_28 ):
'''simple docstring'''
if os.getenv("USE_REAL_DATA" , __lowercase ):
UpperCamelCase__ : str = "examples/seq2seq/wmt_en_ro"
UpperCamelCase__ : List[Any] = max_len * 2 * 64
if not Path(__lowercase ).joinpath("train.len" ).exists():
save_len_file(__lowercase , __lowercase )
else:
UpperCamelCase__ : Optional[Any] = "examples/seq2seq/test_data/wmt_en_ro"
UpperCamelCase__ : Any = max_len * 4
save_len_file(__lowercase , __lowercase )
UpperCamelCase__ : Dict = AutoTokenizer.from_pretrained(__lowercase )
UpperCamelCase__ : Any = SeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path="train" , max_source_length=__lowercase , max_target_length=__lowercase , n_obs=__lowercase , )
return ds, max_tokens, tokenizer
def __lowercase ( self : int ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Any = self._get_dataset()
UpperCamelCase__ : List[Any] = set(DistributedSortishSampler(__lowercase , 2_56 , num_replicas=2 , rank=0 , add_extra_examples=__lowercase ) )
UpperCamelCase__ : Any = set(DistributedSortishSampler(__lowercase , 2_56 , num_replicas=2 , rank=1 , add_extra_examples=__lowercase ) )
assert idsa.intersection(__lowercase ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(__lowercase , use_fast=__lowercase )
if tok_name == MBART_TINY:
UpperCamelCase__ : Any = SeqaSeqDataset(
__lowercase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
UpperCamelCase__ : str = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
UpperCamelCase__ : List[str] = SeqaSeqDataset(
__lowercase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
UpperCamelCase__ : List[str] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(__lowercase ) == 1 if tok_name == BART_TINY else len(__lowercase ) == 0
| 189
|
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
lowercase__ : Dict = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , **__lowercase : Union[str, Any] ):
"""simple docstring"""
super().__init__(**__lowercase )
requires_backends(self , "vision" )
requires_backends(self , "torch" )
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
self.check_model_type(__lowercase )
def snake_case__ ( self : Optional[int] , **__lowercase : Optional[Any] ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = {}
# preprocess args
if "points_per_batch" in kwargs:
snake_case_ = kwargs["points_per_batch"]
if "points_per_crop" in kwargs:
snake_case_ = kwargs["points_per_crop"]
if "crops_n_layers" in kwargs:
snake_case_ = kwargs["crops_n_layers"]
if "crop_overlap_ratio" in kwargs:
snake_case_ = kwargs["crop_overlap_ratio"]
if "crop_n_points_downscale_factor" in kwargs:
snake_case_ = kwargs["crop_n_points_downscale_factor"]
# postprocess args
if "pred_iou_thresh" in kwargs:
snake_case_ = kwargs["pred_iou_thresh"]
if "stability_score_offset" in kwargs:
snake_case_ = kwargs["stability_score_offset"]
if "mask_threshold" in kwargs:
snake_case_ = kwargs["mask_threshold"]
if "stability_score_thresh" in kwargs:
snake_case_ = kwargs["stability_score_thresh"]
if "crops_nms_thresh" in kwargs:
snake_case_ = kwargs["crops_nms_thresh"]
if "output_rle_mask" in kwargs:
snake_case_ = kwargs["output_rle_mask"]
if "output_bboxes_mask" in kwargs:
snake_case_ = kwargs["output_bboxes_mask"]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : Optional[int] , __lowercase : List[str] , *__lowercase : Optional[Any] , __lowercase : Dict=None , __lowercase : List[str]=None , **__lowercase : Optional[Any] ):
"""simple docstring"""
return super().__call__(__lowercase , *__lowercase , num_workers=__lowercase , batch_size=__lowercase , **__lowercase )
def snake_case__ ( self : str , __lowercase : int , __lowercase : List[str]=64 , __lowercase : int = 0 , __lowercase : float = 5_12 / 15_00 , __lowercase : Optional[int] = 32 , __lowercase : Optional[int] = 1 , ):
"""simple docstring"""
snake_case_ = load_image(__lowercase )
snake_case_ = self.image_processor.size["longest_edge"]
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.image_processor.generate_crop_boxes(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
snake_case_ = self.image_processor(images=__lowercase , return_tensors="pt" )
with self.device_placement():
if self.framework == "pt":
snake_case_ = self.get_inference_context()
with inference_context():
snake_case_ = self._ensure_tensor_on_device(__lowercase , device=self.device )
snake_case_ = self.model.get_image_embeddings(model_inputs.pop("pixel_values" ) )
snake_case_ = image_embeddings
snake_case_ = grid_points.shape[1]
snake_case_ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
"Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
"To return all points at once, set points_per_batch to None" )
for i in range(0 , __lowercase , __lowercase ):
snake_case_ = grid_points[:, i : i + points_per_batch, :, :]
snake_case_ = input_labels[:, i : i + points_per_batch]
snake_case_ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def snake_case__ ( self : Tuple , __lowercase : Union[str, Any] , __lowercase : Union[str, Any]=0.88 , __lowercase : Union[str, Any]=0.95 , __lowercase : int=0 , __lowercase : int=1 , ):
"""simple docstring"""
snake_case_ = model_inputs.pop("input_boxes" )
snake_case_ = model_inputs.pop("is_last" )
snake_case_ = model_inputs.pop("original_sizes" ).tolist()
snake_case_ = model_inputs.pop("reshaped_input_sizes" ).tolist()
snake_case_ = self.model(**__lowercase )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
snake_case_ = model_outputs["pred_masks"]
snake_case_ = self.image_processor.post_process_masks(
__lowercase , __lowercase , __lowercase , __lowercase , binarize=__lowercase )
snake_case_ = model_outputs["iou_scores"]
snake_case_ , snake_case_ , snake_case_ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __lowercase , __lowercase , __lowercase , __lowercase , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def snake_case__ ( self : str , __lowercase : Any , __lowercase : Optional[int]=False , __lowercase : int=False , __lowercase : List[str]=0.7 , ):
"""simple docstring"""
snake_case_ = []
snake_case_ = []
snake_case_ = []
for model_output in model_outputs:
all_scores.append(model_output.pop("iou_scores" ) )
all_masks.extend(model_output.pop("masks" ) )
all_boxes.append(model_output.pop("boxes" ) )
snake_case_ = torch.cat(__lowercase )
snake_case_ = torch.cat(__lowercase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.image_processor.post_process_for_mask_generation(
__lowercase , __lowercase , __lowercase , __lowercase )
snake_case_ = defaultdict(__lowercase )
for output in model_outputs:
for k, v in output.items():
extra[k].append(__lowercase )
snake_case_ = {}
if output_rle_mask:
snake_case_ = rle_mask
if output_bboxes_mask:
snake_case_ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 187
| 0
|
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
A = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=_a , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=_a , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=_a )
return parser.parse_args()
def _A ( ):
"""simple docstring"""
A = parse_args()
# Import training_script as a module.
A = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
A = script_fpath.stem
A = importlib.import_module(_a )
# Patch sys.argv
A = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 77
|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
UpperCAmelCase =parser.parse_args()
UpperCAmelCase =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCAmelCase =CLIPImageProcessor()
UpperCAmelCase =CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
UpperCAmelCase =UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 77
| 1
|
from __future__ import annotations
def _a ( a :list[int] , a :list[int] , a :int ) -> tuple[float, list[float]]:
a = list(range(len(_UpperCamelCase ) ) )
a = [v / w for v, w in zip(_UpperCamelCase , _UpperCamelCase )]
index.sort(key=lambda a : ratio[i] , reverse=_UpperCamelCase )
a = 0
a = [0] * len(_UpperCamelCase )
for i in index:
if weight[i] <= capacity:
a = 1
max_value += value[i]
capacity -= weight[i]
else:
a = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0
|
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
SCREAMING_SNAKE_CASE__ = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
SCREAMING_SNAKE_CASE__ = {"facebook/blenderbot-3B": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase__ ( ) -> str:
"""simple docstring"""
snake_case = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
snake_case = bs[:]
snake_case = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_UpperCamelCase )
cs.append(2**8 + n )
n += 1
snake_case = [chr(_UpperCamelCase ) for n in cs]
return dict(zip(_UpperCamelCase , _UpperCamelCase ) )
def lowerCAmelCase__ ( _UpperCamelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
snake_case = set()
snake_case = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case = char
return pairs
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : str = VOCAB_FILES_NAMES
_lowerCAmelCase : str = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Optional[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase="replace" , lowerCAmelCase="<s>" , lowerCAmelCase="</s>" , lowerCAmelCase="</s>" , lowerCAmelCase="<s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<pad>" , lowerCAmelCase="<mask>" , lowerCAmelCase=False , **lowerCAmelCase , ):
"""simple docstring"""
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else bos_token
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else eos_token
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else sep_token
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else cls_token
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else unk_token
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else mask_token
super().__init__(
errors=lowerCAmelCase , bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , sep_token=lowerCAmelCase , cls_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token=lowerCAmelCase , add_prefix_space=lowerCAmelCase , **lowerCAmelCase , )
with open(lowerCAmelCase , encoding='utf-8' ) as vocab_handle:
snake_case = json.load(lowerCAmelCase )
snake_case = {v: k for k, v in self.encoder.items()}
snake_case = errors # how to handle errors in decoding
snake_case = bytes_to_unicode()
snake_case = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase , encoding='utf-8' ) as merges_handle:
snake_case = merges_handle.read().split('\n' )[1:-1]
snake_case = [tuple(merge.split() ) for merge in bpe_merges]
snake_case = dict(zip(lowerCAmelCase , range(len(lowerCAmelCase ) ) ) )
snake_case = {}
snake_case = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
snake_case = 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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case ( self ):
"""simple docstring"""
return len(self.encoder )
def snake_case ( self ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
snake_case = tuple(lowerCAmelCase )
snake_case = get_pairs(lowerCAmelCase )
if not pairs:
return token
while True:
snake_case = min(lowerCAmelCase , key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
snake_case ,snake_case = bigram
snake_case = []
snake_case = 0
while i < len(lowerCAmelCase ):
try:
snake_case = word.index(lowerCAmelCase , lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case = j
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case = tuple(lowerCAmelCase )
snake_case = new_word
if len(lowerCAmelCase ) == 1:
break
else:
snake_case = get_pairs(lowerCAmelCase )
snake_case = ' '.join(lowerCAmelCase )
snake_case = word
return word
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = []
for token in re.findall(self.pat , lowerCAmelCase ):
snake_case = ''.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(lowerCAmelCase ).split(' ' ) )
return bpe_tokens
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
return self.encoder.get(lowerCAmelCase , self.encoder.get(self.unk_token ) )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = ''.join(lowerCAmelCase )
snake_case = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case = os.path.join(
lowerCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
snake_case = os.path.join(
lowerCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCAmelCase , ensure_ascii=lowerCAmelCase ) + '\n' )
snake_case = 0
with open(lowerCAmelCase , '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 lowerCAmelCase : 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!' )
snake_case = token_index
writer.write(' '.join(lowerCAmelCase ) + '\n' )
index += 1
return vocab_file, merge_file
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase , token_ids_a=lowerCAmelCase , already_has_special_tokens=lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase )) + [1]
return [1] + ([0] * len(lowerCAmelCase )) + [1, 1] + ([0] * len(lowerCAmelCase )) + [1]
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None ):
"""simple docstring"""
snake_case = [self.sep_token_id]
snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case ( self , lowerCAmelCase , lowerCAmelCase=False , **lowerCAmelCase ):
"""simple docstring"""
snake_case = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase ) > 0 and not text[0].isspace()):
snake_case = ' ' + text
return (text, kwargs)
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None ):
"""simple docstring"""
return token_ids_a + [self.eos_token_id]
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(' ' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCAmelCase )
snake_case = ' '.join(lowerCAmelCase )
snake_case = self.encode(lowerCAmelCase )
if len(lowerCAmelCase ) > self.model_max_length:
snake_case = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 150
| 0
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __a ( __UpperCamelCase ,__UpperCamelCase ,unittest.TestCase ):
__snake_case : List[str] = StableDiffusionDiffEditPipeline
__snake_case : Dict = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
__snake_case : Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
__snake_case : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__snake_case : Tuple = frozenset([] )
def A ( self : str ):
torch.manual_seed(0 )
lowerCAmelCase_ : List[Any] = 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 , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase , )
lowerCAmelCase_ : Tuple = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , )
lowerCAmelCase_ : List[Any] = DDIMInverseScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=UpperCAmelCase , set_alpha_to_zero=UpperCAmelCase , )
torch.manual_seed(0 )
lowerCAmelCase_ : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
lowerCAmelCase_ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="""gelu""" , projection_dim=5_12 , )
lowerCAmelCase_ : int = CLIPTextModel(UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ : Dict = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def A ( self : Any , UpperCAmelCase : int , UpperCAmelCase : List[str]=0 ):
lowerCAmelCase_ : Optional[Any] = floats_tensor((1, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
lowerCAmelCase_ : Union[str, Any] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
if str(UpperCAmelCase ).startswith("""mps""" ):
lowerCAmelCase_ : List[Any] = torch.manual_seed(UpperCAmelCase )
else:
lowerCAmelCase_ : int = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def A ( self : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int]=0 ):
lowerCAmelCase_ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
lowerCAmelCase_ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ : str = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert("""RGB""" )
if str(UpperCAmelCase ).startswith("""mps""" ):
lowerCAmelCase_ : int = torch.manual_seed(UpperCAmelCase )
else:
lowerCAmelCase_ : Any = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
lowerCAmelCase_ : List[Any] = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def A ( self : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[Any]=0 ):
lowerCAmelCase_ : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
lowerCAmelCase_ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ : Tuple = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert("""RGB""" )
if str(UpperCAmelCase ).startswith("""mps""" ):
lowerCAmelCase_ : Optional[Any] = torch.manual_seed(UpperCAmelCase )
else:
lowerCAmelCase_ : str = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
lowerCAmelCase_ : Tuple = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def A ( self : Any ):
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Optional[Any] = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowerCAmelCase_ : str = self.get_dummy_inputs(UpperCAmelCase )
lowerCAmelCase_ : Any = pipe(**UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCAmelCase )
lowerCAmelCase_ : List[str] = self.pipeline_class.from_pretrained(UpperCAmelCase )
pipe_loaded.to(UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCAmelCase , UpperCAmelCase ) is None , F'`{optional_component}` did not stay set to None after loading.' , )
lowerCAmelCase_ : Any = self.get_dummy_inputs(UpperCAmelCase )
lowerCAmelCase_ : Tuple = pipe_loaded(**UpperCAmelCase )[0]
lowerCAmelCase_ : Any = np.abs(output - output_loaded ).max()
self.assertLess(UpperCAmelCase , 1e-4 )
def A ( self : Any ):
lowerCAmelCase_ : Optional[Any] = """cpu"""
lowerCAmelCase_ : Optional[Any] = self.get_dummy_components()
lowerCAmelCase_ : Tuple = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowerCAmelCase_ : Tuple = self.get_dummy_mask_inputs(UpperCAmelCase )
lowerCAmelCase_ : List[str] = pipe.generate_mask(**UpperCAmelCase )
lowerCAmelCase_ : Dict = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowerCAmelCase_ : Any = np.array([0] * 9 )
lowerCAmelCase_ : Dict = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def A ( self : int ):
lowerCAmelCase_ : Optional[Any] = """cpu"""
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : int = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowerCAmelCase_ : Dict = self.get_dummy_inversion_inputs(UpperCAmelCase )
lowerCAmelCase_ : Tuple = pipe.invert(**UpperCAmelCase ).images
lowerCAmelCase_ : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowerCAmelCase_ : List[Any] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
lowerCAmelCase_ : List[str] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
def A ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def A ( self : Tuple ):
lowerCAmelCase_ : str = """cpu"""
lowerCAmelCase_ : int = self.get_dummy_components()
lowerCAmelCase_ : Optional[Any] = {"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowerCAmelCase_ : Dict = DPMSolverMultistepScheduler(**UpperCAmelCase )
lowerCAmelCase_ : Any = DPMSolverMultistepInverseScheduler(**UpperCAmelCase )
lowerCAmelCase_ : List[Any] = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowerCAmelCase_ : Tuple = self.get_dummy_inversion_inputs(UpperCAmelCase )
lowerCAmelCase_ : int = pipe.invert(**UpperCAmelCase ).images
lowerCAmelCase_ : str = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowerCAmelCase_ : List[str] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
lowerCAmelCase_ : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
@require_torch_gpu
@slow
class __a ( unittest.TestCase ):
def A ( self : int ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def A ( cls : Optional[Any] ):
lowerCAmelCase_ : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowerCAmelCase_ : Any = raw_image.convert("""RGB""" ).resize((7_68, 7_68) )
lowerCAmelCase_ : Union[str, Any] = raw_image
def A ( self : int ):
lowerCAmelCase_ : Any = torch.manual_seed(0 )
lowerCAmelCase_ : List[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
lowerCAmelCase_ : Union[str, Any] = DDIMScheduler.from_config(pipe.scheduler.config )
lowerCAmelCase_ : Tuple = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowerCAmelCase_ : Tuple = """a bowl of fruit"""
lowerCAmelCase_ : Any = """a bowl of pears"""
lowerCAmelCase_ : Dict = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
lowerCAmelCase_ : int = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase ).latents
lowerCAmelCase_ : str = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowerCAmelCase_ : Optional[Any] = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5e-1
def A ( self : Union[str, Any] ):
lowerCAmelCase_ : Dict = torch.manual_seed(0 )
lowerCAmelCase_ : List[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
lowerCAmelCase_ : Dict = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowerCAmelCase_ : int = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowerCAmelCase_ : int = """a bowl of fruit"""
lowerCAmelCase_ : Dict = """a bowl of pears"""
lowerCAmelCase_ : Dict = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
lowerCAmelCase_ : List[Any] = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase , num_inference_steps=25 , ).latents
lowerCAmelCase_ : Optional[Any] = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowerCAmelCase_ : Optional[int] = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 359
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase ( lowercase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = """huggingface/label-files"""
lowerCAmelCase_ : int = """imagenet-1k-id2label.json"""
lowerCAmelCase_ : List[str] = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase_ : Tuple = {int(lowercase__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ : Optional[Any] = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
lowerCAmelCase_ : Tuple = BitConfig(
conv_layer=lowercase__ , num_labels=1000 , idalabel=lowercase__ , labelaid=lowercase__ , )
return config
def __UpperCamelCase ( lowercase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
if "stem.conv" in name:
lowerCAmelCase_ : str = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCAmelCase_ : Tuple = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
lowerCAmelCase_ : Dict = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
lowerCAmelCase_ : List[str] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
lowerCAmelCase_ : Any = """bit.encoder.""" + name
return name
def __UpperCamelCase ( ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ : List[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return im
@torch.no_grad()
def __UpperCamelCase ( lowercase__ : List[str] , lowercase__ : Any , lowercase__ : Any=False ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = get_config(lowercase__ )
# load original model from timm
lowerCAmelCase_ : str = create_model(lowercase__ , pretrained=lowercase__ )
timm_model.eval()
# load state_dict of original model
lowerCAmelCase_ : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
lowerCAmelCase_ : List[str] = state_dict.pop(lowercase__ )
lowerCAmelCase_ : Dict = val.squeeze() if """head""" in key else val
# load HuggingFace model
lowerCAmelCase_ : Tuple = BitForImageClassification(lowercase__ )
model.eval()
model.load_state_dict(lowercase__ )
# create image processor
lowerCAmelCase_ : Tuple = create_transform(**resolve_data_config({} , model=lowercase__ ) )
lowerCAmelCase_ : Union[str, Any] = transform.transforms
lowerCAmelCase_ : str = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
lowerCAmelCase_ : List[str] = BitImageProcessor(
do_resize=lowercase__ , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowercase__ , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=lowercase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowerCAmelCase_ : int = prepare_img()
lowerCAmelCase_ : Tuple = transform(lowercase__ ).unsqueeze(0 )
lowerCAmelCase_ : List[str] = processor(lowercase__ , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(lowercase__ , lowercase__ )
# verify logits
with torch.no_grad():
lowerCAmelCase_ : Tuple = model(lowercase__ )
lowerCAmelCase_ : List[str] = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
lowerCAmelCase_ : Optional[Any] = timm_model(lowercase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowercase__ , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if push_to_hub:
print(f'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(f'ybelkada/{model_name}' )
processor.push_to_hub(f'ybelkada/{model_name}' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT 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.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 28
| 0
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def UpperCamelCase__ ( lowercase__ : Any ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase__ )
def UpperCamelCase__ ( lowercase__ : Optional[int] ):
from transformers.testing_utils import pytest_terminal_summary_main
snake_case : Any = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(lowercase__ , id=lowercase__ )
| 148
|
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
__A = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__A = spec.loader.load_module()
__A = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
__A = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def UpperCamelCase__ ( ):
snake_case : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
snake_case : Tuple = False
# source code of `config_class`
snake_case : Tuple = inspect.getsource(lowercase__ )
snake_case : Optional[int] = _re_checkpoint.findall(lowercase__ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
snake_case , snake_case : str = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
snake_case : Optional[int] = F'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
snake_case : Any = True
break
snake_case : Optional[Any] = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(lowercase__ )
if len(lowercase__ ) > 0:
snake_case : Optional[Any] = "\n".join(sorted(lowercase__ ) )
raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 148
| 1
|
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__SCREAMING_SNAKE_CASE : str = False
try:
__SCREAMING_SNAKE_CASE : int = _is_package_available('''google.colab''')
except ModuleNotFoundError:
pass
@input.register
class __lowerCamelCase :
def __init__(self , lowerCamelCase = None , lowerCamelCase = [] ):
'''simple docstring'''
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = "*"
else:
_lowerCAmelCase = "➔ "
def A__ (self , lowerCamelCase , lowerCamelCase = "" ):
'''simple docstring'''
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , lowerCamelCase )
else:
forceWrite(self.choices[index] , lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if index == self.position:
forceWrite(f""" {self.arrow_char} """ )
self.write_choice(lowerCamelCase )
else:
forceWrite(f""" {self.choices[index]}""" )
reset_cursor()
def A__ (self , lowerCamelCase , lowerCamelCase = 1 ):
'''simple docstring'''
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(lowerCamelCase )
move_cursor(lowerCamelCase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def A__ (self ):
'''simple docstring'''
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def A__ (self ):
'''simple docstring'''
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def A__ (self ):
'''simple docstring'''
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def A__ (self ):
'''simple docstring'''
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(lowerCamelCase )] for number in range(10 )] )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , lowerCamelCase )
else:
return
else:
return
def A__ (self , lowerCamelCase = 0 ):
'''simple docstring'''
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(lowerCamelCase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(lowerCamelCase , """\n""" )
return choice
| 354
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE : Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Optional[int] = [
"RagModel",
"RagPreTrainedModel",
"RagSequenceForGeneration",
"RagTokenForGeneration",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = [
"TFRagModel",
"TFRagPreTrainedModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
_SCREAMING_SNAKE_CASE : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["ConditionalDetrFeatureExtractor"]
_SCREAMING_SNAKE_CASE : List[Any] = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Dict = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85
| 1
|
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCamelCase : str =logging.get_logger(__name__)
@add_end_docstrings(A__ )
class __a ( A__ ):
def __init__( self : List[str] , **SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Dict , SCREAMING_SNAKE_CASE : Union[str, List[str], "Image", List["Image"]] , **SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return super().__call__(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] , **SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = {}
if "candidate_labels" in kwargs:
UpperCamelCase__ : Optional[Any] = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
UpperCamelCase__ : int = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple=None , SCREAMING_SNAKE_CASE : Optional[int]="This is a photo of {}." ):
'''simple docstring'''
UpperCamelCase__ : Dict = load_image(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = self.image_processor(images=[image] , return_tensors=self.framework )
UpperCamelCase__ : Any = candidate_labels
UpperCamelCase__ : Dict = [hypothesis_template.format(SCREAMING_SNAKE_CASE ) for x in candidate_labels]
UpperCamelCase__ : Optional[Any] = self.tokenizer(SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = [text_inputs]
return inputs
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ : Tuple = model_inputs.pop("candidate_labels" )
UpperCamelCase__ : List[str] = model_inputs.pop("text_inputs" )
if isinstance(text_inputs[0] , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Dict = text_inputs[0]
else:
# Batching case.
UpperCamelCase__ : Union[str, Any] = text_inputs[0][0]
UpperCamelCase__ : Any = self.model(**SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = model_outputs.pop("candidate_labels" )
UpperCamelCase__ : int = model_outputs["logits"][0]
if self.framework == "pt":
UpperCamelCase__ : Dict = logits.softmax(dim=-1 ).squeeze(-1 )
UpperCamelCase__ : Optional[Any] = probs.tolist()
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : List[Any] = [scores]
elif self.framework == "tf":
UpperCamelCase__ : Optional[Any] = stable_softmax(SCREAMING_SNAKE_CASE , axis=-1 )
UpperCamelCase__ : Optional[int] = probs.numpy().tolist()
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
UpperCamelCase__ : Optional[int] = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , key=lambda SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 196
|
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
if len(__lowerCAmelCase ) == 0:
return False
UpperCamelCase__ : Any = len(__lowerCAmelCase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __lowerCAmelCase )
else:
return binary_search(a_list[midpoint + 1 :] , __lowerCAmelCase )
if __name__ == "__main__":
lowerCamelCase : Any =input('''Enter numbers separated by comma:\n''').strip()
lowerCamelCase : Dict =[int(item.strip()) for item in user_input.split(''',''')]
lowerCamelCase : List[str] =int(input('''Enter the number to be found in the list:\n''').strip())
lowerCamelCase : Union[str, Any] ='''''' if binary_search(sequence, target) else '''not '''
print(F"""{target} was {not_str}found in {sequence}""")
| 196
| 1
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Union[str, Any] = ["pixel_values"]
def __init__( self , a = True , a = None , a = PILImageResampling.BILINEAR , a = True , a = None , a = True , a = 1 / 2_5_5 , a = True , a = None , a = None , **a , ) -> None:
super().__init__(**a )
lowercase__ : Dict = size if size is not None else {'shortest_edge': 2_5_6}
lowercase__ : Union[str, Any] = get_size_dict(a , default_to_square=a )
lowercase__ : str = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
lowercase__ : Tuple = get_size_dict(a )
lowercase__ : Union[str, Any] = do_resize
lowercase__ : List[Any] = size
lowercase__ : List[Any] = resample
lowercase__ : Any = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : int = do_rescale
lowercase__ : int = rescale_factor
lowercase__ : Dict = do_normalize
lowercase__ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCAmelCase ( self , a , a , a = PILImageResampling.BICUBIC , a = None , **a , ) -> np.ndarray:
lowercase__ : int = get_size_dict(a , default_to_square=a )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
lowercase__ : Optional[int] = get_resize_output_image_size(a , size=size['shortest_edge'] , default_to_square=a )
return resize(a , size=a , resample=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a = None , **a , ) -> np.ndarray:
lowercase__ : List[str] = get_size_dict(a )
return center_crop(a , size=(size['height'], size['width']) , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a = None , **a ) -> np.ndarray:
return rescale(a , scale=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a , a , a = None , **a , ) -> np.ndarray:
return normalize(a , mean=a , std=a , data_format=a , **a )
def _UpperCAmelCase ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ) -> Tuple:
lowercase__ : Optional[Any] = do_resize if do_resize is not None else self.do_resize
lowercase__ : Tuple = size if size is not None else self.size
lowercase__ : Tuple = get_size_dict(a , default_to_square=a )
lowercase__ : Optional[int] = resample if resample is not None else self.resample
lowercase__ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Union[str, Any] = get_size_dict(a )
lowercase__ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : Optional[Any] = image_std if image_std is not None else self.image_std
lowercase__ : Tuple = make_list_of_images(a )
if not valid_images(a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowercase__ : int = [to_numpy_array(a ) for image in images]
if do_resize:
lowercase__ : List[Any] = [self.resize(image=a , size=a , resample=a ) for image in images]
if do_center_crop:
lowercase__ : Any = [self.center_crop(image=a , size=a ) for image in images]
if do_rescale:
lowercase__ : Any = [self.rescale(image=a , scale=a ) for image in images]
if do_normalize:
lowercase__ : Any = [self.normalize(image=a , mean=a , std=a ) for image in images]
lowercase__ : str = [to_channel_dimension_format(a , a ) for image in images]
lowercase__ : int = {'pixel_values': images}
return BatchFeature(data=a , tensor_type=a )
| 77
|
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def a_ ( _lowerCAmelCase : Callable[[int | float], int | float] , _lowerCAmelCase : int | float , _lowerCAmelCase : int | float , _lowerCAmelCase : int = 100 , ):
'''simple docstring'''
lowercase__ : Dict = x_start
lowercase__ : Union[str, Any] = fnc(_lowerCAmelCase )
lowercase__ : Optional[Any] = 0.0
for _ in range(_lowerCAmelCase ):
# Approximates curve as a sequence of linear lines and sums their length
lowercase__ : Union[str, Any] = (x_end - x_start) / steps + xa
lowercase__ : Union[str, Any] = fnc(_lowerCAmelCase )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
lowercase__ : Union[str, Any] = xa
lowercase__ : int = fxa
return length
if __name__ == "__main__":
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
return math.sin(10 * x )
print("f(x) = sin(10 * x)")
print("The length of the curve from x = -10 to x = 10 is:")
_UpperCamelCase : str = 10
while i <= 10_00_00:
print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 77
| 1
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__a: str = logging.get_logger(__name__)
__a: Tuple = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
__a: Optional[int] = {
"""b0""": {
"""hidden_dim""": 12_80,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 2_24,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 12_80,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 2_40,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 14_08,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 2_60,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 15_36,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 3_00,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 17_92,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 3_80,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 20_48,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 4_56,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 23_04,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 5_28,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 25_60,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 6_00,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : List[str] = EfficientNetConfig()
lowercase__ : Union[str, Any] = CONFIG_MAP[model_name]['''hidden_dim''']
lowercase__ : Any = CONFIG_MAP[model_name]['''width_coef''']
lowercase__ : Union[str, Any] = CONFIG_MAP[model_name]['''depth_coef''']
lowercase__ : Any = CONFIG_MAP[model_name]['''image_size''']
lowercase__ : List[Any] = CONFIG_MAP[model_name]['''dropout_rate''']
lowercase__ : Union[str, Any] = CONFIG_MAP[model_name]['''dw_padding''']
lowercase__ : int = '''huggingface/label-files'''
lowercase__ : List[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : int = 1000
lowercase__ : int = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
lowercase__ : Any = idalabel
lowercase__ : Any = {v: k for k, v in idalabel.items()}
return config
def __UpperCamelCase ( ):
lowercase__ : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : str = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Dict = CONFIG_MAP[model_name]['''image_size''']
lowercase__ : Optional[int] = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=UpperCAmelCase , )
return preprocessor
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Any = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
lowercase__ : str = sorted(set(UpperCAmelCase ) )
lowercase__ : Optional[int] = len(UpperCAmelCase )
lowercase__ : Dict = {b: str(UpperCAmelCase ) for b, i in zip(UpperCAmelCase , range(UpperCAmelCase ) )}
lowercase__ : Union[str, Any] = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
lowercase__ : Any = block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
lowercase__ : str = {}
for item in rename_keys:
if item[0] in original_param_names:
lowercase__ : Tuple = '''efficientnet.''' + item[1]
lowercase__ : Any = '''classifier.weight'''
lowercase__ : Optional[int] = '''classifier.bias'''
return key_mapping
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
for key, value in tf_params.items():
if "normalization" in key:
continue
lowercase__ : Union[str, Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowercase__ : Dict = torch.from_numpy(UpperCAmelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
lowercase__ : Union[str, Any] = torch.from_numpy(UpperCAmelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
lowercase__ : Any = torch.from_numpy(np.transpose(UpperCAmelCase ) )
else:
lowercase__ : str = torch.from_numpy(UpperCAmelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(UpperCAmelCase )
@torch.no_grad()
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
lowercase__ : List[str] = model_classes[model_name](
include_top=UpperCAmelCase , weights='''imagenet''' , input_tensor=UpperCAmelCase , input_shape=UpperCAmelCase , pooling=UpperCAmelCase , classes=1000 , classifier_activation='''softmax''' , )
lowercase__ : List[Any] = original_model.trainable_variables
lowercase__ : List[str] = original_model.non_trainable_variables
lowercase__ : Tuple = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowercase__ : Dict = param.numpy()
lowercase__ : Union[str, Any] = list(tf_params.keys() )
# Load HuggingFace model
lowercase__ : Optional[int] = get_efficientnet_config(UpperCAmelCase )
lowercase__ : List[Any] = EfficientNetForImageClassification(UpperCAmelCase ).eval()
lowercase__ : Tuple = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
lowercase__ : Optional[Any] = rename_keys(UpperCAmelCase )
replace_params(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Initialize preprocessor and preprocess input image
lowercase__ : List[str] = convert_image_processor(UpperCAmelCase )
lowercase__ : int = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowercase__ : Tuple = hf_model(**UpperCAmelCase )
lowercase__ : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
lowercase__ : str = False
lowercase__ : List[Any] = CONFIG_MAP[model_name]['''image_size''']
lowercase__ : List[Any] = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
lowercase__ : Any = image.img_to_array(UpperCAmelCase )
lowercase__ : Optional[Any] = np.expand_dims(UpperCAmelCase , axis=0 )
lowercase__ : List[Any] = original_model.predict(UpperCAmelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(UpperCAmelCase , UpperCAmelCase , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(UpperCAmelCase ):
os.mkdir(UpperCAmelCase )
# Save converted model and image processor
hf_model.save_pretrained(UpperCAmelCase )
preprocessor.save_pretrained(UpperCAmelCase )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
lowercase__ : Any = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(UpperCAmelCase )
hf_model.push_to_hub(UpperCAmelCase )
if __name__ == "__main__":
__a: Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
__a: int = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 214
|
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ : Dict = len(UpperCAmelCase )
print('''The following activities are selected:''' )
# The first activity is always selected
lowercase__ : str = 0
print(UpperCAmelCase , end=''',''' )
# Consider rest of the activities
for j in range(UpperCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(UpperCAmelCase , end=''',''' )
lowercase__ : str = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__a: str = [1, 3, 0, 5, 8, 5]
__a: Optional[Any] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 214
| 1
|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : str = logging.get_logger(__name__)
def _UpperCAmelCase ( a__ , a__=False):
'''simple docstring'''
a_ : Dict = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight'''))
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias'''))
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight'''))
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias'''))
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight'''))
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias'''))
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight'''))
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias'''))
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight'''))
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias'''))
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
])
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
])
# if just the base model, we should remove "vit" from all keys that start with "vit"
a_ : List[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 _UpperCAmelCase ( a__ , a__ , a__=False):
'''simple docstring'''
for i in range(config.num_hidden_layers):
if base_model:
a_ : Tuple = """"""
else:
a_ : Any = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
a_ : Dict = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''')
a_ : Optional[int] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''')
# next, add query, keys and values (in that order) to the state dict
a_ : Any = in_proj_weight[
: config.hidden_size, :
]
a_ : List[Any] = in_proj_bias[: config.hidden_size]
a_ : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
a_ : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
a_ : Union[str, Any] = in_proj_weight[
-config.hidden_size :, :
]
a_ : Dict = in_proj_bias[-config.hidden_size :]
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Tuple = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(A__ , A__)
def _UpperCAmelCase ( a__ , a__ , a__):
'''simple docstring'''
a_ : List[str] = dct.pop(A__)
a_ : Tuple = val
def _UpperCAmelCase ( ):
'''simple docstring'''
a_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
a_ : str = Image.open(requests.get(A__ , stream=A__).raw)
return im
@torch.no_grad()
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
a_ : int = ViTConfig()
a_ : int = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
a_ : int = True
a_ : List[str] = int(vit_name[-1_2:-1_0])
a_ : List[Any] = int(vit_name[-9:-6])
else:
a_ : Dict = 1_0_0_0
a_ : Dict = """huggingface/label-files"""
a_ : Optional[int] = """imagenet-1k-id2label.json"""
a_ : Optional[int] = json.load(open(hf_hub_download(A__ , A__ , repo_type="""dataset""") , """r"""))
a_ : Optional[int] = {int(A__): v for k, v in idalabel.items()}
a_ : Optional[int] = idalabel
a_ : List[str] = {v: k for k, v in idalabel.items()}
a_ : Optional[int] = int(vit_name[-6:-4])
a_ : List[Any] = int(vit_name[-3:])
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("""tiny"""):
a_ : List[Any] = 1_9_2
a_ : int = 7_6_8
a_ : Optional[Any] = 1_2
a_ : List[Any] = 3
elif vit_name[9:].startswith("""small"""):
a_ : str = 3_8_4
a_ : Any = 1_5_3_6
a_ : Optional[int] = 1_2
a_ : Optional[int] = 6
else:
pass
else:
if vit_name[4:].startswith("""small"""):
a_ : Any = 7_6_8
a_ : Dict = 2_3_0_4
a_ : Any = 8
a_ : int = 8
elif vit_name[4:].startswith("""base"""):
pass
elif vit_name[4:].startswith("""large"""):
a_ : Union[str, Any] = 1_0_2_4
a_ : Union[str, Any] = 4_0_9_6
a_ : int = 2_4
a_ : Optional[Any] = 1_6
elif vit_name[4:].startswith("""huge"""):
a_ : Optional[int] = 1_2_8_0
a_ : str = 5_1_2_0
a_ : Any = 3_2
a_ : Union[str, Any] = 1_6
# load original model from timm
a_ : int = timm.create_model(A__ , pretrained=A__)
timm_model.eval()
# load state_dict of original model, remove and rename some keys
a_ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(A__)
a_ : Any = create_rename_keys(A__ , A__)
for src, dest in rename_keys:
rename_key(A__ , A__ , A__)
read_in_q_k_v(A__ , A__ , A__)
# load HuggingFace model
if vit_name[-5:] == "in21k":
a_ : List[Any] = ViTModel(A__).eval()
else:
a_ : Any = ViTForImageClassification(A__).eval()
model.load_state_dict(A__)
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
a_ : Optional[Any] = DeiTImageProcessor(size=config.image_size)
else:
a_ : List[Any] = ViTImageProcessor(size=config.image_size)
a_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
a_ : int = encoding["""pixel_values"""]
a_ : Union[str, Any] = model(A__)
if base_model:
a_ : List[str] = timm_model.forward_features(A__)
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(A__ , outputs.pooler_output , atol=1e-3)
else:
a_ : List[str] = timm_model(A__)
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(A__ , outputs.logits , atol=1e-3)
Path(A__).mkdir(exist_ok=A__)
print(f'''Saving model {vit_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__":
__snake_case : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_patch16_224""",
type=str,
help="""Name of the ViT 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."""
)
__snake_case : Tuple = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 248
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 28
| 0
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""note_seq"""]
def __init__( self : List[Any] , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str ) -> str:
"""simple docstring"""
requires_backends(self , ['''note_seq'''] )
@classmethod
def lowerCAmelCase ( cls : Any , *UpperCamelCase__ : Union[str, Any] , **UpperCamelCase__ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['''note_seq'''] )
@classmethod
def lowerCAmelCase ( cls : List[str] , *UpperCamelCase__ : Dict , **UpperCamelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ['''note_seq'''] )
| 83
|
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 83
| 1
|
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 __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase ( self ):
'''simple docstring'''
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 ):
'''simple docstring'''
if self.config_name == "default":
__lowerCamelCase = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
__lowerCamelCase = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=False ):
'''simple docstring'''
if gpus is None:
__lowerCamelCase = 1 if torch.cuda.is_available() else 0
__lowerCamelCase = {'src': sources, 'mt': predictions, 'ref': references}
__lowerCamelCase = [dict(zip(_A , _A ) ) for t in zip(*data.values() )]
__lowerCamelCase = self.scorer.predict(_A , gpus=_A , progress_bar=_A )
return {"mean_score": mean_score, "scores": scores}
| 330
|
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE_ :
def __init__( self : List[Any] , _A : Optional[Any] , _A : Dict=13 , _A : Union[str, Any]=30 , _A : Tuple=2 , _A : Union[str, Any]=3 , _A : Optional[int]=True , _A : Optional[Any]=True , _A : str=32 , _A : int=2 , _A : List[str]=4 , _A : List[str]=37 , _A : Tuple="gelu" , _A : Dict=0.1 , _A : Optional[Any]=0.1 , _A : Optional[int]=10 , _A : Optional[int]=0.0_2 , _A : Optional[Any]=3 , _A : str=0.6 , _A : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
snake_case_ : Optional[int] = parent
snake_case_ : Tuple = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : List[str] = patch_size
snake_case_ : List[str] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : Any = use_labels
snake_case_ : Tuple = hidden_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : List[str] = initializer_range
snake_case_ : Optional[Any] = mask_ratio
snake_case_ : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
snake_case_ : Optional[int] = (image_size // patch_size) ** 2
snake_case_ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Union[str, Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
"""simple docstring"""
return ViTMAEConfig(
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 , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_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 , mask_ratio=self.mask_ratio , )
def UpperCAmelCase_ ( self : List[Any] , _A : int , _A : Dict , _A : str ) -> Dict:
"""simple docstring"""
snake_case_ : Union[str, Any] = TFViTMAEModel(config=_A )
snake_case_ : str = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : Dict , _A : Dict , _A : Any , _A : List[Any] ) -> int:
"""simple docstring"""
snake_case_ : Any = TFViTMAEForPreTraining(_A )
snake_case_ : Optional[Any] = model(_A , training=_A )
# expected sequence length = num_patches
snake_case_ : List[str] = (self.image_size // self.patch_size) ** 2
snake_case_ : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
snake_case_ : str = 1
snake_case_ : Dict = TFViTMAEForPreTraining(_A )
snake_case_ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : List[str] = model(_A , training=_A )
snake_case_ : Optional[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
snake_case_ : List[Any] = self.prepare_config_and_inputs()
((snake_case_) ,(snake_case_) ,(snake_case_)) : Any = config_and_inputs
snake_case_ : Optional[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ , unittest.TestCase ):
__magic_name__: List[str] = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
__magic_name__: str = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}
__magic_name__: Dict = False
__magic_name__: Dict = False
__magic_name__: List[Any] = False
__magic_name__: Dict = False
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[Any] = TFViTMAEModelTester(self )
snake_case_ : Tuple = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=37 )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds' )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
pass
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ ,snake_case_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case_ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , tf.keras.layers.Layer ) )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
"""simple docstring"""
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[str] = model_class(_A )
snake_case_ : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Dict = [*signature.parameters.keys()]
snake_case_ : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , _A )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
"""simple docstring"""
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_A )
def UpperCAmelCase_ ( self : Tuple ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
snake_case_ ,snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Optional[int] = int((config.image_size // config.patch_size) ** 2 )
snake_case_ : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
snake_case_ : Optional[Any] = model_class(_A )
snake_case_ : Union[str, Any] = self._prepare_for_class(_A , _A )
snake_case_ : List[str] = model(_A , noise=_A )
snake_case_ : Tuple = copy.deepcopy(self._prepare_for_class(_A , _A ) )
snake_case_ : str = model(**_A , noise=_A )
snake_case_ : Union[str, Any] = outputs_dict[0].numpy()
snake_case_ : Optional[Any] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
np.random.seed(2 )
snake_case_ ,snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Tuple = int((config.image_size // config.patch_size) ** 2 )
snake_case_ : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(_A : int ):
snake_case_ : Any = {}
for k, v in inputs_dict.items():
if tf.is_tensor(_A ):
snake_case_ : str = v.numpy()
else:
snake_case_ : Optional[Any] = np.array(_A )
return inputs_np_dict
for model_class in self.all_model_classes:
snake_case_ : int = model_class(_A )
snake_case_ : List[Any] = self._prepare_for_class(_A , _A )
snake_case_ : Any = prepare_numpy_arrays(_A )
snake_case_ : List[Any] = model(_A , noise=_A )
snake_case_ : List[Any] = model(**_A , noise=_A )
self.assert_outputs_same(_A , _A )
def UpperCAmelCase_ ( self : Tuple , _A : Union[str, Any] , _A : Union[str, Any] , _A : List[Any] ) -> List[str]:
"""simple docstring"""
np.random.seed(2 )
snake_case_ : Optional[int] = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
snake_case_ : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
snake_case_ : Optional[int] = tf.constant(_A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
snake_case_ : Optional[Any] = tf_noise
super().check_pt_tf_models(_A , _A , _A )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
snake_case_ ,snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : int = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(_A )
if module_member_name.endswith('MainLayer' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('MainLayer' )] == model_class.__name__[: -len('Model' )]
for module_member in (getattr(_A , _A ),)
if isinstance(_A , _A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(_A , '_keras_serializable' , _A )
}
snake_case_ : List[Any] = int((config.image_size // config.patch_size) ** 2 )
snake_case_ : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
snake_case_ : Optional[int] = tf.convert_to_tensor(_A )
inputs_dict.update({'noise': noise} )
for main_layer_class in tf_main_layer_classes:
snake_case_ : Optional[Any] = main_layer_class(_A )
snake_case_ : List[str] = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
snake_case_ : Union[str, Any] = tf.keras.Model(_A , outputs=main_layer(_A ) )
snake_case_ : int = model(_A )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : List[Any] = os.path.join(_A , 'keras_model.h5' )
model.save(_A )
snake_case_ : str = tf.keras.models.load_model(
_A , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(_A , tf.keras.Model )
snake_case_ : List[str] = model(_A )
self.assert_outputs_same(_A , _A )
@slow
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
np.random.seed(2 )
snake_case_ ,snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : int = int((config.image_size // config.patch_size) ** 2 )
snake_case_ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
snake_case_ : Optional[Any] = model_class(_A )
snake_case_ : Optional[Any] = self._prepare_for_class(_A , _A )
snake_case_ : int = model(_A , noise=_A )
if model_class.__name__ == "TFViTMAEModel":
snake_case_ : Any = outputs.last_hidden_state.numpy()
snake_case_ : Optional[int] = 0
else:
snake_case_ : str = outputs.logits.numpy()
snake_case_ : Optional[Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A , saved_model=_A )
snake_case_ : Any = model_class.from_pretrained(_A )
snake_case_ : Any = model(_A , noise=_A )
if model_class.__name__ == "TFViTMAEModel":
snake_case_ : Dict = after_outputs['last_hidden_state'].numpy()
snake_case_ : Dict = 0
else:
snake_case_ : Any = after_outputs['logits'].numpy()
snake_case_ : Optional[Any] = 0
snake_case_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def UpperCAmelCase_ ( self : Any ) -> str:
"""simple docstring"""
np.random.seed(2 )
snake_case_ ,snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Optional[int] = int((config.image_size // config.patch_size) ** 2 )
snake_case_ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
snake_case_ : str = model_class(_A )
snake_case_ : int = self._prepare_for_class(_A , _A )
snake_case_ : str = model(_A , noise=_A )
snake_case_ : Dict = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(_A )
snake_case_ : Any = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
snake_case_ : str = model_class.from_config(model.config )
snake_case_ : Union[str, Any] = new_model(_A ) # Build model
new_model.set_weights(model.get_weights() )
snake_case_ : List[str] = new_model(_A , noise=_A )
self.assert_outputs_same(_A , _A )
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = TFViTMAEModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(_A )
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase_ ( self : str ) -> Dict:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None
@slow
def UpperCAmelCase_ ( self : str ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
snake_case_ : List[str] = TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' )
snake_case_ : List[Any] = self.default_image_processor
snake_case_ : Dict = prepare_img()
snake_case_ : Optional[Any] = image_processor(images=_A , return_tensors='tf' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
snake_case_ : int = ViTMAEConfig()
snake_case_ : List[Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
snake_case_ : List[Any] = np.random.uniform(size=(1, num_patches) )
# forward pass
snake_case_ : Optional[Any] = model(**_A , noise=_A )
# verify the logits
snake_case_ : Optional[int] = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , _A )
snake_case_ : Any = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , _A , atol=1E-4 )
| 327
| 0
|
from math import isclose, sqrt
def __lowerCamelCase ( lowerCamelCase__ : float , lowerCamelCase__ : float , lowerCamelCase__ : float ):
'''simple docstring'''
lowerCamelCase = point_y / 4 / point_x
lowerCamelCase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase = outgoing_gradient**2 + 4
lowerCamelCase = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase = (point_y - outgoing_gradient * point_x) ** 2 - 100
lowerCamelCase = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase = x_minus if isclose(lowerCamelCase__ , lowerCamelCase__ ) else x_plus
lowerCamelCase = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def __lowerCamelCase ( lowerCamelCase__ : float = 1.4 , lowerCamelCase__ : float = -9.6 ):
'''simple docstring'''
lowerCamelCase = 0
lowerCamelCase = first_x_coord
lowerCamelCase = first_y_coord
lowerCamelCase = (1_0.1 - point_y) / (0.0 - point_x)
while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
lowerCamelCase , lowerCamelCase , lowerCamelCase = next_point(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f"""{solution() = }""")
| 66
|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MCTCTProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = [
"MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MCTCTForCTC",
"MCTCTModel",
"MCTCTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
UpperCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 66
| 1
|
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase = TypeVar('''T''')
__lowerCAmelCase = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase = Union[T, List[T], Dict[str, T]]
__lowerCAmelCase = Union[str, bytes, os.PathLike]
| 196
|
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : str = CpmAntTokenizer
__lowercase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
super().setUp()
lowercase__: Any = [
'<d>',
'</d>',
'<s>',
'</s>',
'</_>',
'<unk>',
'<pad>',
'</n>',
'我',
'是',
'C',
'P',
'M',
'A',
'n',
't',
]
lowercase__: List[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] ) )
@tooslow
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Optional[int] = CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b' )
lowercase__: Optional[Any] = '今天天气真好！'
lowercase__: str = ['今天', '天气', '真', '好', '！']
lowercase__: Optional[Any] = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: List[str] = '今天天气真好！'
lowercase__: List[str] = [tokenizer.bos_token] + tokens
lowercase__: Tuple = [6, 9_802, 14_962, 2_082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
lowercase__: Any = tokenizer.decode(lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
| 196
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = 1
__magic_name__ : List[Any] = 3
__magic_name__ : Tuple = (32, 32)
__magic_name__ : Tuple = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(_SCREAMING_SNAKE_CASE )
return image
@property
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
__magic_name__ : Any = 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 , )
return model
@property
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
__magic_name__ : Dict = 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 , )
return model
@property
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
__magic_name__ : str = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_006 , )
return RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE )
@property
def SCREAMING_SNAKE_CASE ( self ):
def extract(*_a , **_a ):
class _snake_case :
def __init__( self ):
__magic_name__ : Tuple = torch.ones([0] )
def SCREAMING_SNAKE_CASE ( self , _a ):
self.pixel_values.to(_SCREAMING_SNAKE_CASE )
return self
return Out()
return extract
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : List[Any] = self.dummy_cond_unet
__magic_name__ : Optional[Any] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
__magic_name__ : List[Any] = self.dummy_vae
__magic_name__ : int = self.dummy_text_encoder
__magic_name__ : Tuple = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
__magic_name__ : List[str] = 77
__magic_name__ : List[Any] = self.dummy_image.to(_SCREAMING_SNAKE_CASE )
__magic_name__ : Any = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
__magic_name__ : Union[str, Any] = AltDiffusionImgaImgPipeline(
unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__magic_name__ : int = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=_SCREAMING_SNAKE_CASE )
__magic_name__ : Optional[int] = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__magic_name__ : int = "A painting of a squirrel eating a burger"
__magic_name__ : List[str] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
__magic_name__ : Union[str, Any] = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , )
__magic_name__ : Dict = output.images
__magic_name__ : Optional[int] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
__magic_name__ : Optional[Any] = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , )[0]
__magic_name__ : List[Any] = image[0, -3:, -3:, -1]
__magic_name__ : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : Union[str, Any] = np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.dummy_cond_unet
__magic_name__ : Optional[int] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
__magic_name__ : Tuple = self.dummy_vae
__magic_name__ : Any = self.dummy_text_encoder
__magic_name__ : Dict = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
__magic_name__ : Dict = 77
__magic_name__ : Tuple = self.dummy_image.to(_SCREAMING_SNAKE_CASE )
# put models in fp16
__magic_name__ : Union[str, Any] = unet.half()
__magic_name__ : Optional[Any] = vae.half()
__magic_name__ : Tuple = bert.half()
# make sure here that pndm scheduler skips prk
__magic_name__ : List[str] = AltDiffusionImgaImgPipeline(
unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__magic_name__ : Optional[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=_SCREAMING_SNAKE_CASE )
__magic_name__ : Dict = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__magic_name__ : Union[str, Any] = "A painting of a squirrel eating a burger"
__magic_name__ : List[str] = torch.manual_seed(0 )
__magic_name__ : Dict = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
# resize to resolution that is divisible by 8 but not 16 or 32
__magic_name__ : List[str] = init_image.resize((760, 504) )
__magic_name__ : Optional[int] = "BAAI/AltDiffusion"
__magic_name__ : Union[str, Any] = AltDiffusionImgaImgPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
pipe.enable_attention_slicing()
__magic_name__ : Union[str, Any] = "A fantasy landscape, trending on artstation"
__magic_name__ : Union[str, Any] = torch.manual_seed(0 )
__magic_name__ : List[str] = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , strength=0.75 , guidance_scale=7.5 , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__magic_name__ : Tuple = output.images[0]
__magic_name__ : Dict = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
__magic_name__ : Tuple = np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
__magic_name__ : Optional[Any] = init_image.resize((768, 512) )
__magic_name__ : Union[str, Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )
__magic_name__ : List[Any] = "BAAI/AltDiffusion"
__magic_name__ : Dict = AltDiffusionImgaImgPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
pipe.enable_attention_slicing()
__magic_name__ : int = "A fantasy landscape, trending on artstation"
__magic_name__ : Optional[Any] = torch.manual_seed(0 )
__magic_name__ : List[Any] = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , strength=0.75 , guidance_scale=7.5 , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__magic_name__ : Dict = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 368
|
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _snake_case ( snake_case ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_a , "tf_padding" ) )
self.parent.assertTrue(hasattr(_a , "depth_multiplier" ) )
class _snake_case :
def __init__( self , _a , _a=13 , _a=3 , _a=32 , _a=0.25 , _a=8 , _a=True , _a=1_024 , _a=32 , _a="relu6" , _a=0.1 , _a=0.02 , _a=True , _a=True , _a=10 , _a=None , ):
__magic_name__ : Optional[int] = parent
__magic_name__ : Union[str, Any] = batch_size
__magic_name__ : Tuple = num_channels
__magic_name__ : Tuple = image_size
__magic_name__ : Any = depth_multiplier
__magic_name__ : Any = min_depth
__magic_name__ : Any = tf_padding
__magic_name__ : int = int(last_hidden_size * depth_multiplier )
__magic_name__ : Any = output_stride
__magic_name__ : Tuple = hidden_act
__magic_name__ : Optional[int] = classifier_dropout_prob
__magic_name__ : Any = use_labels
__magic_name__ : str = is_training
__magic_name__ : List[str] = num_labels
__magic_name__ : str = initializer_range
__magic_name__ : Union[str, Any] = scope
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : Any = None
__magic_name__ : Optional[Any] = None
if self.use_labels:
__magic_name__ : int = ids_tensor([self.batch_size] , self.num_labels )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__magic_name__ : List[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE ( self ):
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , _a ):
__magic_name__ : List[str] = MobileNetVaModel(config=_a )
model.to(_a )
model.eval()
__magic_name__ : Optional[int] = model(_a )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , _a ):
__magic_name__ : Any = self.num_labels
__magic_name__ : Dict = MobileNetVaForImageClassification(_a )
model.to(_a )
model.eval()
__magic_name__ : Any = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] = config_and_inputs
__magic_name__ : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
UpperCamelCase__ = (
{'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = MobileNetVaModelTester(self )
__magic_name__ : List[str] = MobileNetVaConfigTester(self , config_class=_a , has_text_modality=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : str = model_class(_a )
__magic_name__ : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple = [*signature.parameters.keys()]
__magic_name__ : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def SCREAMING_SNAKE_CASE ( self ):
def check_hidden_states_output(_a , _a , _a ):
__magic_name__ : Dict = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
__magic_name__ : Tuple = model(**self._prepare_for_class(_a , _a ) )
__magic_name__ : Optional[Any] = outputs.hidden_states
__magic_name__ : Optional[Any] = 26
self.assertEqual(len(_a ) , _a )
__magic_name__ , __magic_name__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__magic_name__ : Dict = True
check_hidden_states_output(_a , _a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : List[str] = MobileNetVaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(_a )
__magic_name__ : Dict = self.default_image_processor
__magic_name__ : List[str] = prepare_img()
__magic_name__ : Optional[Any] = image_processor(images=_a , return_tensors="pt" ).to(_a )
# forward pass
with torch.no_grad():
__magic_name__ : Union[str, Any] = model(**_a )
# verify the logits
__magic_name__ : List[str] = torch.Size((1, 1_001) )
self.assertEqual(outputs.logits.shape , _a )
__magic_name__ : Union[str, Any] = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 41
| 0
|
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
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''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 SCREAMING_SNAKE_CASE__ (__snake_case ):
__lowerCamelCase : List[Any] = """yolos"""
def __init__( self , a=768 , a=12 , a=12 , a=3072 , a="gelu" , a=0.0 , a=0.0 , a=0.02 , a=1e-12 , a=[512, 864] , a=16 , a=3 , a=True , a=100 , a=True , a=False , a=1 , a=5 , a=2 , a=5 , a=2 , a=0.1 , **a , ):
super().__init__(**a)
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Optional[int] = num_hidden_layers
lowercase__ : List[Any] = num_attention_heads
lowercase__ : Tuple = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Optional[Any] = initializer_range
lowercase__ : str = layer_norm_eps
lowercase__ : Optional[int] = image_size
lowercase__ : int = patch_size
lowercase__ : Union[str, Any] = num_channels
lowercase__ : str = qkv_bias
lowercase__ : List[str] = num_detection_tokens
lowercase__ : Dict = use_mid_position_embeddings
lowercase__ : List[str] = auxiliary_loss
# Hungarian matcher
lowercase__ : Tuple = class_cost
lowercase__ : int = bbox_cost
lowercase__ : str = giou_cost
# Loss coefficients
lowercase__ : Union[str, Any] = bbox_loss_coefficient
lowercase__ : List[str] = giou_loss_coefficient
lowercase__ : Dict = eos_coefficient
class SCREAMING_SNAKE_CASE__ (__snake_case ):
__lowerCamelCase : Optional[int] = version.parse("""1.11""" )
@property
def snake_case_ ( self):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def snake_case_ ( self):
return 1e-4
@property
def snake_case_ ( self):
return 12
| 214
|
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case_ = TypeVar('''T''')
snake_case_ = TypeVar('''U''')
class SCREAMING_SNAKE_CASE__ (Generic[T, U] ):
def __init__( self , a , a):
lowercase__ : List[Any] = key
lowercase__ : List[Any] = val
lowercase__ : DoubleLinkedListNode[T, U] | None = None
lowercase__ : DoubleLinkedListNode[T, U] | None = None
def __repr__( self):
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
)
class SCREAMING_SNAKE_CASE__ (Generic[T, U] ):
def __init__( self):
lowercase__ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(a , a)
lowercase__ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(a , a)
lowercase__ , lowercase__ : Union[str, Any] = self.rear, self.head
def __repr__( self):
lowercase__ : Any = ['DoubleLinkedList']
lowercase__ : List[str] = self.head
while node.next is not None:
rep.append(str(a))
lowercase__ : Tuple = node.next
rep.append(str(self.rear))
return ",\n ".join(a)
def snake_case_ ( self , a):
lowercase__ : Optional[Any] = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
lowercase__ : Dict = node
lowercase__ : int = previous
lowercase__ : Union[str, Any] = node
lowercase__ : Optional[int] = self.rear
def snake_case_ ( self , a):
if node.prev is None or node.next is None:
return None
lowercase__ : Union[str, Any] = node.next
lowercase__ : Tuple = node.prev
lowercase__ : Union[str, Any] = None
lowercase__ : List[Any] = None
return node
class SCREAMING_SNAKE_CASE__ (Generic[T, U] ):
__lowerCamelCase : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self , a):
lowercase__ : DoubleLinkedList[T, U] = DoubleLinkedList()
lowercase__ : Optional[Any] = capacity
lowercase__ : Union[str, Any] = 0
lowercase__ : Tuple = 0
lowercase__ : int = 0
lowercase__ : dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__( self):
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self , a):
return key in self.cache
def snake_case_ ( self , a):
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
lowercase__ : DoubleLinkedListNode[T, U] = self.cache[key]
lowercase__ : str = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(a)
return node.val
self.miss += 1
return None
def snake_case_ ( self , a , a):
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
lowercase__ : Optional[int] = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(a) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
lowercase__ : Optional[Any] = DoubleLinkedListNode(a , a)
self.list.add(self.cache[key])
self.num_keys += 1
else:
# bump node to the end of the list, update value
lowercase__ : Any = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
lowercase__ : Union[str, Any] = value
self.list.add(a)
@classmethod
def snake_case_ ( cls , a = 128):
def cache_decorator_inner(a) -> Callable[..., U]:
def cache_decorator_wrapper(*a) -> U:
if func not in cls.decorator_function_to_instance_map:
lowercase__ : Dict = LRUCache(a)
lowercase__ : str = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
lowercase__ : str = func(*a)
cls.decorator_function_to_instance_map[func].put(args[0] , a)
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(a , 'cache_info' , a) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214
| 1
|
import os
from pathlib import Path
def UpperCamelCase_( ) -> List[Any]:
from torch.utils.cpp_extension import load
UpperCAmelCase__ = Path(snake_case__ ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
UpperCAmelCase__ = [
root / filename
for filename in [
'vision.cpp',
os.path.join('cpu' , 'ms_deform_attn_cpu.cpp' ),
os.path.join('cuda' , 'ms_deform_attn_cuda.cu' ),
]
]
load(
'MultiScaleDeformableAttention' , snake_case__ , with_cuda=snake_case__ , extra_include_paths=[str(snake_case__ )] , extra_cflags=['-DWITH_CUDA=1'] , extra_cuda_cflags=[
'-DCUDA_HAS_FP16=1',
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 335
|
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
@add_end_docstrings(_UpperCamelCase )
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , **__a ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__a )
requires_backends(self , 'vision' )
requires_backends(self , 'torch' )
if self.framework != "pt":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
self.check_model_type(__a )
def UpperCamelCase__ (self , **__a ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
# preprocess args
if "points_per_batch" in kwargs:
UpperCAmelCase__ = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
UpperCAmelCase__ = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
UpperCAmelCase__ = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
UpperCAmelCase__ = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
UpperCAmelCase__ = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
UpperCAmelCase__ = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
UpperCAmelCase__ = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
UpperCAmelCase__ = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
UpperCAmelCase__ = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
UpperCAmelCase__ = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
UpperCAmelCase__ = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
UpperCAmelCase__ = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__(self , __a , *__a , __a=None , __a=None , **__a ) -> List[str]:
"""simple docstring"""
return super().__call__(__a , *__a , num_workers=__a , batch_size=__a , **__a )
def UpperCamelCase__ (self , __a , __a=64 , __a = 0 , __a = 512 / 1500 , __a = 32 , __a = 1 , ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = load_image(__a )
UpperCAmelCase__ = self.image_processor.size['longest_edge']
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.generate_crop_boxes(
__a , __a , __a , __a , __a , __a )
UpperCAmelCase__ = self.image_processor(images=__a , return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
UpperCAmelCase__ = self.get_inference_context()
with inference_context():
UpperCAmelCase__ = self._ensure_tensor_on_device(__a , device=self.device )
UpperCAmelCase__ = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
UpperCAmelCase__ = image_embeddings
UpperCAmelCase__ = grid_points.shape[1]
UpperCAmelCase__ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0 , __a , __a ):
UpperCAmelCase__ = grid_points[:, i : i + points_per_batch, :, :]
UpperCAmelCase__ = input_labels[:, i : i + points_per_batch]
UpperCAmelCase__ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def UpperCamelCase__ (self , __a , __a=0.88 , __a=0.95 , __a=0 , __a=1 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = model_inputs.pop('input_boxes' )
UpperCAmelCase__ = model_inputs.pop('is_last' )
UpperCAmelCase__ = model_inputs.pop('original_sizes' ).tolist()
UpperCAmelCase__ = model_inputs.pop('reshaped_input_sizes' ).tolist()
UpperCAmelCase__ = self.model(**__a )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
UpperCAmelCase__ = model_outputs['pred_masks']
UpperCAmelCase__ = self.image_processor.post_process_masks(
__a , __a , __a , __a , binarize=__a )
UpperCAmelCase__ = model_outputs['iou_scores']
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __a , __a , __a , __a , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def UpperCamelCase__ (self , __a , __a=False , __a=False , __a=0.7 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
UpperCAmelCase__ = torch.cat(__a )
UpperCAmelCase__ = torch.cat(__a )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.post_process_for_mask_generation(
__a , __a , __a , __a )
UpperCAmelCase__ = defaultdict(__a )
for output in model_outputs:
for k, v in output.items():
extra[k].append(__a )
UpperCAmelCase__ = {}
if output_rle_mask:
UpperCAmelCase__ = rle_mask
if output_bboxes_mask:
UpperCAmelCase__ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 335
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
snake_case_ : Any = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[str] = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
snake_case_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 83
|
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1 , UpperCAmelCase_ = 1_0_0_0 ):
_UpperCamelCase : int = 1
_UpperCamelCase : Union[str, Any] = 0
for divide_by_number in range(UpperCAmelCase_ , digit + 1 ):
_UpperCamelCase : list[int] = []
_UpperCamelCase : int = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(UpperCAmelCase_ ):
_UpperCamelCase : Optional[Any] = len(UpperCAmelCase_ )
_UpperCamelCase : List[Any] = divide_by_number
else:
has_been_divided.append(UpperCAmelCase_ )
_UpperCamelCase : str = now_divide * 1_0 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83
| 1
|
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase__ =argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
UpperCamelCase__ =parser.parse_args()
if args.model_type == "bert":
UpperCamelCase__ =BertForMaskedLM.from_pretrained(args.model_name)
UpperCamelCase__ ="bert"
else:
raise ValueError('args.model_type should be \"bert\".')
UpperCamelCase__ =model.state_dict()
UpperCamelCase__ ={}
for w in ["word_embeddings", "position_embeddings"]:
UpperCamelCase__ =state_dict[f"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
UpperCamelCase__ =state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
UpperCamelCase__ =0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
UpperCamelCase__ =state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
UpperCamelCase__ =state_dict["cls.predictions.decoder.weight"]
UpperCamelCase__ =state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
UpperCamelCase__ =state_dict[f"cls.predictions.transform.dense.{w}"]
UpperCamelCase__ =state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
| 371
|
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__( __lowercase , __lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self , __lowerCamelCase = 1_2_8 , __lowerCamelCase = 2_5_6 , __lowerCamelCase = 2000.0 , __lowerCamelCase = 7_6_8 , __lowerCamelCase = 1_2 , __lowerCamelCase = 1_2 , __lowerCamelCase = 6_4 , __lowerCamelCase = 2_0_4_8 , __lowerCamelCase = 0.1 , ) -> int:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[int] = nn.Sequential(
nn.Linear(__lowerCamelCase , d_model * 4 , bias=__lowerCamelCase ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__lowerCamelCase ) , nn.SiLU() , )
_SCREAMING_SNAKE_CASE : str = nn.Embedding(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(p=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList()
for lyr_num in range(__lowerCamelCase ):
# FiLM conditional T5 decoder
_SCREAMING_SNAKE_CASE : Optional[int] = DecoderLayer(d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase )
self.decoders.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = TaLayerNorm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = nn.Dropout(p=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
_SCREAMING_SNAKE_CASE : int = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_SCREAMING_SNAKE_CASE : Tuple = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_SCREAMING_SNAKE_CASE : str = self.conditioning_emb(__lowerCamelCase ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_SCREAMING_SNAKE_CASE : Tuple = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_SCREAMING_SNAKE_CASE : Optional[int] = torch.broadcast_to(
torch.arange(__lowerCamelCase , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.position_encoding(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.continuous_inputs_projection(__lowerCamelCase )
inputs += position_encodings
_SCREAMING_SNAKE_CASE : Any = self.dropout(__lowerCamelCase )
# decoder: No padding present.
_SCREAMING_SNAKE_CASE : Any = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_SCREAMING_SNAKE_CASE : List[str] = [(x, self.encoder_decoder_mask(__lowerCamelCase , __lowerCamelCase )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_SCREAMING_SNAKE_CASE : Tuple = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_SCREAMING_SNAKE_CASE : Optional[Any] = lyr(
__lowerCamelCase , conditioning_emb=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , )[0]
_SCREAMING_SNAKE_CASE : int = self.decoder_norm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = self.post_dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = self.spec_out(__lowerCamelCase )
return spec_out
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=1E-6 ) -> Dict:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , dropout_rate=__lowerCamelCase ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , dropout_rate=__lowerCamelCase , layer_norm_epsilon=__lowerCamelCase , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase , layer_norm_epsilon=__lowerCamelCase ) )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : int = self.layer[0](
__lowerCamelCase , conditioning_emb=__lowerCamelCase , attention_mask=__lowerCamelCase , )
if encoder_hidden_states is not None:
_SCREAMING_SNAKE_CASE : str = torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_SCREAMING_SNAKE_CASE : Tuple = self.layer[1](
__lowerCamelCase , key_value_states=__lowerCamelCase , attention_mask=__lowerCamelCase , )
# Apply Film Conditional Feed Forward layer
_SCREAMING_SNAKE_CASE : Optional[Any] = self.layer[-1](__lowerCamelCase , __lowerCamelCase )
return (hidden_states,)
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : List[str] = TaLayerNorm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = TaFiLMLayer(in_features=d_model * 4 , out_features=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = Attention(query_dim=__lowerCamelCase , heads=__lowerCamelCase , dim_head=__lowerCamelCase , out_bias=__lowerCamelCase , scale_qk=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , ) -> Union[str, Any]:
# pre_self_attention_layer_norm
_SCREAMING_SNAKE_CASE : int = self.layer_norm(__lowerCamelCase )
if conditioning_emb is not None:
_SCREAMING_SNAKE_CASE : Any = self.FiLMLayer(__lowerCamelCase , __lowerCamelCase )
# Self-attention block
_SCREAMING_SNAKE_CASE : Optional[int] = self.attention(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = hidden_states + self.dropout(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[str]:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[Any] = Attention(query_dim=__lowerCamelCase , heads=__lowerCamelCase , dim_head=__lowerCamelCase , out_bias=__lowerCamelCase , scale_qk=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = TaLayerNorm(__lowerCamelCase , eps=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Tuple = self.layer_norm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.attention(
__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , attention_mask=attention_mask.squeeze(1 ) , )
_SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_states + self.dropout(__lowerCamelCase )
return layer_output
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Tuple = TaDenseGatedActDense(d_model=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = TaFiLMLayer(in_features=d_model * 4 , out_features=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = TaLayerNorm(__lowerCamelCase , eps=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = self.layer_norm(__lowerCamelCase )
if conditioning_emb is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.film(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = self.DenseReluDense(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = hidden_states + self.dropout(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = NewGELUActivation()
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Any:
_SCREAMING_SNAKE_CASE : Dict = self.act(self.wi_a(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : Dict = self.wi_a(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = hidden_gelu * hidden_linear
_SCREAMING_SNAKE_CASE : Optional[int] = self.dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = self.wo(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase=1E-6 ) -> int:
super().__init__()
_SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.ones(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : str = eps
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Optional[Any]:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_SCREAMING_SNAKE_CASE : Tuple = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_SCREAMING_SNAKE_CASE : str = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def UpperCamelCase_ ( self , __lowerCamelCase ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase , 3.0 )) ))
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Any = nn.Linear(__lowerCamelCase , out_features * 2 , bias=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE : List[Any] = self.scale_bias(__lowerCamelCase )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = torch.chunk(__lowerCamelCase , 2 , -1 )
_SCREAMING_SNAKE_CASE : Optional[int] = x * (1 + scale) + shift
return x
| 325
| 0
|
"""simple docstring"""
import enum
import shutil
import sys
__a , __a = shutil.get_terminal_size()
__a = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class lowerCamelCase ( enum.Enum ):
'''simple docstring'''
_A : Dict = 0
_A : str = 1
def A_ ( _lowercase, _lowercase="" ):
'''simple docstring'''
sys.stdout.write(str(_lowercase ) + end )
sys.stdout.flush()
def A_ ( _lowercase, _lowercase, _lowercase="" ):
'''simple docstring'''
forceWrite(f"""\u001b[{color}m{content}\u001b[0m""", _lowercase )
def A_ ( ):
'''simple docstring'''
forceWrite("""\r""" )
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def A_ ( ):
'''simple docstring'''
forceWrite(""" """ * TERMINAL_WIDTH )
reset_cursor()
def A_ ( ):
'''simple docstring'''
reset_cursor()
forceWrite("""-""" * TERMINAL_WIDTH )
| 66
|
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[Any]:
snake_case_ :Optional[int] = {}
def lowerCAmelCase_ ( self: Dict , snake_case: str ) -> None:
snake_case_ :str = {}
def lowerCAmelCase_ ( self: Optional[int] , snake_case: str , snake_case: str , snake_case: float ) -> None:
if nodea not in self.connections:
self.add_node(snake_case )
if nodea not in self.connections:
self.add_node(snake_case )
snake_case_ :Dict = probability
def lowerCAmelCase_ ( self: List[Any] ) -> list[str]:
return list(self.connections )
def lowerCAmelCase_ ( self: Any , snake_case: str ) -> str:
snake_case_ :Optional[Any] = 0
snake_case_ :List[str] = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :List[str] = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(_lowercase, _lowercase, _lowercase )
snake_case_ :int = Counter(graph.get_nodes() )
snake_case_ :Optional[Any] = start
for _ in range(_lowercase ):
snake_case_ :Tuple = graph.transition(_lowercase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 66
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
A__ : List[str] =["""pixel_values"""]
def __init__( self : Optional[Any] , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Union[int, float] = 1 / 255 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : bool = True , **UpperCAmelCase_ : List[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = size if size is not None else {'height': 384, 'width': 384}
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = resample
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE__ = do_convert_rgb
def A_ ( self : Any , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Dict[str, int] , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Any , ):
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
SCREAMING_SNAKE_CASE__ = (size['height'], size['width'])
return resize(UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Tuple , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Union[int, float] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Tuple , ):
return rescale(UpperCAmelCase_ , scale=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : List[Any] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : str , ):
return normalize(UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Optional[int] , UpperCAmelCase_ : ImageInput , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[Dict[str, int]] = None , UpperCAmelCase_ : PILImageResampling = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase_ : str , ):
SCREAMING_SNAKE_CASE__ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE__ = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
SCREAMING_SNAKE_CASE__ = [convert_to_rgb(UpperCAmelCase_ ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ = [self.resize(image=UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ = [self.rescale(image=UpperCAmelCase_ , scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE__ = [self.normalize(image=UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ ) for image in images]
SCREAMING_SNAKE_CASE__ = [to_channel_dimension_format(UpperCAmelCase_ , UpperCAmelCase_ ) for image in images]
SCREAMING_SNAKE_CASE__ = BatchFeature(data={'pixel_values': images} , tensor_type=UpperCAmelCase_ )
return encoded_outputs
| 367
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowercase__ ( _UpperCAmelCase , unittest.TestCase ):
A__ : Tuple =FlaxAutoencoderKL
@property
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 3
SCREAMING_SNAKE_CASE__ = (32, 32)
SCREAMING_SNAKE_CASE__ = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ = jax.random.uniform(UpperCAmelCase_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = {
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
SCREAMING_SNAKE_CASE__ = self.dummy_input
return init_dict, inputs_dict
| 169
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any], UpperCAmelCase__ : Dict, UpperCAmelCase__ : int=1_3, UpperCAmelCase__ : int=7, UpperCAmelCase__ : Optional[Any]=6, UpperCAmelCase__ : List[str]=1_7, UpperCAmelCase__ : List[str]=2_3, UpperCAmelCase__ : Any=1_1, UpperCAmelCase__ : Dict=True, ):
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = act_dim
__lowercase = state_dim
__lowercase = hidden_size
__lowercase = max_length
__lowercase = is_training
def _lowercase ( self : Any ):
__lowercase = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
__lowercase = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
__lowercase = floats_tensor((self.batch_size, self.seq_length, 1) )
__lowercase = floats_tensor((self.batch_size, self.seq_length, 1) )
__lowercase = ids_tensor((self.batch_size, self.seq_length), vocab_size=1_0_0_0 )
__lowercase = random_attention_mask((self.batch_size, self.seq_length) )
__lowercase = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _lowercase ( self : Dict ):
return DecisionTransformerConfig(
batch_size=self.batch_size, seq_length=self.seq_length, act_dim=self.act_dim, state_dim=self.state_dim, hidden_size=self.hidden_size, max_length=self.max_length, )
def _lowercase ( self : str, UpperCAmelCase__ : Tuple, UpperCAmelCase__ : Any, UpperCAmelCase__ : int, UpperCAmelCase__ : Optional[int], UpperCAmelCase__ : Tuple, UpperCAmelCase__ : Union[str, Any], UpperCAmelCase__ : List[Any], ):
__lowercase = DecisionTransformerModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
__lowercase = model(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
self.parent.assertEqual(result.state_preds.shape, states.shape )
self.parent.assertEqual(result.action_preds.shape, actions.shape )
self.parent.assertEqual(result.return_preds.shape, returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def _lowercase ( self : int ):
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) ,(
__lowercase
) ,(
__lowercase
) ,(
__lowercase
) ,(
__lowercase
) ,(
__lowercase
) ,(
__lowercase
) ,
) = config_and_inputs
__lowercase = {
"states": states,
"actions": actions,
"rewards": rewards,
"returns_to_go": returns_to_go,
"timesteps": timesteps,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( lowercase ,lowercase ,lowercase ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = (DecisionTransformerModel,) if is_torch_available() else ()
__UpperCAmelCase : int = ()
__UpperCAmelCase : Tuple = {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__UpperCAmelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__UpperCAmelCase : str = False
__UpperCAmelCase : str = False
__UpperCAmelCase : int = False
__UpperCAmelCase : Any = False
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Any = False
__UpperCAmelCase : Any = False
__UpperCAmelCase : Optional[int] = False
def _lowercase ( self : Dict ):
__lowercase = DecisionTransformerModelTester(self )
__lowercase = ConfigTester(self, config_class=UpperCAmelCase__, hidden_size=3_7 )
def _lowercase ( self : str ):
self.config_tester.run_common_tests()
def _lowercase ( self : int ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def _lowercase ( self : int ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DecisionTransformerModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : Any ):
__lowercase ,__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(UpperCAmelCase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = [
"states",
"actions",
"rewards",
"returns_to_go",
"timesteps",
"attention_mask",
]
self.assertListEqual(arg_names[: len(UpperCAmelCase__ )], UpperCAmelCase__ )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowercase ( self : Union[str, Any] ):
__lowercase = 2 # number of steps of autoregressive prediction we will perform
__lowercase = 1_0 # defined by the RL environment, may be normalized
__lowercase = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
__lowercase = model.to(UpperCAmelCase__ )
__lowercase = model.config
torch.manual_seed(0 )
__lowercase = torch.randn(1, 1, config.state_dim ).to(device=UpperCAmelCase__, dtype=torch.floataa ) # env.reset()
__lowercase = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]], device=UpperCAmelCase__ )
__lowercase = torch.tensor(UpperCAmelCase__, device=UpperCAmelCase__, dtype=torch.floataa ).reshape(1, 1, 1 )
__lowercase = state
__lowercase = torch.zeros(1, 0, config.act_dim, device=UpperCAmelCase__, dtype=torch.floataa )
__lowercase = torch.zeros(1, 0, device=UpperCAmelCase__, dtype=torch.floataa )
__lowercase = torch.tensor(0, device=UpperCAmelCase__, dtype=torch.long ).reshape(1, 1 )
for step in range(UpperCAmelCase__ ):
__lowercase = torch.cat([actions, torch.zeros(1, 1, config.act_dim, device=UpperCAmelCase__ )], dim=1 )
__lowercase = torch.cat([rewards, torch.zeros(1, 1, device=UpperCAmelCase__ )], dim=1 )
__lowercase = torch.ones(1, states.shape[1] ).to(dtype=torch.long, device=states.device )
with torch.no_grad():
__lowercase ,__lowercase ,__lowercase = model(
states=UpperCAmelCase__, actions=UpperCAmelCase__, rewards=UpperCAmelCase__, returns_to_go=UpperCAmelCase__, timesteps=UpperCAmelCase__, attention_mask=UpperCAmelCase__, return_dict=UpperCAmelCase__, )
self.assertEqual(action_pred.shape, actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1], expected_outputs[step], atol=1E-4 ) )
__lowercase ,__lowercase ,__lowercase ,__lowercase = ( # env.step(action)
torch.randn(1, 1, config.state_dim ).to(device=UpperCAmelCase__, dtype=torch.floataa ),
1.0,
False,
{},
)
__lowercase = action_pred[0, -1]
__lowercase = torch.cat([states, state], dim=1 )
__lowercase = returns_to_go[0, -1] - reward
__lowercase = torch.cat([returns_to_go, pred_return.reshape(1, 1, 1 )], dim=1 )
__lowercase = torch.cat(
[timesteps, torch.ones((1, 1), device=UpperCAmelCase__, dtype=torch.long ) * (step + 1)], dim=1 )
| 17
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_A : List[str] =logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_A : Tuple =[]
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
]
)
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
lowerCamelCase__ : List[Any] = state_dict.pop(UpperCamelCase )
lowerCamelCase__ : Any = val
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Dict:
lowerCamelCase__ : Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowerCamelCase__ : List[str] = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
lowerCamelCase__ : Optional[int] = value
else:
lowerCamelCase__ : Any = value
return new_state_dict
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase=False ) -> Dict:
lowerCamelCase__ : Optional[int] = """"""
if is_panoptic:
lowerCamelCase__ : Dict = """conditional_detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowerCamelCase__ : List[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowerCamelCase__ : Union[str, Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ : int = in_proj_weight[:256, :]
lowerCamelCase__ : Any = in_proj_bias[:256]
lowerCamelCase__ : str = in_proj_weight[256:512, :]
lowerCamelCase__ : Optional[int] = in_proj_bias[256:512]
lowerCamelCase__ : Dict = in_proj_weight[-256:, :]
lowerCamelCase__ : str = in_proj_bias[-256:]
def SCREAMING_SNAKE_CASE_ () -> List[Any]:
lowerCamelCase__ : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ : Optional[Any] = Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> int:
lowerCamelCase__ : Optional[Any] = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
lowerCamelCase__ : Any = """resnet101"""
if "dc5" in model_name:
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : int = """panoptic""" in model_name
if is_panoptic:
lowerCamelCase__ : List[str] = 250
else:
lowerCamelCase__ : int = 91
lowerCamelCase__ : int = """huggingface/label-files"""
lowerCamelCase__ : List[str] = """coco-detection-id2label.json"""
lowerCamelCase__ : Optional[Any] = json.load(open(hf_hub_download(UpperCamelCase , UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
lowerCamelCase__ : Any = {int(UpperCamelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ : str = idalabel
lowerCamelCase__ : List[str] = {v: k for k, v in idalabel.items()}
# load image processor
lowerCamelCase__ : Optional[int] = """coco_panoptic""" if is_panoptic else """coco_detection"""
lowerCamelCase__ : int = ConditionalDetrImageProcessor(format=UpperCamelCase )
# prepare image
lowerCamelCase__ : List[str] = prepare_img()
lowerCamelCase__ : int = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
lowerCamelCase__ : Optional[Any] = encoding["""pixel_values"""]
logger.info(f'''Converting model {model_name}...''' )
# load original model from torch hub
lowerCamelCase__ : List[Any] = torch.hub.load("""DeppMeng/ConditionalDETR""" , UpperCamelCase , pretrained=UpperCamelCase ).eval()
lowerCamelCase__ : Dict = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
lowerCamelCase__ : Optional[Any] = """conditional_detr.""" + src
rename_key(UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Dict = rename_backbone_keys(UpperCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCamelCase , is_panoptic=UpperCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowerCamelCase__ : Dict = """conditional_detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
lowerCamelCase__ : int = state_dict.pop(UpperCamelCase )
lowerCamelCase__ : Union[str, Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowerCamelCase__ : List[str] = state_dict.pop(UpperCamelCase )
lowerCamelCase__ : Any = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
lowerCamelCase__ : int = state_dict.pop(UpperCamelCase )
lowerCamelCase__ : Tuple = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowerCamelCase__ : Union[str, Any] = state_dict.pop(UpperCamelCase )
lowerCamelCase__ : Dict = val
# finally, create HuggingFace model and load state dict
lowerCamelCase__ : Tuple = ConditionalDetrForSegmentation(UpperCamelCase ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase )
model.load_state_dict(UpperCamelCase )
model.eval()
model.push_to_hub(repo_id=UpperCamelCase , organization="""DepuMeng""" , commit_message="""Add model""" )
# verify our conversion
lowerCamelCase__ : Optional[Any] = conditional_detr(UpperCamelCase )
lowerCamelCase__ : Optional[Any] = model(UpperCamelCase )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1E-4 )
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
model.save_pretrained(UpperCamelCase )
image_processor.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
_A : List[Any] =argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''conditional_detr_resnet50''',
type=str,
help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
_A : Optional[Any] =parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 41
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''squeezebert/squeezebert-uncased''': 5_12,
'''squeezebert/squeezebert-mnli''': 5_12,
'''squeezebert/squeezebert-mnli-headless''': 5_12,
}
__snake_case = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Tuple = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Dict = SqueezeBertTokenizer
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=True , snake_case__="[UNK]" , snake_case__="[SEP]" , snake_case__="[PAD]" , snake_case__="[CLS]" , snake_case__="[MASK]" , snake_case__=True , snake_case__=None , **snake_case__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , do_lower_case=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , tokenize_chinese_chars=snake_case__ , strip_accents=snake_case__ , **snake_case__ , )
UpperCAmelCase : List[Any] =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , snake_case__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , snake_case__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , snake_case__ ) != tokenize_chinese_chars
):
UpperCAmelCase : int =getattr(snake_case__ , normalizer_state.pop('''type''' ) )
UpperCAmelCase : int =do_lower_case
UpperCAmelCase : Tuple =strip_accents
UpperCAmelCase : Any =tokenize_chinese_chars
UpperCAmelCase : Union[str, Any] =normalizer_class(**snake_case__ )
UpperCAmelCase : Optional[int] =do_lower_case
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=None ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Dict =[self.sep_token_id]
UpperCAmelCase : Any =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 360
|
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : Dict = MODEL_FOR_MASKED_LM_MAPPING
__lowerCamelCase : Optional[int] = TF_MODEL_FOR_MASKED_LM_MAPPING
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] =pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''tf''' )
UpperCAmelCase : List[Any] =unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 3_8015, '''token_str''': ''' grouped'''},
{'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 2_5506, '''token_str''': ''' accuser'''},
] , )
UpperCAmelCase : Tuple =unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is grouped''',
'''score''': 2.1e-05,
'''token''': 3_8015,
'''token_str''': ''' grouped''',
},
{
'''sequence''': '''The largest city in France is accuser''',
'''score''': 2.1e-05,
'''token''': 2_5506,
'''token_str''': ''' accuser''',
},
] , )
UpperCAmelCase : Dict =unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3606, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 2941, '''token_str''': ''' Te'''},
] , )
@require_torch
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Any =pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''pt''' )
UpperCAmelCase : List[Any] =unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 3_5676, '''token_str''': ''' Maul'''},
{'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS'''},
] , )
UpperCAmelCase : Union[str, Any] =unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is Maul''',
'''score''': 2.2e-05,
'''token''': 3_5676,
'''token_str''': ''' Maul''',
},
{'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS'''},
] , )
UpperCAmelCase : int =unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 2941, '''token_str''': ''' Te'''},
{'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3606, '''token_str''': ''' Clara'''},
] , )
UpperCAmelCase : List[str] =unmasker('''My name is <mask> <mask>''' , top_k=2 )
self.assertEqual(
nested_simplify(snake_case__ , decimals=6 ) , [
[
{
'''score''': 2.2e-05,
'''token''': 3_5676,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is Maul<mask></s>''',
},
{'''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
],
[
{
'''score''': 2.2e-05,
'''token''': 3_5676,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is<mask> Maul</s>''',
},
{'''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
],
] , )
@require_torch_gpu
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =pipeline('''fill-mask''' , model='''hf-internal-testing/tiny-random-distilbert''' , device=0 , framework='''pt''' )
# convert model to fp16
pipe.model.half()
UpperCAmelCase : Dict =pipe('''Paris is the [MASK] of France.''' )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(snake_case__ , snake_case__ )
@slow
@require_torch
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : str =pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''pt''' )
self.run_large_test(snake_case__ )
@slow
@require_tf
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''tf''' )
self.run_large_test(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ ) , [
{'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 610, '''token_str''': ''' John'''},
{'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 1573, '''token_str''': ''' Chris'''},
] , )
UpperCAmelCase : int =unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(snake_case__ ) , [
{
'''sequence''': '''The largest city in France is Paris''',
'''score''': 0.251,
'''token''': 2201,
'''token_str''': ''' Paris''',
},
{
'''sequence''': '''The largest city in France is Lyon''',
'''score''': 0.214,
'''token''': 1_2790,
'''token_str''': ''' Lyon''',
},
] , )
UpperCAmelCase : int =unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(snake_case__ ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 3499, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_3606, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 2941, '''token_str''': ''' Te'''},
] , )
@require_torch
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Any =pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''pt''' )
UpperCAmelCase : List[str] =None
UpperCAmelCase : str =None
self.run_pipeline_test(snake_case__ , [] )
@require_tf
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''tf''' )
UpperCAmelCase : str =None
UpperCAmelCase : Dict =None
self.run_pipeline_test(snake_case__ , [] )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Any:
'''simple docstring'''
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''' )
UpperCAmelCase : Any =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
UpperCAmelCase : int =[
f'''This is another {tokenizer.mask_token} test''',
]
return fill_masker, examples
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Any =fill_masker.tokenizer
UpperCAmelCase : Optional[int] =fill_masker.model
UpperCAmelCase : Dict =fill_masker(
f'''This is a {tokenizer.mask_token}''' , )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
UpperCAmelCase : int =fill_masker([f'''This is a {tokenizer.mask_token}'''] )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
UpperCAmelCase : Optional[Any] =fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.'''] )
self.assertEqual(
snake_case__ , [
[
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
],
[
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
],
] , )
with self.assertRaises(snake_case__ ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(snake_case__ ):
fill_masker('''This is''' )
self.run_test_top_k(snake_case__ , snake_case__ )
self.run_test_targets(snake_case__ , snake_case__ )
self.run_test_top_k_targets(snake_case__ , snake_case__ )
self.fill_mask_with_duplicate_targets_and_top_k(snake_case__ , snake_case__ )
self.fill_mask_with_multiple_masks(snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] =tokenizer.get_vocab()
UpperCAmelCase : List[str] =sorted(vocab.keys() )[:2]
# Pipeline argument
UpperCAmelCase : Any =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ , targets=snake_case__ )
UpperCAmelCase : Tuple =fill_masker(f'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
UpperCAmelCase : int ={vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , snake_case__ )
UpperCAmelCase : Optional[Any] =[tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(snake_case__ ) )
# Call argument
UpperCAmelCase : Union[str, Any] =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
UpperCAmelCase : Optional[int] =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=snake_case__ )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
UpperCAmelCase : Any ={vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , snake_case__ )
UpperCAmelCase : Optional[int] =[tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(snake_case__ ) )
# Score equivalence
UpperCAmelCase : Any =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=snake_case__ )
UpperCAmelCase : Union[str, Any] =[top_mask['''token_str'''] for top_mask in outputs]
UpperCAmelCase : Optional[int] =[top_mask['''score'''] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(snake_case__ ) == set(snake_case__ ):
UpperCAmelCase : List[Any] =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=snake_case__ )
UpperCAmelCase : List[str] =[top_mask['''score'''] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(snake_case__ ) , nested_simplify(snake_case__ ) )
# Raises with invalid
with self.assertRaises(snake_case__ ):
UpperCAmelCase : int =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(snake_case__ ):
UpperCAmelCase : int =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=[''''''] )
with self.assertRaises(snake_case__ ):
UpperCAmelCase : Any =fill_masker(f'''This is a {tokenizer.mask_token}''' , targets='''''' )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ , top_k=2 )
UpperCAmelCase : Optional[Any] =fill_masker(f'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
UpperCAmelCase : Any =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
UpperCAmelCase : List[Any] =fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
] , )
self.assertEqual(nested_simplify(snake_case__ ) , nested_simplify(snake_case__ ) )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str =tokenizer.get_vocab()
UpperCAmelCase : int =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
# top_k=2, ntargets=3
UpperCAmelCase : Optional[Any] =sorted(vocab.keys() )[:3]
UpperCAmelCase : str =fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=2 , targets=snake_case__ )
# If we use the most probably targets, and filter differently, we should still
# have the same results
UpperCAmelCase : Tuple =[el['''token_str'''] for el in sorted(snake_case__ , key=lambda snake_case__ : x["score"] , reverse=snake_case__ )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(snake_case__ ).issubset(snake_case__ ):
UpperCAmelCase : str =fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=3 , targets=snake_case__ )
# They should yield exactly the same result
self.assertEqual(nested_simplify(snake_case__ ) , nested_simplify(snake_case__ ) )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : str =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
UpperCAmelCase : Union[str, Any] =tokenizer.get_vocab()
# String duplicates + id duplicates
UpperCAmelCase : List[Any] =sorted(vocab.keys() )[:3]
UpperCAmelCase : Optional[int] =[targets[0], targets[1], targets[0], targets[2], targets[1]]
UpperCAmelCase : str =fill_masker(f'''My name is {tokenizer.mask_token}''' , targets=snake_case__ , top_k=10 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(snake_case__ ) , 3 )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =FillMaskPipeline(model=snake_case__ , tokenizer=snake_case__ )
UpperCAmelCase : Union[str, Any] =fill_masker(
f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
snake_case__ , [
[
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
],
[
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
],
[
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
{'''sequence''': ANY(snake_case__ ), '''score''': ANY(snake_case__ ), '''token''': ANY(snake_case__ ), '''token_str''': ANY(snake_case__ )},
],
] , )
| 78
| 0
|
"""simple docstring"""
import os
from pathlib import Path
def _snake_case ( ):
from torch.utils.cpp_extension import load
A__ = Path(UpperCAmelCase_ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
A__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , UpperCAmelCase_ , with_cuda=UpperCAmelCase_ , extra_include_paths=[str(UpperCAmelCase_ )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 335
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Any = [
'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
SCREAMING_SNAKE_CASE_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 335
| 1
|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=1_0_2_4 , lowerCamelCase__=1_0_2_4 , lowerCamelCase__=False , **lowerCamelCase__ ):
lowerCamelCase_ = AutoTokenizer.from_pretrained(lowerCamelCase__ )
lowerCamelCase_ = SeqaSeqDataset(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , type_path="train" , **lowerCamelCase__ )
lowerCamelCase_ = tok.pad_token_id
def get_lens(lowerCamelCase__ ):
lowerCamelCase_ = tqdm(
DataLoader(lowerCamelCase__ , batch_size=5_1_2 , num_workers=8 , shuffle=lowerCamelCase__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
lowerCamelCase_ = []
for batch in dl:
lowerCamelCase_ = batch["input_ids"].ne(lowerCamelCase__ ).sum(1 ).tolist()
lowerCamelCase_ = batch["labels"].ne(lowerCamelCase__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(lowerCamelCase__ , lowerCamelCase__ ):
max_lens.append(max(lowerCamelCase__ , lowerCamelCase__ ) )
else:
max_lens.extend(lowerCamelCase__ )
return max_lens
lowerCamelCase_ = get_lens(lowerCamelCase__ )
lowerCamelCase_ = SeqaSeqDataset(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , type_path="val" , **lowerCamelCase__ )
lowerCamelCase_ = get_lens(lowerCamelCase__ )
pickle_save(lowerCamelCase__ , train_ds.len_file )
pickle_save(lowerCamelCase__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 47
|
__A ='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__A =[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__A ={
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 47
| 1
|
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