code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def a__ ( a : float , a : float , a : float , ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
raise ValueError("Stress cannot be negative" )
elif tangential_force < 0:
raise ValueError("Tangential Force cannot be negative" )
elif area < 0:
raise ValueError("Area cannot be negative" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : str = DiTPipeline
__lowercase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__lowercase : Any = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
__lowercase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Union[str, Any] = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case_ , activation_fn="gelu-approximate" , num_embeds_ada_norm=10_00 , norm_type="ada_norm_zero" , norm_elementwise_affine=snake_case_ , )
_snake_case : int = AutoencoderKL()
_snake_case : List[str] = DDIMScheduler()
_snake_case : Dict = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : Dict = torch.manual_seed(snake_case_ )
else:
_snake_case : List[Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : List[Any] = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : Any = "cpu"
_snake_case : str = self.get_dummy_components()
_snake_case : Union[str, Any] = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Tuple = self.get_dummy_inputs(snake_case_ )
_snake_case : List[str] = pipe(**snake_case_ ).images
_snake_case : Optional[int] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_snake_case : List[str] = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_snake_case : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1E-3 )
def lowerCamelCase__ ( self ):
self._test_inference_batch_single_identical(relax_max_difference=snake_case_ , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_snake_case : List[str] = torch.manual_seed(0 )
_snake_case : Dict = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
_snake_case : Union[str, Any] = ["vase", "umbrella", "white shark", "white wolf"]
_snake_case : Optional[Any] = pipe.get_label_ids(snake_case_ )
_snake_case : Tuple = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(snake_case_ , snake_case_ ):
_snake_case : str = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase__ ( self ):
_snake_case : int = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
_snake_case : Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
_snake_case : str = ["vase", "umbrella"]
_snake_case : Any = pipe.get_label_ids(snake_case_ )
_snake_case : List[str] = torch.manual_seed(0 )
_snake_case : Tuple = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(snake_case_ , snake_case_ ):
_snake_case : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_a : int = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _UpperCAmelCase ( _snake_case):
@staticmethod
def lowerCamelCase__ ( snake_case_ ):
_snake_case : Dict = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=snake_case_ , required=snake_case_ , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=snake_case_ , required=snake_case_ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=snake_case_ , required=snake_case_ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=snake_case_ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=snake_case_ , default=snake_case_ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : str = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
_snake_case : Optional[int] = model_type
_snake_case : Any = tf_checkpoint
_snake_case : Optional[int] = pytorch_dump_output
_snake_case : Tuple = config
_snake_case : Tuple = finetuning_task_name
def lowerCamelCase__ ( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case : int = self._tf_checkpoint
_snake_case : Optional[Any] = ""
else:
_snake_case : Optional[int] = self._tf_checkpoint
_snake_case : List[str] = ""
convert_transfo_xl_checkpoint_to_pytorch(
snake_case_ , self._config , self._pytorch_dump_output , snake_case_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 87 | 1 |
"""simple docstring"""
def a__ ( a : list ):
"""simple docstring"""
_snake_case : Optional[Any] = len(a )
for _ in range(a ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_snake_case , _snake_case : Dict = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
_a : Union[str, Any] = list(range(10, 0, -1))
print(f'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 87 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : str = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = [
"""SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Swinv2ForImageClassification""",
"""Swinv2ForMaskedImageModeling""",
"""Swinv2Model""",
"""Swinv2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
_a : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 | 1 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0" )
_snake_case : Union[str, Any] = img
_snake_case : List[str] = img.shape[1]
_snake_case : List[str] = img.shape[0]
_snake_case : str = dst_width
_snake_case : Optional[int] = dst_height
_snake_case : Optional[Any] = self.src_w / self.dst_w
_snake_case : Any = self.src_h / self.dst_h
_snake_case : List[str] = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def lowerCamelCase__ ( self ):
for i in range(self.dst_h ):
for j in range(self.dst_w ):
_snake_case : List[str] = self.img[self.get_y(snake_case_ )][self.get_x(snake_case_ )]
def lowerCamelCase__ ( self , snake_case_ ):
return int(self.ratio_x * x )
def lowerCamelCase__ ( self , snake_case_ ):
return int(self.ratio_y * y )
if __name__ == "__main__":
_a, _a : Dict = 800, 600
_a : Any = imread("""image_data/lena.jpg""", 1)
_a : Dict = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _UpperCAmelCase ( _snake_case):
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
class _UpperCAmelCase ( nn.Module):
__lowercase : int
__lowercase : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
__lowercase : jnp.dtype = jnp.floataa
def lowerCamelCase__ ( self ):
_snake_case : Any = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_snake_case : Any = []
for i in range(len(self.block_out_channels ) - 1 ):
_snake_case : Dict = self.block_out_channels[i]
_snake_case : int = self.block_out_channels[i + 1]
_snake_case : str = nn.Conv(
snake_case_ , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case_ )
_snake_case : Any = nn.Conv(
snake_case_ , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case_ )
_snake_case : Any = blocks
_snake_case : Dict = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case_ ):
_snake_case : List[Any] = self.conv_in(snake_case_ )
_snake_case : List[Any] = nn.silu(snake_case_ )
for block in self.blocks:
_snake_case : Tuple = block(snake_case_ )
_snake_case : str = nn.silu(snake_case_ )
_snake_case : Dict = self.conv_out(snake_case_ )
return embedding
@flax_register_to_config
class _UpperCAmelCase ( nn.Module , _snake_case , _snake_case):
__lowercase : int = 3_2
__lowercase : int = 4
__lowercase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
__lowercase : Union[bool, Tuple[bool]] = False
__lowercase : Tuple[int] = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
__lowercase : int = 2
__lowercase : Union[int, Tuple[int]] = 8
__lowercase : Optional[Union[int, Tuple[int]]] = None
__lowercase : int = 1_2_8_0
__lowercase : float = 0.0
__lowercase : bool = False
__lowercase : jnp.dtype = jnp.floataa
__lowercase : bool = True
__lowercase : int = 0
__lowercase : str = "rgb"
__lowercase : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
def lowerCamelCase__ ( self , snake_case_ ):
# init input tensors
_snake_case : int = (1, self.in_channels, self.sample_size, self.sample_size)
_snake_case : Dict = jnp.zeros(snake_case_ , dtype=jnp.floataa )
_snake_case : Any = jnp.ones((1,) , dtype=jnp.intaa )
_snake_case : Any = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_snake_case : int = (1, 3, self.sample_size * 8, self.sample_size * 8)
_snake_case : Dict = jnp.zeros(snake_case_ , dtype=jnp.floataa )
_snake_case , _snake_case : List[Any] = jax.random.split(snake_case_ )
_snake_case : Any = {"params": params_rng, "dropout": dropout_rng}
return self.init(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )["params"]
def lowerCamelCase__ ( self ):
_snake_case : int = self.block_out_channels
_snake_case : Tuple = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_snake_case : int = self.num_attention_heads or self.attention_head_dim
# input
_snake_case : Optional[Any] = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_snake_case : Tuple = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_snake_case : Tuple = FlaxTimestepEmbedding(snake_case_ , dtype=self.dtype )
_snake_case : Any = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
_snake_case : int = self.only_cross_attention
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Optional[int] = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Optional[int] = (num_attention_heads,) * len(self.down_block_types )
# down
_snake_case : Optional[Any] = []
_snake_case : Optional[int] = []
_snake_case : List[Any] = block_out_channels[0]
_snake_case : Union[str, Any] = nn.Conv(
snake_case_ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case_ )
for i, down_block_type in enumerate(self.down_block_types ):
_snake_case : Optional[Any] = output_channel
_snake_case : Dict = block_out_channels[i]
_snake_case : int = i == len(snake_case_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_snake_case : Tuple = FlaxCrossAttnDownBlockaD(
in_channels=snake_case_ , out_channels=snake_case_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
_snake_case : Optional[Any] = FlaxDownBlockaD(
in_channels=snake_case_ , out_channels=snake_case_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case_ )
for _ in range(self.layers_per_block ):
_snake_case : Tuple = nn.Conv(
snake_case_ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case_ )
if not is_final_block:
_snake_case : Dict = nn.Conv(
snake_case_ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case_ )
_snake_case : int = down_blocks
_snake_case : Dict = controlnet_down_blocks
# mid
_snake_case : Union[str, Any] = block_out_channels[-1]
_snake_case : int = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case_ , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
_snake_case : Union[str, Any] = nn.Conv(
snake_case_ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = 1.0 , snake_case_ = True , snake_case_ = False , ):
_snake_case : Union[str, Any] = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_snake_case : List[str] = jnp.flip(snake_case_ , axis=1 )
# 1. time
if not isinstance(snake_case_ , jnp.ndarray ):
_snake_case : int = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
_snake_case : Optional[int] = timesteps.astype(dtype=jnp.floataa )
_snake_case : Union[str, Any] = jnp.expand_dims(snake_case_ , 0 )
_snake_case : Any = self.time_proj(snake_case_ )
_snake_case : Optional[int] = self.time_embedding(snake_case_ )
# 2. pre-process
_snake_case : Union[str, Any] = jnp.transpose(snake_case_ , (0, 2, 3, 1) )
_snake_case : str = self.conv_in(snake_case_ )
_snake_case : str = jnp.transpose(snake_case_ , (0, 2, 3, 1) )
_snake_case : Dict = self.controlnet_cond_embedding(snake_case_ )
sample += controlnet_cond
# 3. down
_snake_case : Any = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case_ , snake_case_ ):
_snake_case , _snake_case : List[Any] = down_block(snake_case_ , snake_case_ , snake_case_ , deterministic=not train )
else:
_snake_case , _snake_case : Dict = down_block(snake_case_ , snake_case_ , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
_snake_case : Any = self.mid_block(snake_case_ , snake_case_ , snake_case_ , deterministic=not train )
# 5. contronet blocks
_snake_case : Tuple = ()
for down_block_res_sample, controlnet_block in zip(snake_case_ , self.controlnet_down_blocks ):
_snake_case : Dict = controlnet_block(snake_case_ )
controlnet_down_block_res_samples += (down_block_res_sample,)
_snake_case : List[str] = controlnet_down_block_res_samples
_snake_case : Tuple = self.controlnet_mid_block(snake_case_ )
# 6. scaling
_snake_case : int = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case_ , mid_block_res_sample=snake_case_ )
| 87 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import math
def a__ ( a : int = 100 ):
"""simple docstring"""
_snake_case : Dict = sum(i * i for i in range(1 , n + 1 ) )
_snake_case : str = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 | 1 |
"""simple docstring"""
def a__ ( a : int=28_123 ):
"""simple docstring"""
_snake_case : int = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
_snake_case : Union[str, Any] = set()
_snake_case : str = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(a )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 87 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=3 , snake_case_=32 , snake_case_=3 , snake_case_=10 , snake_case_=[10, 20, 30, 40] , snake_case_=[1, 1, 2, 1] , snake_case_=True , snake_case_=True , snake_case_="relu" , snake_case_=3 , snake_case_=None , ):
_snake_case : List[Any] = parent
_snake_case : Optional[Any] = batch_size
_snake_case : Optional[Any] = image_size
_snake_case : str = num_channels
_snake_case : str = embeddings_size
_snake_case : str = hidden_sizes
_snake_case : int = depths
_snake_case : Optional[int] = is_training
_snake_case : Optional[Any] = use_labels
_snake_case : List[Any] = hidden_act
_snake_case : Union[str, Any] = num_labels
_snake_case : int = scope
_snake_case : List[Any] = len(snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : List[Any] = None
if self.use_labels:
_snake_case : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_snake_case : Dict = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = RegNetModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : str = model(snake_case_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_labels
_snake_case : List[str] = RegNetForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : List[Any] = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case : int = config_and_inputs
_snake_case : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
__lowercase : Dict = (
{"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
if is_torch_available()
else {}
)
__lowercase : Optional[Any] = False
__lowercase : Dict = False
__lowercase : List[Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = RegNetModelTester(self )
_snake_case : int = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowerCamelCase__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase__ ( self ):
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Any = model_class(snake_case_ )
_snake_case : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : List[str] = [*signature.parameters.keys()]
_snake_case : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_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 : Union[str, Any] = model_class(config=snake_case_ )
for name, module in model.named_modules():
if isinstance(snake_case_ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[str] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_snake_case : Tuple = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_snake_case : str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(snake_case_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_snake_case , _snake_case : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case : str = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_snake_case : str = layer_type
_snake_case : List[str] = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case : str = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Tuple = RegNetModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def a__ ( ):
"""simple docstring"""
_snake_case : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _UpperCAmelCase ( unittest.TestCase):
@cached_property
def lowerCamelCase__ ( self ):
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_snake_case : Tuple = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case_ )
_snake_case : Dict = self.default_image_processor
_snake_case : List[Any] = prepare_img()
_snake_case : Tuple = image_processor(images=snake_case_ , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_snake_case : int = model(**snake_case_ )
# verify the logits
_snake_case : List[str] = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_snake_case : Optional[int] = torch.tensor([-0.4180, -1.5051, -3.4836] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
class _UpperCAmelCase : # Public class to implement a graph
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[str] = row
_snake_case : Dict = col
_snake_case : Optional[Any] = graph
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
# Checking all 8 elements surrounding nth element
_snake_case : Tuple = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
_snake_case : str = [-1, 0, 1, -1, 1, -1, 0, 1]
_snake_case : Union[str, Any] = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , snake_case_ ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , snake_case_ )
def lowerCamelCase__ ( self ): # And finally, count all islands.
_snake_case : Union[str, Any] = [[False for j in range(self.COL )] for i in range(self.ROW )]
_snake_case : int = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(snake_case_ , snake_case_ , snake_case_ )
count += 1
return count
| 87 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_a : Optional[Any] = logging.get_logger(__name__)
_a : str = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
_a : List[str] = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def a__ ( a : Any , a : Optional[int] , a : Any , a : Dict , a : List[str] , a : Any ):
"""simple docstring"""
for attribute in key.split("." ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_snake_case : Dict = "lm_head"
_snake_case : Union[str, Any] = getattr(a , a )
if weight_type is not None:
_snake_case : List[str] = getattr(a , a ).shape
else:
_snake_case : Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
_snake_case : Any = value
elif weight_type == "weight_g":
_snake_case : List[Any] = value
elif weight_type == "weight_v":
_snake_case : List[Any] = value
elif weight_type == "bias":
_snake_case : Tuple = value
else:
_snake_case : str = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def a__ ( a : str , a : Optional[int] , a : Any ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Dict = fairseq_model.state_dict()
_snake_case : Dict = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_snake_case : str = False
if "conv_layers" in name:
load_conv_layer(
a , a , a , a , hf_model.config.feat_extract_norm == "group" , )
_snake_case : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
_snake_case : Union[str, Any] = "unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_snake_case : Any = True
if "*" in mapped_key:
_snake_case : Optional[Any] = name.split(a )[0].split("." )[-2]
_snake_case : str = mapped_key.replace("*" , a )
if "weight_g" in name:
_snake_case : List[Any] = "weight_g"
elif "weight_v" in name:
_snake_case : str = "weight_v"
elif "bias" in name:
_snake_case : List[str] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_snake_case : List[str] = "weight"
else:
_snake_case : str = None
set_recursively(a , a , a , a , a , a )
continue
if not is_used:
unused_weights.append(a )
logger.warning(f'Unused weights: {unused_weights}' )
def a__ ( a : int , a : Any , a : int , a : str , a : str ):
"""simple docstring"""
_snake_case : Optional[int] = full_name.split("conv_layers." )[-1]
_snake_case : Dict = name.split("." )
_snake_case : Optional[Any] = int(items[0] )
_snake_case : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
_snake_case : Tuple = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
_snake_case : str = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
_snake_case : int = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
_snake_case : Any = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(a )
@torch.no_grad()
def a__ ( a : Any , a : List[str] , a : int=None , a : Union[str, Any]=None , a : str=True ):
"""simple docstring"""
if config_path is not None:
_snake_case : Optional[int] = UniSpeechConfig.from_pretrained(a )
else:
_snake_case : Optional[int] = UniSpeechConfig()
if is_finetuned:
if dict_path:
_snake_case : Any = Dictionary.load_from_json(a )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_snake_case : Optional[Any] = target_dict.pad_index
_snake_case : List[Any] = target_dict.bos_index
_snake_case : Union[str, Any] = target_dict.eos_index
_snake_case : int = len(target_dict.symbols )
_snake_case : Any = os.path.join(a , "vocab.json" )
if not os.path.isdir(a ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a ) )
return
os.makedirs(a , exist_ok=a )
_snake_case : Any = target_dict.indices
# fairseq has the <pad> and <s> switched
_snake_case : Optional[int] = 42
_snake_case : List[Any] = 43
with open(a , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(a , a )
_snake_case : List[str] = WavaVecaPhonemeCTCTokenizer(
a , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=a , )
_snake_case : Optional[Any] = True if config.feat_extract_norm == "layer" else False
_snake_case : str = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=a , return_attention_mask=a , )
_snake_case : Optional[Any] = WavaVecaProcessor(feature_extractor=a , tokenizer=a )
processor.save_pretrained(a )
_snake_case : Any = UniSpeechForCTC(a )
else:
_snake_case : List[Any] = UniSpeechForPreTraining(a )
if is_finetuned:
_snake_case , _snake_case , _snake_case : Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] ), "w2v_path": checkpoint_path} )
else:
_snake_case , _snake_case , _snake_case : Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_snake_case : Optional[int] = model[0].eval()
recursively_load_weights(a , a , a )
hf_unispeech.save_pretrained(a )
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Optional[int] = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 87 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_snake_case : List[Any] = Vector()
def lowerCamelCase__ ( self ):
_snake_case : Any = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case_ ) , "(0,0,0,0,0,1)" )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case_ ) , 4 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2] )
_snake_case : List[str] = Vector([1, 2, 3, 4, 5] )
_snake_case : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_snake_case : Any = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
_snake_case : Any = 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 lowerCamelCase__ ( self ):
_snake_case : str = Vector([1, 2, 3] )
_snake_case : Union[str, Any] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Vector([1, 2, 3] )
_snake_case : List[Any] = Vector([2, -1, 4] ) # for test of dot product
_snake_case : Union[str, Any] = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Vector([1, 2, 3] )
_snake_case : Optional[Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case_ , snake_case_ ) ) , "(3,4,7)" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Vector([1, 0, 0, 0, 0, 0] )
_snake_case : Optional[int] = x.copy()
self.assertEqual(str(snake_case_ ) , str(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case_ ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : str = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : str = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self ):
_snake_case : str = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_snake_case : List[str] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : int = 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 lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = 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 lowerCamelCase__ ( 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()
| 87 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=50 , snake_case_=0.02 , snake_case_=True , snake_case_=None , ):
_snake_case : Optional[int] = parent
_snake_case : Any = batch_size
_snake_case : Union[str, Any] = seq_length
_snake_case : List[str] = is_training
_snake_case : Any = use_input_mask
_snake_case : Optional[Any] = vocab_size
_snake_case : str = hidden_size
_snake_case : Any = num_hidden_layers
_snake_case : Optional[int] = num_attention_heads
_snake_case : Tuple = intermediate_size
_snake_case : int = hidden_act
_snake_case : int = hidden_dropout_prob
_snake_case : Any = attention_probs_dropout_prob
_snake_case : Tuple = max_position_embeddings
_snake_case : List[str] = initializer_range
_snake_case : Tuple = use_labels
_snake_case : int = scope
def lowerCamelCase__ ( self ):
_snake_case : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Any = None
if self.use_input_mask:
_snake_case : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
_snake_case : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def lowerCamelCase__ ( self ):
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
def lowerCamelCase__ ( self ):
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : int = self.prepare_config_and_inputs()
_snake_case : Union[str, Any] = True
_snake_case : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , **snake_case_ , ):
_snake_case : Tuple = BertGenerationEncoder(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : int = model(snake_case_ , attention_mask=snake_case_ )
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , **snake_case_ , ):
_snake_case : Any = True
_snake_case : int = BertGenerationEncoder(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : int = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , )
_snake_case : int = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , **snake_case_ , ):
_snake_case : Tuple = True
_snake_case : Optional[Any] = True
_snake_case : Optional[int] = BertGenerationDecoder(config=snake_case_ ).to(snake_case_ ).eval()
# first forward pass
_snake_case : Dict = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , use_cache=snake_case_ , )
_snake_case : Dict = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_snake_case : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_snake_case : List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_snake_case : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
_snake_case : List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
_snake_case : Optional[int] = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , output_hidden_states=snake_case_ , )["hidden_states"][0]
_snake_case : Tuple = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , past_key_values=snake_case_ , output_hidden_states=snake_case_ , )["hidden_states"][0]
# select random slice
_snake_case : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_snake_case : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
_snake_case : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case_ , snake_case_ , atol=1E-3 ) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : List[str] = BertGenerationDecoder(snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Dict = model(snake_case_ , attention_mask=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case , _snake_case , _snake_case : Optional[Any] = self.prepare_config_and_inputs()
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _snake_case , _snake_case , _snake_case , unittest.TestCase):
__lowercase : Any = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
__lowercase : List[Any] = (BertGenerationDecoder,) if is_torch_available() else ()
__lowercase : List[str] = (
{"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
if is_torch_available()
else {}
)
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = BertGenerationEncoderTester(self )
_snake_case : Optional[int] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case , _snake_case , _snake_case : Any = self.model_tester.prepare_config_and_inputs()
_snake_case : Optional[Any] = "bert"
self.model_tester.create_and_check_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_ )
def lowerCamelCase__ ( self ):
# This regression test was failing with PyTorch < 1.3
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
_snake_case : List[str] = None
self.model_tester.create_and_check_model_as_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
_snake_case : int = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(snake_case_ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : str = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
_snake_case : Optional[int] = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
_snake_case : int = model(snake_case_ )[0]
_snake_case : str = torch.Size([1, 8, 10_24] )
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[int] = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
_snake_case : List[Any] = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
_snake_case : List[str] = model(snake_case_ )[0]
_snake_case : str = torch.Size([1, 8, 5_03_58] )
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[int] = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
| 87 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( a : float , a : float , a : float ):
"""simple docstring"""
_snake_case : Optional[Any] = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ = 13 , snake_case_ = 64 , snake_case_ = 2 , snake_case_ = 3 , snake_case_ = 3 , snake_case_ = True , snake_case_ = True , snake_case_ = 1_28 , snake_case_=[16, 32, 64, 1_28] , snake_case_ = 7 , snake_case_ = 4 , snake_case_ = 37 , snake_case_ = "gelu" , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 10 , snake_case_ = 0.02 , snake_case_ = 2 , snake_case_ = 1 , snake_case_ = 1_28 , snake_case_ = [2, 2, 2, 2] , snake_case_ = 2 , snake_case_ = 2 , ):
_snake_case : str = parent
_snake_case : List[Any] = batch_size
_snake_case : Dict = image_size
_snake_case : Dict = patch_size
_snake_case : Optional[int] = num_channels
_snake_case : List[str] = is_training
_snake_case : int = use_labels
_snake_case : Tuple = hidden_size
_snake_case : str = num_hidden_layers
_snake_case : Tuple = num_attention_heads
_snake_case : List[str] = intermediate_size
_snake_case : Optional[Any] = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : List[Any] = attention_probs_dropout_prob
_snake_case : List[str] = type_sequence_label_size
_snake_case : int = initializer_range
_snake_case : List[Any] = encoder_stride
_snake_case : Optional[int] = num_attention_outputs
_snake_case : Optional[Any] = embed_dim
_snake_case : Union[str, Any] = embed_dim + 1
_snake_case : Union[str, Any] = resolution
_snake_case : Tuple = depths
_snake_case : Any = hidden_sizes
_snake_case : int = dim
_snake_case : str = mlp_expansion_ratio
def lowerCamelCase__ ( self ):
_snake_case : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : int = None
if self.use_labels:
_snake_case : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : str = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = TFEfficientFormerModel(config=snake_case_ )
_snake_case : Tuple = model(snake_case_ , training=snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = self.type_sequence_label_size
_snake_case : Any = TFEfficientFormerForImageClassification(snake_case_ )
_snake_case : Tuple = model(snake_case_ , labels=snake_case_ , training=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_snake_case : Tuple = 1
_snake_case : Optional[Any] = TFEfficientFormerForImageClassification(snake_case_ )
_snake_case : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_snake_case : Tuple = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase__ ( self ):
_snake_case : str = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case : Optional[Any] = config_and_inputs
_snake_case : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Tuple = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__lowercase : str = False
__lowercase : str = False
__lowercase : str = False
__lowercase : str = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = TFEfficientFormerModelTester(self )
_snake_case : Optional[int] = ConfigTester(
self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : List[str] = model_class(snake_case_ )
_snake_case : Union[str, Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : Optional[Any] = [*signature.parameters.keys()]
_snake_case : Optional[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = model_class(snake_case_ )
_snake_case : Dict = model(**self._prepare_for_class(snake_case_ , snake_case_ ) , training=snake_case_ )
_snake_case : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case : int = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
if hasattr(self.model_tester , "encoder_seq_length" ):
_snake_case : List[Any] = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , "chunk_length" ) and self.model_tester.chunk_length > 1:
_snake_case : List[Any] = seq_length * self.model_tester.chunk_length
else:
_snake_case : List[str] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
_snake_case : List[str] = outputs.decoder_hidden_states
self.asseretIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , snake_case_ )
_snake_case : List[Any] = getattr(self.model_tester , "seq_length" , snake_case_ )
_snake_case : List[Any] = getattr(self.model_tester , "decoder_seq_length" , snake_case_ )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
_snake_case , _snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : str = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case : List[str] = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : List[str] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet" )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Optional[int] = TFEfficientFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case : Optional[int] = True
_snake_case : str = getattr(self.model_tester , "seq_length" , snake_case_ )
_snake_case : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , snake_case_ )
_snake_case : Dict = getattr(self.model_tester , "key_length" , snake_case_ )
_snake_case : Optional[int] = getattr(self.model_tester , "chunk_length" , snake_case_ )
if chunk_length is not None and hasattr(self.model_tester , "num_hashes" ):
_snake_case : List[Any] = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
_snake_case : List[str] = True
_snake_case : Dict = False
_snake_case : Tuple = True
_snake_case : Optional[Any] = model_class(snake_case_ )
_snake_case : Optional[int] = model(**self._prepare_for_class(snake_case_ , snake_case_ ) , training=snake_case_ )
_snake_case : List[str] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_snake_case : Optional[Any] = True
_snake_case : Union[str, Any] = model_class(snake_case_ )
_snake_case : List[Any] = model(**self._prepare_for_class(snake_case_ , snake_case_ ) , training=snake_case_ )
_snake_case : Optional[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def lowerCamelCase__ ( self ):
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
_snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
_snake_case : Tuple = model_class(snake_case_ )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
_snake_case : str = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=snake_case_ )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
_snake_case : str = model(snake_case_ )
self.assertTrue(outputs_dict is not None )
def a__ ( ):
"""simple docstring"""
_snake_case : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase):
@cached_property
def lowerCamelCase__ ( self ):
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300" )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_snake_case : List[str] = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300" )
_snake_case : Any = self.default_image_processor
_snake_case : Optional[Any] = prepare_img()
_snake_case : str = image_processor(images=snake_case_ , return_tensors="tf" )
# forward pass
_snake_case : List[str] = model(**snake_case_ , training=snake_case_ )
# verify the logits
_snake_case : Tuple = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
@slow
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300" )
_snake_case : str = self.default_image_processor
_snake_case : List[Any] = prepare_img()
_snake_case : str = image_processor(images=snake_case_ , return_tensors="tf" )
# forward pass
_snake_case : Dict = model(**snake_case_ , training=snake_case_ )
# verify the logits
_snake_case : Any = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_snake_case : List[str] = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
| 87 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Any = TextToVideoSDPipeline
__lowercase : str = TEXT_TO_IMAGE_PARAMS
__lowercase : int = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__lowercase : Optional[int] = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : str = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case : List[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , )
torch.manual_seed(0 )
_snake_case : Union[str, Any] = 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 )
_snake_case : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
_snake_case : Tuple = CLIPTextModel(snake_case_ )
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : str = torch.manual_seed(snake_case_ )
else:
_snake_case : Union[str, Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[Any] = self.get_dummy_components()
_snake_case : Tuple = TextToVideoSDPipeline(**snake_case_ )
_snake_case : List[str] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = self.get_dummy_inputs(snake_case_ )
_snake_case : Union[str, Any] = "np"
_snake_case : Dict = sd_pipe(**snake_case_ ).frames
_snake_case : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_snake_case : Dict = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=1E-2 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy" )
_snake_case : int = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_snake_case : Tuple = pipe.to("cuda" )
_snake_case : List[Any] = "Spiderman is surfing"
_snake_case : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : int = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="pt" ).frames
_snake_case : int = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def lowerCamelCase__ ( self ):
_snake_case : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy" )
_snake_case : str = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : int = pipe.to("cuda" )
_snake_case : Any = "Spiderman is surfing"
_snake_case : str = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : Any = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=2 , output_type="pt" ).frames
_snake_case : Optional[int] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 87 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """EncodecFeatureExtractor"""
__lowercase : str = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
_snake_case : Dict = self.feature_extractor
_snake_case : Any = False
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ , language=snake_case_ , no_timestamps=snake_case_ )
def __call__( self , *snake_case_ , **snake_case_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
_snake_case : str = kwargs.pop("audio" , snake_case_ )
_snake_case : Optional[int] = kwargs.pop("sampling_rate" , snake_case_ )
_snake_case : Optional[Any] = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Any = args[0]
_snake_case : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_snake_case : Any = self.tokenizer(snake_case_ , **snake_case_ )
if audio is not None:
_snake_case : Any = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_snake_case : str = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_snake_case : List[str] = audio_inputs["padding_mask"]
return inputs
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
_snake_case : Tuple = kwargs.pop("audio" , snake_case_ )
_snake_case : List[str] = kwargs.pop("padding_mask" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Tuple = args[0]
_snake_case : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(snake_case_ , padding_mask=snake_case_ )
else:
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = to_numpy(snake_case_ )
_snake_case , _snake_case , _snake_case : Tuple = audio_values.shape
if padding_mask is None:
return list(snake_case_ )
_snake_case : Optional[int] = to_numpy(snake_case_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_snake_case : Any = seq_len - padding_mask.shape[-1]
_snake_case : Optional[Any] = 1 - self.feature_extractor.padding_value
_snake_case : Optional[int] = np.pad(snake_case_ , ((0, 0), (0, difference)) , "constant" , constant_values=snake_case_ )
_snake_case : Any = audio_values.tolist()
for i in range(snake_case_ ):
_snake_case : Tuple = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_snake_case : Tuple = sliced_audio.reshape(snake_case_ , -1 )
return audio_values
| 87 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """EncodecFeatureExtractor"""
__lowercase : str = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
_snake_case : Dict = self.feature_extractor
_snake_case : Any = False
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ , language=snake_case_ , no_timestamps=snake_case_ )
def __call__( self , *snake_case_ , **snake_case_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
_snake_case : str = kwargs.pop("audio" , snake_case_ )
_snake_case : Optional[int] = kwargs.pop("sampling_rate" , snake_case_ )
_snake_case : Optional[Any] = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Any = args[0]
_snake_case : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_snake_case : Any = self.tokenizer(snake_case_ , **snake_case_ )
if audio is not None:
_snake_case : Any = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_snake_case : str = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_snake_case : List[str] = audio_inputs["padding_mask"]
return inputs
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
_snake_case : Tuple = kwargs.pop("audio" , snake_case_ )
_snake_case : List[str] = kwargs.pop("padding_mask" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Tuple = args[0]
_snake_case : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(snake_case_ , padding_mask=snake_case_ )
else:
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = to_numpy(snake_case_ )
_snake_case , _snake_case , _snake_case : Tuple = audio_values.shape
if padding_mask is None:
return list(snake_case_ )
_snake_case : Optional[int] = to_numpy(snake_case_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_snake_case : Any = seq_len - padding_mask.shape[-1]
_snake_case : Optional[Any] = 1 - self.feature_extractor.padding_value
_snake_case : Optional[int] = np.pad(snake_case_ , ((0, 0), (0, difference)) , "constant" , constant_values=snake_case_ )
_snake_case : Any = audio_values.tolist()
for i in range(snake_case_ ):
_snake_case : Tuple = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_snake_case : Tuple = sliced_audio.reshape(snake_case_ , -1 )
return audio_values
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=False , snake_case_=True , snake_case_="None" , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Union[str, Any] = parent
_snake_case : Union[str, Any] = batch_size
_snake_case : List[str] = seq_length
_snake_case : List[Any] = is_training
_snake_case : Dict = use_input_mask
_snake_case : Dict = use_token_type_ids
_snake_case : Optional[int] = use_labels
_snake_case : Tuple = vocab_size
_snake_case : str = hidden_size
_snake_case : Any = num_hidden_layers
_snake_case : List[Any] = num_attention_heads
_snake_case : Union[str, Any] = intermediate_size
_snake_case : str = hidden_act
_snake_case : List[str] = hidden_dropout_prob
_snake_case : Optional[int] = attention_probs_dropout_prob
_snake_case : List[Any] = max_position_embeddings
_snake_case : List[Any] = type_vocab_size
_snake_case : List[Any] = type_sequence_label_size
_snake_case : Tuple = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : List[Any] = num_choices
_snake_case : List[str] = relative_attention
_snake_case : List[Any] = position_biased_input
_snake_case : str = pos_att_type
_snake_case : List[str] = scope
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Dict = None
if self.use_input_mask:
_snake_case : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Optional[int] = None
_snake_case : Union[str, Any] = None
_snake_case : Any = None
if self.use_labels:
_snake_case : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : str = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=snake_case_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFDebertaVaModel(config=snake_case_ )
_snake_case : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Dict = [input_ids, input_mask]
_snake_case : int = model(snake_case_ )
_snake_case : str = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[str] = TFDebertaVaForMaskedLM(config=snake_case_ )
_snake_case : List[str] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = self.num_labels
_snake_case : str = TFDebertaVaForSequenceClassification(config=snake_case_ )
_snake_case : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Tuple = self.num_labels
_snake_case : Tuple = TFDebertaVaForTokenClassification(config=snake_case_ )
_snake_case : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_snake_case : str = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Tuple = TFDebertaVaForQuestionAnswering(config=snake_case_ )
_snake_case : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : str = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : List[Any] = config_and_inputs
_snake_case : List[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : str = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
__lowercase : Optional[int] = (
{
"""feature-extraction""": TFDebertaVaModel,
"""fill-mask""": TFDebertaVaForMaskedLM,
"""question-answering""": TFDebertaVaForQuestionAnswering,
"""text-classification""": TFDebertaVaForSequenceClassification,
"""token-classification""": TFDebertaVaForTokenClassification,
"""zero-shot""": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Dict = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
_snake_case : Any = TFDebertaVaModelTester(self )
_snake_case : Dict = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
_snake_case : Dict = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@unittest.skip(reason="Model not available yet" )
def lowerCamelCase__ ( self ):
pass
@slow
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
_snake_case : Optional[int] = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
_snake_case : List[str] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_snake_case : str = model(snake_case_ , attention_mask=snake_case_ )[0]
_snake_case : Optional[Any] = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , snake_case_ , atol=1E-4 )
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""YolosFeatureExtractor"""]
_a : List[Any] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def a__ ( a : Dict ):
"""simple docstring"""
return 1.0 / (1.0 + np.exp(-_outputs ))
def a__ ( a : List[str] ):
"""simple docstring"""
_snake_case : Tuple = np.max(_outputs , axis=-1 , keepdims=a )
_snake_case : Tuple = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=a )
class _UpperCAmelCase ( _snake_case):
__lowercase : Dict = """sigmoid"""
__lowercase : Any = """softmax"""
__lowercase : Optional[int] = """none"""
@add_end_docstrings(
_snake_case , r"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class _UpperCAmelCase ( _snake_case):
__lowercase : Any = False
__lowercase : Dict = ClassificationFunction.NONE
def __init__( self , **snake_case_ ):
super().__init__(**snake_case_ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_="" , **snake_case_ ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
_snake_case : str = tokenizer_kwargs
_snake_case : str = {}
if hasattr(self.model.config , "return_all_scores" ) and return_all_scores is None:
_snake_case : str = self.model.config.return_all_scores
if isinstance(snake_case_ , snake_case_ ) or top_k is None:
_snake_case : Optional[Any] = top_k
_snake_case : List[str] = False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`." , snake_case_ , )
if return_all_scores:
_snake_case : Optional[int] = None
else:
_snake_case : Dict = 1
if isinstance(snake_case_ , snake_case_ ):
_snake_case : List[Any] = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
_snake_case : List[str] = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *snake_case_ , **snake_case_ ):
_snake_case : Dict = super().__call__(*snake_case_ , **snake_case_ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
_snake_case : Tuple = "top_k" not in kwargs
if isinstance(args[0] , snake_case_ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase__ ( self , snake_case_ , **snake_case_ ):
_snake_case : str = self.framework
if isinstance(snake_case_ , snake_case_ ):
return self.tokenizer(**snake_case_ , return_tensors=snake_case_ , **snake_case_ )
elif isinstance(snake_case_ , snake_case_ ) and len(snake_case_ ) == 1 and isinstance(inputs[0] , snake_case_ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=snake_case_ , **snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair." )
return self.tokenizer(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
return self.model(**snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_=None , snake_case_=1 , snake_case_=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
_snake_case : Dict = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
_snake_case : Union[str, Any] = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , "function_to_apply" ) and function_to_apply is None:
_snake_case : Any = self.model.config.function_to_apply
else:
_snake_case : str = ClassificationFunction.NONE
_snake_case : Optional[Any] = model_outputs["logits"][0]
_snake_case : Optional[Any] = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
_snake_case : Optional[Any] = sigmoid(snake_case_ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
_snake_case : Tuple = softmax(snake_case_ )
elif function_to_apply == ClassificationFunction.NONE:
_snake_case : Dict = outputs
else:
raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
_snake_case : List[Any] = [
{"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(snake_case_ )
]
if not _legacy:
dict_scores.sort(key=lambda snake_case_ : x["score"] , reverse=snake_case_ )
if top_k is not None:
_snake_case : int = dict_scores[:top_k]
return dict_scores
| 87 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = dataset
_snake_case : str = process
_snake_case : int = params
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
_snake_case : Union[str, Any] = self.dataset[i]
_snake_case : Optional[Any] = self.process(snake_case_ , **self.params )
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
_snake_case : Union[str, Any] = loader
_snake_case : Tuple = infer
_snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_snake_case : int = None
_snake_case : int = loader_batch_size
# Internal bookkeeping
_snake_case : Any = None
_snake_case : Dict = None
def __len__( self ):
return len(self.loader )
def __iter__( self ):
_snake_case : int = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_snake_case : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
_snake_case : Tuple = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_snake_case : Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : int = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_snake_case : Tuple = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : Tuple = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
_snake_case : Tuple = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : List[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : Union[str, Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_snake_case : List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_snake_case : int = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def lowerCamelCase__ ( self ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_snake_case : Tuple = next(self.iterator )
_snake_case : Any = self.infer(snake_case_ , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Optional[int] = list(processed.keys() )[0]
_snake_case : List[str] = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = len(snake_case_ )
else:
_snake_case : Optional[int] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_snake_case : str = processed
_snake_case : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
_snake_case : Tuple = iter(self.loader )
_snake_case : List[Any] = None
return self
def lowerCamelCase__ ( self ):
if self.subiterator is None:
_snake_case : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_snake_case : Union[str, Any] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_snake_case : str = self.infer(next(self.iterator ) , **self.params )
_snake_case : Tuple = next(self.subiterator )
return processed
class _UpperCAmelCase ( _snake_case):
def __iter__( self ):
_snake_case : Optional[Any] = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
_snake_case : Optional[Any] = False
_snake_case : Tuple = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : str = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
_snake_case : List[str] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Tuple = list(processed.keys() )[0]
_snake_case : Tuple = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Any = len(snake_case_ )
else:
_snake_case : List[Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Dict = observed_batch_size
_snake_case : List[Any] = processed
_snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : int = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
_snake_case : Dict = processed
_snake_case : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ ):
_snake_case : str = dataset
_snake_case : Any = key
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return self.dataset[i][self.key]
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = dataset
_snake_case : Any = keya
_snake_case : int = keya
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 87 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a : str = """platform"""
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def a__ ( a : Union[str, Any] , a : Dict , a : str=None , a : int=None , a : Optional[Any]=None , a : List[str]=None , a : Union[str, Any]=None , a : Any=None , ):
"""simple docstring"""
if attention_mask is None:
_snake_case : int = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
_snake_case : Optional[Any] = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
_snake_case : Union[str, Any] = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_snake_case : Dict = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_snake_case : Tuple = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=False , snake_case_=99 , snake_case_=16 , snake_case_=2 , snake_case_=4 , snake_case_=4 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=32 , snake_case_=2 , snake_case_=1 , snake_case_=0 , snake_case_=0.02 , ):
_snake_case : Union[str, Any] = parent
_snake_case : str = batch_size
_snake_case : Any = seq_length
_snake_case : Union[str, Any] = is_training
_snake_case : Optional[Any] = use_labels
_snake_case : Dict = vocab_size
_snake_case : Any = hidden_size
_snake_case : Optional[Any] = num_hidden_layers
_snake_case : int = num_attention_heads
_snake_case : Any = intermediate_size
_snake_case : Tuple = hidden_act
_snake_case : List[str] = hidden_dropout_prob
_snake_case : Optional[int] = attention_probs_dropout_prob
_snake_case : int = max_position_embeddings
_snake_case : int = eos_token_id
_snake_case : List[Any] = pad_token_id
_snake_case : Optional[int] = bos_token_id
_snake_case : Dict = initializer_range
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
_snake_case : Optional[Any] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
_snake_case : str = shift_tokens_right(snake_case_ , 1 , 2 )
_snake_case : Union[str, Any] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=snake_case_ , )
_snake_case : List[str] = prepare_blenderbot_inputs_dict(snake_case_ , snake_case_ , snake_case_ )
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : List[str] = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = 20
_snake_case : Any = model_class_name(snake_case_ )
_snake_case : List[Any] = model.encode(inputs_dict["input_ids"] )
_snake_case , _snake_case : Tuple = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_snake_case : List[Any] = model.init_cache(decoder_input_ids.shape[0] , snake_case_ , snake_case_ )
_snake_case : List[str] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
_snake_case : Union[str, Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_snake_case : Tuple = model.decode(
decoder_input_ids[:, :-1] , snake_case_ , decoder_attention_mask=snake_case_ , past_key_values=snake_case_ , decoder_position_ids=snake_case_ , )
_snake_case : Optional[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
_snake_case : Tuple = model.decode(
decoder_input_ids[:, -1:] , snake_case_ , decoder_attention_mask=snake_case_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=snake_case_ , )
_snake_case : Tuple = model.decode(snake_case_ , snake_case_ )
_snake_case : Union[str, Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = 20
_snake_case : Optional[int] = model_class_name(snake_case_ )
_snake_case : Union[str, Any] = model.encode(inputs_dict["input_ids"] )
_snake_case , _snake_case : Optional[Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_snake_case : Any = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_snake_case : Dict = model.init_cache(decoder_input_ids.shape[0] , snake_case_ , snake_case_ )
_snake_case : Union[str, Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_snake_case : List[Any] = model.decode(
decoder_input_ids[:, :-1] , snake_case_ , decoder_attention_mask=snake_case_ , past_key_values=snake_case_ , decoder_position_ids=snake_case_ , )
_snake_case : str = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
_snake_case : List[str] = model.decode(
decoder_input_ids[:, -1:] , snake_case_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=snake_case_ , decoder_position_ids=snake_case_ , )
_snake_case : Tuple = model.decode(snake_case_ , snake_case_ , decoder_attention_mask=snake_case_ )
_snake_case : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
@require_flax
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : List[str] = 9_9
def lowerCamelCase__ ( self ):
_snake_case : str = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
_snake_case : List[Any] = input_ids.shape[0]
_snake_case : str = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def lowerCamelCase__ ( self ):
_snake_case , _snake_case , _snake_case : int = self._get_config_and_data()
_snake_case : List[str] = FlaxBlenderbotSmallForConditionalGeneration(snake_case_ )
_snake_case : Any = lm_model(input_ids=snake_case_ )
_snake_case : int = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
_snake_case : Tuple = FlaxBlenderbotSmallForConditionalGeneration(snake_case_ )
_snake_case : int = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
_snake_case : List[Any] = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
_snake_case : str = lm_model(input_ids=snake_case_ , decoder_input_ids=snake_case_ )
_snake_case : int = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
_snake_case : Optional[Any] = shift_tokens_right(snake_case_ , 1 , 2 )
_snake_case : int = np.equal(snake_case_ , 1 ).astype(np.floataa ).sum()
_snake_case : str = np.equal(snake_case_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(snake_case_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class _UpperCAmelCase ( _snake_case , unittest.TestCase , _snake_case):
__lowercase : str = True
__lowercase : Optional[Any] = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
__lowercase : Dict = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def lowerCamelCase__ ( self ):
_snake_case : str = FlaxBlenderbotSmallModelTester(self )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_snake_case : Tuple = self._prepare_for_class(snake_case_ , snake_case_ )
_snake_case : int = model_class(snake_case_ )
@jax.jit
def encode_jitted(snake_case_ , snake_case_=None , **snake_case_ ):
return model.encode(input_ids=snake_case_ , attention_mask=snake_case_ )
with self.subTest("JIT Enabled" ):
_snake_case : Union[str, Any] = encode_jitted(**snake_case_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_snake_case : List[Any] = encode_jitted(**snake_case_ ).to_tuple()
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for jitted_output, output in zip(snake_case_ , snake_case_ ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_snake_case : Optional[Any] = model_class(snake_case_ )
_snake_case : List[Any] = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
_snake_case : str = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(snake_case_ , snake_case_ , snake_case_ ):
return model.decode(
decoder_input_ids=snake_case_ , decoder_attention_mask=snake_case_ , encoder_outputs=snake_case_ , )
with self.subTest("JIT Enabled" ):
_snake_case : Optional[Any] = decode_jitted(**snake_case_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_snake_case : Any = decode_jitted(**snake_case_ ).to_tuple()
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for jitted_output, output in zip(snake_case_ , snake_case_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowerCamelCase__ ( self ):
for model_class_name in self.all_model_classes:
_snake_case : Any = model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
_snake_case : Any = np.ones((1, 1) ) * model.config.eos_token_id
_snake_case : Any = model(snake_case_ )
self.assertIsNotNone(snake_case_ )
| 87 |
"""simple docstring"""
def a__ ( a : int ):
"""simple docstring"""
if not isinstance(a , a ):
raise TypeError("Input value must be an 'int' type" )
_snake_case : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_a : List[str] = logging.get_logger("""transformers.models.speecht5""")
def a__ ( a : Optional[int] , a : Dict , a : List[str] ):
"""simple docstring"""
hf_model.apply_weight_norm()
_snake_case : Optional[int] = checkpoint["input_conv.weight_g"]
_snake_case : List[str] = checkpoint["input_conv.weight_v"]
_snake_case : List[str] = checkpoint["input_conv.bias"]
for i in range(len(config.upsample_rates ) ):
_snake_case : str = checkpoint[f'upsamples.{i}.1.weight_g']
_snake_case : Optional[int] = checkpoint[f'upsamples.{i}.1.weight_v']
_snake_case : Union[str, Any] = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
_snake_case : List[Any] = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
_snake_case : Optional[Any] = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
_snake_case : List[str] = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
_snake_case : Optional[Any] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
_snake_case : List[str] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
_snake_case : Optional[Any] = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
_snake_case : str = checkpoint["output_conv.1.weight_g"]
_snake_case : Dict = checkpoint["output_conv.1.weight_v"]
_snake_case : Tuple = checkpoint["output_conv.1.bias"]
hf_model.remove_weight_norm()
@torch.no_grad()
def a__ ( a : int , a : Dict , a : List[Any] , a : Dict=None , a : List[Any]=None , ):
"""simple docstring"""
if config_path is not None:
_snake_case : Any = SpeechTaHifiGanConfig.from_pretrained(a )
else:
_snake_case : Tuple = SpeechTaHifiGanConfig()
_snake_case : Union[str, Any] = SpeechTaHifiGan(a )
_snake_case : Tuple = torch.load(a )
load_weights(orig_checkpoint["model"]["generator"] , a , a )
_snake_case : Tuple = np.load(a )
_snake_case : Dict = stats[0].reshape(-1 )
_snake_case : Union[str, Any] = stats[1].reshape(-1 )
_snake_case : Any = torch.from_numpy(a ).float()
_snake_case : Union[str, Any] = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print("Pushing to the hub..." )
model.push_to_hub(a )
if __name__ == "__main__":
_a : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_a : Any = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 87 |
"""simple docstring"""
from __future__ import annotations
import requests
_a : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( a : str , a : int = 1 , a : str = "new" , a : list | None = None ):
"""simple docstring"""
_snake_case : Any = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(a ) - valid_terms ) ):
_snake_case : Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(a )
_snake_case : int = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"User-agent": "A random string"} , )
if response.status_code == 429:
raise requests.HTTPError
_snake_case : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(a )}
_snake_case : Tuple = {}
for id_ in range(a ):
_snake_case : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 87 | 1 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( enum.Enum):
__lowercase : str = 0
__lowercase : List[Any] = 1
__lowercase : Tuple = 2
@add_end_docstrings(_snake_case)
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
"""
def __init__( self , *snake_case_ , **snake_case_ ):
super().__init__(*snake_case_ , **snake_case_ )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
_snake_case : int = None
if self.model.config.prefix is not None:
_snake_case : str = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
_snake_case : Optional[Any] = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
_snake_case , _snake_case , _snake_case : str = self._sanitize_parameters(prefix=snake_case_ , **self._forward_params )
_snake_case : str = {**self._preprocess_params, **preprocess_params}
_snake_case : Optional[int] = {**self._forward_params, **forward_params}
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , **snake_case_ , ):
_snake_case : Optional[int] = {}
if prefix is not None:
_snake_case : List[str] = prefix
if prefix:
_snake_case : Dict = self.tokenizer(
snake_case_ , padding=snake_case_ , add_special_tokens=snake_case_ , return_tensors=self.framework )
_snake_case : Dict = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
F'{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'
" [None, 'hole']" )
_snake_case : Any = handle_long_generation
preprocess_params.update(snake_case_ )
_snake_case : int = generate_kwargs
_snake_case : Union[str, Any] = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
_snake_case : Optional[Any] = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
_snake_case : str = ReturnType.TENSORS
if return_type is not None:
_snake_case : List[str] = return_type
if clean_up_tokenization_spaces is not None:
_snake_case : Dict = clean_up_tokenization_spaces
if stop_sequence is not None:
_snake_case : List[str] = self.tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
if len(snake_case_ ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
_snake_case : Optional[int] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*snake_case_ , **snake_case_ )
def __call__( self , snake_case_ , **snake_case_ ):
return super().__call__(snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_="" , snake_case_=None , **snake_case_ ):
_snake_case : Dict = self.tokenizer(
prefix + prompt_text , padding=snake_case_ , add_special_tokens=snake_case_ , return_tensors=self.framework )
_snake_case : Any = prompt_text
if handle_long_generation == "hole":
_snake_case : Dict = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
_snake_case : Union[str, Any] = generate_kwargs["max_new_tokens"]
else:
_snake_case : int = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
_snake_case : List[str] = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
_snake_case : Optional[Any] = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
_snake_case : int = inputs["attention_mask"][:, -keep_length:]
return inputs
def lowerCamelCase__ ( self , snake_case_ , **snake_case_ ):
_snake_case : str = model_inputs["input_ids"]
_snake_case : Optional[Any] = model_inputs.get("attention_mask" , snake_case_ )
# Allow empty prompts
if input_ids.shape[1] == 0:
_snake_case : List[Any] = None
_snake_case : str = None
_snake_case : Tuple = 1
else:
_snake_case : Optional[Any] = input_ids.shape[0]
_snake_case : Optional[int] = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
_snake_case : Union[str, Any] = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
_snake_case : Any = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
_snake_case : Optional[int] = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
_snake_case : int = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
_snake_case : Optional[int] = self.model.generate(input_ids=snake_case_ , attention_mask=snake_case_ , **snake_case_ )
_snake_case : int = generated_sequence.shape[0]
if self.framework == "pt":
_snake_case : List[Any] = generated_sequence.reshape(snake_case_ , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
_snake_case : Union[str, Any] = tf.reshape(snake_case_ , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def lowerCamelCase__ ( self , snake_case_ , snake_case_=ReturnType.FULL_TEXT , snake_case_=True ):
_snake_case : str = model_outputs["generated_sequence"][0]
_snake_case : int = model_outputs["input_ids"]
_snake_case : Optional[int] = model_outputs["prompt_text"]
_snake_case : List[Any] = generated_sequence.numpy().tolist()
_snake_case : Union[str, Any] = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
_snake_case : int = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
_snake_case : int = self.tokenizer.decode(
snake_case_ , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
_snake_case : Dict = 0
else:
_snake_case : Any = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ , ) )
if return_type == ReturnType.FULL_TEXT:
_snake_case : str = prompt_text + text[prompt_length:]
else:
_snake_case : Tuple = text[prompt_length:]
_snake_case : List[Any] = {"generated_text": all_text}
records.append(snake_case_ )
return records
| 87 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(a ), magnitude * sin(a )]
return [magnitude * cos(radians(a ) ), magnitude * sin(radians(a ) )]
def a__ ( a : NDArray[floataa] , a : NDArray[floataa] , a : float = 10**-1 ):
"""simple docstring"""
_snake_case : NDArray[floataa] = cross(a , a )
_snake_case : float = sum(a )
return abs(a ) < eps
if __name__ == "__main__":
# Test to check if it works
_a : Tuple = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
_a : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_a : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
_a : List[Any] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_a : List[str] = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
_a : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def a__ ( a : List[str] ):
"""simple docstring"""
_snake_case : Dict = SwinConfig(image_size=192 )
if "base" in model_name:
_snake_case : List[Any] = 6
_snake_case : Dict = 128
_snake_case : Dict = (2, 2, 18, 2)
_snake_case : Tuple = (4, 8, 16, 32)
elif "large" in model_name:
_snake_case : Dict = 12
_snake_case : Tuple = 192
_snake_case : List[str] = (2, 2, 18, 2)
_snake_case : Tuple = (6, 12, 24, 48)
else:
raise ValueError("Model not supported, only supports base and large variants" )
_snake_case : Tuple = window_size
_snake_case : Union[str, Any] = embed_dim
_snake_case : int = depths
_snake_case : Tuple = num_heads
return config
def a__ ( a : List[str] ):
"""simple docstring"""
if "encoder.mask_token" in name:
_snake_case : Union[str, Any] = name.replace("encoder.mask_token" , "embeddings.mask_token" )
if "encoder.patch_embed.proj" in name:
_snake_case : Any = name.replace("encoder.patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "encoder.patch_embed.norm" in name:
_snake_case : Any = name.replace("encoder.patch_embed.norm" , "embeddings.norm" )
if "attn.proj" in name:
_snake_case : Optional[int] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
_snake_case : Tuple = name.replace("attn" , "attention.self" )
if "norm1" in name:
_snake_case : Any = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
_snake_case : Optional[Any] = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
_snake_case : int = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
_snake_case : Union[str, Any] = name.replace("mlp.fc2" , "output.dense" )
if name == "encoder.norm.weight":
_snake_case : int = "layernorm.weight"
if name == "encoder.norm.bias":
_snake_case : str = "layernorm.bias"
if "decoder" in name:
pass
else:
_snake_case : List[Any] = "swin." + name
return name
def a__ ( a : int , a : Dict ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_snake_case : List[Any] = orig_state_dict.pop(a )
if "attn_mask" in key:
pass
elif "qkv" in key:
_snake_case : Union[str, Any] = key.split("." )
_snake_case : List[str] = int(key_split[2] )
_snake_case : Union[str, Any] = int(key_split[4] )
_snake_case : List[Any] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_snake_case : List[Any] = val[:dim, :]
_snake_case : Optional[int] = val[
dim : dim * 2, :
]
_snake_case : Any = val[-dim:, :]
else:
_snake_case : Optional[int] = val[
:dim
]
_snake_case : List[Any] = val[
dim : dim * 2
]
_snake_case : Any = val[
-dim:
]
else:
_snake_case : str = val
return orig_state_dict
def a__ ( a : Optional[Any] , a : Optional[Any] , a : Optional[Any] , a : List[str] ):
"""simple docstring"""
_snake_case : str = torch.load(a , map_location="cpu" )["model"]
_snake_case : Union[str, Any] = get_swin_config(a )
_snake_case : Optional[Any] = SwinForMaskedImageModeling(a )
model.eval()
_snake_case : List[str] = convert_state_dict(a , a )
model.load_state_dict(a )
_snake_case : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_snake_case : List[str] = ViTImageProcessor(size={"height": 192, "width": 192} )
_snake_case : int = Image.open(requests.get(a , stream=a ).raw )
_snake_case : int = image_processor(images=a , return_tensors="pt" )
with torch.no_grad():
_snake_case : str = model(**a ).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(a )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(a )
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__":
_a : Optional[Any] = 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."""
)
_a : Optional[Any] = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """openai-gpt"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=4_04_78 , snake_case_=5_12 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_="cls_index" , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=0.1 , **snake_case_ , ):
_snake_case : Tuple = vocab_size
_snake_case : Dict = n_positions
_snake_case : Any = n_embd
_snake_case : Any = n_layer
_snake_case : Optional[int] = n_head
_snake_case : Union[str, Any] = afn
_snake_case : Dict = resid_pdrop
_snake_case : str = embd_pdrop
_snake_case : Union[str, Any] = attn_pdrop
_snake_case : str = layer_norm_epsilon
_snake_case : Union[str, Any] = initializer_range
_snake_case : Any = summary_type
_snake_case : List[str] = summary_use_proj
_snake_case : Optional[int] = summary_activation
_snake_case : Union[str, Any] = summary_first_dropout
_snake_case : Optional[int] = summary_proj_to_labels
super().__init__(**snake_case_ )
| 87 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a : List[Any] = True
except (ImportError, ModuleNotFoundError):
_a : Any = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def a__ ( a : str ):
"""simple docstring"""
re.sub("<n>" , "" , a ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(a ) )
| 87 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_a : int = []
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}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_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'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""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"""),
]
)
def a__ ( a : List[str] , a : int , a : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = state_dict.pop(a )
_snake_case : Union[str, Any] = val
def a__ ( a : Tuple ):
"""simple docstring"""
_snake_case : Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_snake_case : Dict = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
_snake_case : Tuple = value
else:
_snake_case : Dict = value
return new_state_dict
def a__ ( a : int ):
"""simple docstring"""
_snake_case : Any = ""
# 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)
_snake_case : str = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : str = 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
_snake_case : int = in_proj_weight[:256, :]
_snake_case : List[str] = in_proj_bias[:256]
_snake_case : Optional[Any] = in_proj_weight[256:512, :]
_snake_case : List[str] = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : Dict = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_snake_case : List[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_snake_case : Union[str, Any] = in_proj_weight[:256, :]
_snake_case : Tuple = in_proj_bias[:256]
_snake_case : int = in_proj_weight[256:512, :]
_snake_case : int = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : str = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_snake_case : Dict = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
_snake_case : Optional[int] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_snake_case : Dict = in_proj_weight_cross_attn[:256, :]
_snake_case : Any = in_proj_bias_cross_attn[:256]
_snake_case : Union[str, Any] = in_proj_weight_cross_attn[256:512, :]
_snake_case : Optional[int] = in_proj_bias_cross_attn[256:512]
_snake_case : Any = in_proj_weight_cross_attn[-256:, :]
_snake_case : str = in_proj_bias_cross_attn[-256:]
def a__ ( a : str , a : int ):
"""simple docstring"""
_snake_case , _snake_case : List[str] = image.size
_snake_case : Dict = max(a , a )
_snake_case : Union[str, Any] = 800 if "detection" in checkpoint_url else 1_000
_snake_case : Any = target_max_size / current_max_size
_snake_case : str = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( a : str ):
"""simple docstring"""
_snake_case : str = F.to_tensor(a )
_snake_case : Union[str, Any] = F.normalize(a , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( a : Optional[Any] , a : Any , a : Union[str, Any] ):
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
_snake_case : Tuple = torch.hub.load_state_dict_from_url(a , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(a , a , a )
_snake_case : Union[str, Any] = rename_backbone_keys(a )
# query, key and value matrices need special treatment
read_in_q_k_v(a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_snake_case : int = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_snake_case : Optional[int] = state_dict.pop(a )
_snake_case : Any = val
# create HuggingFace model and load state dict
_snake_case : Tuple = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
_snake_case : Any = 15
_snake_case : int = 2
_snake_case : Optional[Any] = {0: "table", 1: "table rotated"}
_snake_case : Union[str, Any] = idalabel
_snake_case : Union[str, Any] = {v: k for k, v in idalabel.items()}
else:
_snake_case : Any = 125
_snake_case : Union[str, Any] = 6
_snake_case : List[str] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
_snake_case : Any = idalabel
_snake_case : Optional[int] = {v: k for k, v in idalabel.items()}
_snake_case : Union[str, Any] = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1_000 )
_snake_case : str = TableTransformerForObjectDetection(a )
model.load_state_dict(a )
model.eval()
# verify our conversion
_snake_case : Optional[int] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
_snake_case : Optional[Any] = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=a )
_snake_case : Dict = Image.open(a ).convert("RGB" )
_snake_case : Union[str, Any] = normalize(resize(a , a ) ).unsqueeze(0 )
_snake_case : str = model(a )
if "detection" in checkpoint_url:
_snake_case : int = (1, 15, 3)
_snake_case : List[str] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
_snake_case : List[str] = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
_snake_case : Union[str, Any] = (1, 125, 7)
_snake_case : str = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
_snake_case : Optional[Any] = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
_snake_case : int = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(a )
image_processor.push_to_hub(a )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint 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."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : Any = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""YolosFeatureExtractor"""]
_a : List[Any] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 | 1 |
"""simple docstring"""
def a__ ( a : list[list] ):
"""simple docstring"""
_snake_case : Any = current_set.copy()
for row_index, row in enumerate(a ):
_snake_case : List[Any] = row[0]
for column_index, column in enumerate(a ):
if magnitude == 0:
_snake_case : Union[str, Any] = column
continue
_snake_case : List[str] = column / magnitude
# Subtract to cancel term
_snake_case : Optional[int] = current_set[0]
_snake_case : Optional[int] = [first_row]
_snake_case : List[str] = current_set[1::]
for row in current_set:
_snake_case : Tuple = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(a )
continue
for column_index in range(len(a ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(a )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
_snake_case : List[str] = final_set[0]
_snake_case : Dict = []
_snake_case : Tuple = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
_snake_case : Optional[int] = simplify(a )
for i in range(len(a ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , a )
_snake_case : Any = resultant
return final_set
def a__ ( a : list[list] ):
"""simple docstring"""
if len(a ) == 0:
raise IndexError("solve_simultaneous() requires n lists of length n+1" )
_snake_case : Optional[int] = len(a ) + 1
if any(len(a ) != _length for item in equations ):
raise IndexError("solve_simultaneous() requires n lists of length n+1" )
for row in equations:
if any(not isinstance(a , (int, float) ) for column in row ):
raise ValueError("solve_simultaneous() requires lists of integers" )
if len(a ) == 1:
return [equations[0][-1] / equations[0][0]]
_snake_case : Dict = equations.copy()
if any(0 in row for row in data_set ):
_snake_case : Tuple = data_set.copy()
_snake_case : Any = []
for row_index, row in enumerate(a ):
if 0 not in row:
_snake_case : Optional[Any] = data_set.pop(a )
break
if not full_row:
raise ValueError("solve_simultaneous() requires at least 1 full equation" )
data_set.insert(0 , a )
_snake_case : Optional[int] = data_set.copy()
_snake_case : Union[str, Any] = simplify(a )
_snake_case : Optional[Any] = simplified[::-1]
_snake_case : list = []
for row in simplified:
_snake_case : str = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
_snake_case : str = row.copy()[: len(a ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(a ) == 0:
solutions.append(0 )
continue
_snake_case : Any = temp_row[1::]
_snake_case : Any = temp_row[::-1]
for column_index, column in enumerate(a ):
current_solution -= column * solutions[column_index]
solutions.append(a )
_snake_case : Optional[Any] = []
for item in solutions:
final.append(float(round(a , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
_a : Optional[int] = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( a : str , a : Dict , a : Tuple , a : Dict , a : Dict ):
"""simple docstring"""
with open(a ) as metadata_file:
_snake_case : Tuple = json.load(a )
_snake_case : Dict = LukeConfig(use_entity_aware_attention=a , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
_snake_case : int = torch.load(a , map_location="cpu" )["module"]
# Load the entity vocab file
_snake_case : Dict = load_original_entity_vocab(a )
# add an entry for [MASK2]
_snake_case : Any = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_snake_case : Union[str, Any] = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
_snake_case : List[str] = AddedToken("<ent>" , lstrip=a , rstrip=a )
_snake_case : Tuple = AddedToken("<ent2>" , lstrip=a , rstrip=a )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(a )
with open(os.path.join(a , "tokenizer_config.json" ) , "r" ) as f:
_snake_case : str = json.load(a )
_snake_case : Optional[Any] = "MLukeTokenizer"
with open(os.path.join(a , "tokenizer_config.json" ) , "w" ) as f:
json.dump(a , a )
with open(os.path.join(a , MLukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(a , a )
_snake_case : Tuple = MLukeTokenizer.from_pretrained(a )
# Initialize the embeddings of the special tokens
_snake_case : List[Any] = tokenizer.convert_tokens_to_ids(["@"] )[0]
_snake_case : List[Any] = tokenizer.convert_tokens_to_ids(["#"] )[0]
_snake_case : Tuple = state_dict["embeddings.word_embeddings.weight"]
_snake_case : Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
_snake_case : Dict = word_emb[enta_init_index].unsqueeze(0 )
_snake_case : Any = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_snake_case : Optional[Any] = state_dict[bias_name]
_snake_case : List[str] = decoder_bias[ent_init_index].unsqueeze(0 )
_snake_case : Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
_snake_case : Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_snake_case : Optional[int] = f'encoder.layer.{layer_index}.attention.self.'
_snake_case : Union[str, Any] = state_dict[prefix + matrix_name]
_snake_case : str = state_dict[prefix + matrix_name]
_snake_case : List[str] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_snake_case : List[Any] = state_dict["entity_embeddings.entity_embeddings.weight"]
_snake_case : Optional[int] = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
_snake_case : str = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_snake_case : str = state_dict["entity_predictions.bias"]
_snake_case : List[str] = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
_snake_case : str = torch.cat([entity_prediction_bias, entity_mask_bias] )
_snake_case : Optional[Any] = LukeForMaskedLM(config=a ).eval()
state_dict.pop("entity_predictions.decoder.weight" )
state_dict.pop("lm_head.decoder.weight" )
state_dict.pop("lm_head.decoder.bias" )
_snake_case : Optional[int] = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
_snake_case : Tuple = state_dict[key]
else:
_snake_case : str = state_dict[key]
_snake_case , _snake_case : str = model.load_state_dict(a , strict=a )
if set(a ) != {"luke.embeddings.position_ids"}:
raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}' )
if set(a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_snake_case : Dict = MLukeTokenizer.from_pretrained(a , task="entity_classification" )
_snake_case : str = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
_snake_case : Tuple = (0, 9)
_snake_case : Any = tokenizer(a , entity_spans=[span] , return_tensors="pt" )
_snake_case : str = model(**a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_snake_case : Any = torch.Size((1, 33, 768) )
_snake_case : Any = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , a , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_snake_case : Tuple = torch.Size((1, 1, 768) )
_snake_case : Union[str, Any] = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , a , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
_snake_case : str = MLukeTokenizer.from_pretrained(a )
_snake_case : Optional[int] = "Tokyo is the capital of <mask>."
_snake_case : Any = (24, 30)
_snake_case : Dict = tokenizer(a , entity_spans=[span] , return_tensors="pt" )
_snake_case : List[Any] = model(**a )
_snake_case : List[str] = encoding["input_ids"][0].tolist()
_snake_case : List[Any] = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
_snake_case : Optional[int] = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(a )
_snake_case : int = outputs.entity_logits[0][0].argmax().item()
_snake_case : Union[str, Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith("en:" )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(a ) )
model.save_pretrained(a )
def a__ ( a : List[str] ):
"""simple docstring"""
_snake_case : Optional[int] = ["[MASK]", "[PAD]", "[UNK]"]
_snake_case : Dict = [json.loads(a ) for line in open(a )]
_snake_case : Any = {}
for entry in data:
_snake_case : List[Any] = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_snake_case : int = entity_id
break
_snake_case : int = f'{language}:{entity_name}'
_snake_case : Dict = entity_id
return new_mapping
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
parser.add_argument(
"""--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
)
parser.add_argument(
"""--entity_vocab_path""",
default=None,
type=str,
help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
)
parser.add_argument(
"""--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
)
_a : Dict = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 87 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_a : int = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _UpperCAmelCase ( _snake_case):
@staticmethod
def lowerCamelCase__ ( snake_case_ ):
_snake_case : Dict = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=snake_case_ , required=snake_case_ , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=snake_case_ , required=snake_case_ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=snake_case_ , required=snake_case_ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=snake_case_ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=snake_case_ , default=snake_case_ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : str = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
_snake_case : Optional[int] = model_type
_snake_case : Any = tf_checkpoint
_snake_case : Optional[int] = pytorch_dump_output
_snake_case : Tuple = config
_snake_case : Tuple = finetuning_task_name
def lowerCamelCase__ ( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case : int = self._tf_checkpoint
_snake_case : Optional[Any] = ""
else:
_snake_case : Optional[int] = self._tf_checkpoint
_snake_case : List[str] = ""
convert_transfo_xl_checkpoint_to_pytorch(
snake_case_ , self._config , self._pytorch_dump_output , snake_case_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 87 | 1 |
"""simple docstring"""
import string
def a__ ( a : str ):
"""simple docstring"""
_snake_case : Any = ""
for i in sequence:
_snake_case : List[Any] = ord(a )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - extract )
else:
output += i
return output
def a__ ( a : str ):
"""simple docstring"""
_snake_case : str = string.ascii_letters
_snake_case : int = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(a )] if c in letters else c for c in sequence )
def a__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running performance benchmarks..." )
_snake_case : int = "from string import printable ; from __main__ import atbash, atbash_slow"
print(f'> atbash_slow(): {timeit("atbash_slow(printable)" , setup=a )} seconds' )
print(f'> atbash(): {timeit("atbash(printable)" , setup=a )} seconds' )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'{example} encrypted in atbash: {atbash(example)}')
benchmark()
| 87 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(a ), magnitude * sin(a )]
return [magnitude * cos(radians(a ) ), magnitude * sin(radians(a ) )]
def a__ ( a : NDArray[floataa] , a : NDArray[floataa] , a : float = 10**-1 ):
"""simple docstring"""
_snake_case : NDArray[floataa] = cross(a , a )
_snake_case : float = sum(a )
return abs(a ) < eps
if __name__ == "__main__":
# Test to check if it works
_a : Tuple = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
_a : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_a : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
_a : List[Any] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_a : List[str] = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
_a : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Optional[Any] = ShapEPipeline
__lowercase : int = ["""prompt"""]
__lowercase : Optional[int] = ["""prompt"""]
__lowercase : Optional[Any] = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
__lowercase : Optional[Any] = False
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return self.time_input_dim * 4
@property
def lowerCamelCase__ ( self ):
return 8
@property
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(snake_case_ )
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Tuple = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
_snake_case : Dict = PriorTransformer(**snake_case_ )
return model
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Dict = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
_snake_case : Union[str, Any] = ShapERenderer(**snake_case_ )
return model
def lowerCamelCase__ ( self ):
_snake_case : int = self.dummy_prior
_snake_case : str = self.dummy_text_encoder
_snake_case : Optional[int] = self.dummy_tokenizer
_snake_case : Tuple = self.dummy_renderer
_snake_case : Dict = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=10_24 , prediction_type="sample" , use_karras_sigmas=snake_case_ , clip_sample=snake_case_ , clip_sample_range=1.0 , )
_snake_case : List[str] = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : Any = torch.manual_seed(snake_case_ )
else:
_snake_case : Union[str, Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : Any = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : Any = "cpu"
_snake_case : int = self.get_dummy_components()
_snake_case : Tuple = self.pipeline_class(**snake_case_ )
_snake_case : Any = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Optional[int] = pipe(**self.get_dummy_inputs(snake_case_ ) )
_snake_case : List[str] = output.images[0]
_snake_case : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_snake_case : List[str] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = torch_device == "cpu"
_snake_case : Tuple = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=snake_case_ , relax_max_difference=snake_case_ , )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.get_dummy_components()
_snake_case : str = self.pipeline_class(**snake_case_ )
_snake_case : Optional[Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = 1
_snake_case : Dict = 2
_snake_case : Union[str, Any] = self.get_dummy_inputs(snake_case_ )
for key in inputs.keys():
if key in self.batch_params:
_snake_case : Tuple = batch_size * [inputs[key]]
_snake_case : Optional[Any] = pipe(**snake_case_ , num_images_per_prompt=snake_case_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy" )
_snake_case : Any = ShapEPipeline.from_pretrained("openai/shap-e" )
_snake_case : List[Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : str = torch.Generator(device=snake_case_ ).manual_seed(0 )
_snake_case : Dict = pipe(
"a shark" , generator=snake_case_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 87 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_a : List[Any] = get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_a : str = get_tests_dir("""fixtures/vocab.json""")
_a : int = get_tests_dir("""fixtures""")
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : Optional[Any] = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
def lowerCamelCase__ ( self ):
_snake_case : Any = 0
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case : Union[str, Any] = WavaVecaConfig()
_snake_case : Optional[int] = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
# save in new folder
model_config.save_pretrained(snake_case_ )
processor.save_pretrained(snake_case_ )
_snake_case : List[Any] = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(snake_case_ , os.path.join(snake_case_ , snake_case_ ) )
copyfile(snake_case_ , os.path.join(snake_case_ , "vocab.json" ) )
_snake_case : Dict = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case : List[Any] = WavaVecaFeatureExtractor()
_snake_case : Optional[int] = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
_snake_case : Optional[Any] = WavaVecaProcessor(snake_case_ , snake_case_ )
# save in new folder
processor.save_pretrained(snake_case_ )
# drop `processor_class` in tokenizer
with open(os.path.join(snake_case_ , snake_case_ ) , "r" ) as f:
_snake_case : Any = json.load(snake_case_ )
config_dict.pop("processor_class" )
with open(os.path.join(snake_case_ , snake_case_ ) , "w" ) as f:
f.write(json.dumps(snake_case_ ) )
_snake_case : Optional[Any] = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case : Optional[int] = WavaVecaFeatureExtractor()
_snake_case : Tuple = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
_snake_case : str = WavaVecaProcessor(snake_case_ , snake_case_ )
# save in new folder
processor.save_pretrained(snake_case_ )
# drop `processor_class` in feature extractor
with open(os.path.join(snake_case_ , snake_case_ ) , "r" ) as f:
_snake_case : str = json.load(snake_case_ )
config_dict.pop("processor_class" )
with open(os.path.join(snake_case_ , snake_case_ ) , "w" ) as f:
f.write(json.dumps(snake_case_ ) )
_snake_case : Tuple = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case : int = WavaVecaConfig(processor_class="Wav2Vec2Processor" )
model_config.save_pretrained(snake_case_ )
# copy relevant files
copyfile(snake_case_ , os.path.join(snake_case_ , "vocab.json" ) )
# create emtpy sample processor
with open(os.path.join(snake_case_ , snake_case_ ) , "w" ) as f:
f.write("{}" )
_snake_case : Tuple = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case_ ):
_snake_case : List[str] = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case_ ):
_snake_case : Dict = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=snake_case_ )
_snake_case : int = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" , trust_remote_code=snake_case_ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
_snake_case : Optional[Any] = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
_snake_case : Optional[Any] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
_snake_case : Dict = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=snake_case_ , use_fast=snake_case_ )
_snake_case : Union[str, Any] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def lowerCamelCase__ ( self ):
try:
AutoConfig.register("custom" , snake_case_ )
AutoFeatureExtractor.register(snake_case_ , snake_case_ )
AutoTokenizer.register(snake_case_ , slow_tokenizer_class=snake_case_ )
AutoProcessor.register(snake_case_ , snake_case_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case_ ):
AutoProcessor.register(snake_case_ , snake_case_ )
# Now that the config is registered, it can be used as any other config with the auto-API
_snake_case : int = CustomFeatureExtractor.from_pretrained(snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_snake_case : Optional[Any] = os.path.join(snake_case_ , "vocab.txt" )
with open(snake_case_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
_snake_case : Optional[Any] = CustomTokenizer(snake_case_ )
_snake_case : List[Any] = CustomProcessor(snake_case_ , snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(snake_case_ )
_snake_case : str = AutoProcessor.from_pretrained(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase__ ( self ):
class _UpperCAmelCase ( _snake_case):
__lowercase : Dict = False
class _UpperCAmelCase ( _snake_case):
__lowercase : Tuple = False
class _UpperCAmelCase ( _snake_case):
__lowercase : Dict = """AutoFeatureExtractor"""
__lowercase : Any = """AutoTokenizer"""
__lowercase : Union[str, Any] = False
try:
AutoConfig.register("custom" , snake_case_ )
AutoFeatureExtractor.register(snake_case_ , snake_case_ )
AutoTokenizer.register(snake_case_ , slow_tokenizer_class=snake_case_ )
AutoProcessor.register(snake_case_ , snake_case_ )
# If remote code is not set, the default is to use local classes.
_snake_case : str = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
_snake_case : str = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=snake_case_ )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
_snake_case : Union[str, Any] = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=snake_case_ )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(processor.__class__.__name__ , "BertTokenizerFast" )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext" )
self.assertEqual(processor.__class__.__name__ , "ConvNextImageProcessor" )
@is_staging_test
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : int = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def lowerCamelCase__ ( cls ):
_snake_case : List[Any] = TOKEN
HfFolder.save_token(snake_case_ )
@classmethod
def lowerCamelCase__ ( cls ):
try:
delete_repo(token=cls._token , repo_id="test-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-processor" )
except HTTPError:
pass
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = WavaVecaProcessor.from_pretrained(snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case_ , "test-processor" ) , push_to_hub=snake_case_ , use_auth_token=self._token )
_snake_case : Optional[Any] = WavaVecaProcessor.from_pretrained(F'{USER}/test-processor' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case_ , getattr(new_processor.feature_extractor , snake_case_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = WavaVecaProcessor.from_pretrained(snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case_ , "test-processor-org" ) , push_to_hub=snake_case_ , use_auth_token=self._token , organization="valid_org" , )
_snake_case : Dict = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case_ , getattr(new_processor.feature_extractor , snake_case_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase__ ( self ):
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
_snake_case : Tuple = CustomFeatureExtractor.from_pretrained(snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_snake_case : Optional[Any] = os.path.join(snake_case_ , "vocab.txt" )
with open(snake_case_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
_snake_case : Optional[Any] = CustomTokenizer(snake_case_ )
_snake_case : Optional[int] = CustomProcessor(snake_case_ , snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F'{USER}/test-dynamic-processor' , token=self._token )
_snake_case : Union[str, Any] = Repository(snake_case_ , clone_from=F'{USER}/test-dynamic-processor' , token=self._token )
processor.save_pretrained(snake_case_ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(snake_case_ , "tokenizer_config.json" ) ) as f:
_snake_case : str = json.load(snake_case_ )
self.assertDictEqual(
tokenizer_config["auto_map"] , {
"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(snake_case_ , "custom_feature_extraction.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case_ , "custom_tokenization.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case_ , "custom_processing.py" ) ) )
repo.push_to_hub()
_snake_case : Tuple = AutoProcessor.from_pretrained(F'{USER}/test-dynamic-processor' , trust_remote_code=snake_case_ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , "CustomProcessor" )
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = ["""MobileViTFeatureExtractor"""]
_a : Union[str, Any] = ["""MobileViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = [
"""MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MobileViTForImageClassification""",
"""MobileViTForSemanticSegmentation""",
"""MobileViTModel""",
"""MobileViTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = [
"""TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFMobileViTForImageClassification""",
"""TFMobileViTForSemanticSegmentation""",
"""TFMobileViTModel""",
"""TFMobileViTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 | 1 |
"""simple docstring"""
from manim import *
class _UpperCAmelCase ( _snake_case):
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Rectangle(height=0.5 , width=0.5 )
_snake_case : Any = Rectangle(height=0.25 , width=0.25 )
_snake_case : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_snake_case : str = [mem.copy() for i in range(6 )]
_snake_case : Dict = [mem.copy() for i in range(6 )]
_snake_case : str = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Any = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Any = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Tuple = Text("CPU" , font_size=24 )
_snake_case : Optional[Any] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case_ )
_snake_case : Tuple = [mem.copy() for i in range(4 )]
_snake_case : List[Any] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Tuple = Text("GPU" , font_size=24 )
_snake_case : Optional[Any] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
gpu.move_to([-1, -1, 0] )
self.add(snake_case_ )
_snake_case : List[Any] = [mem.copy() for i in range(6 )]
_snake_case : Union[str, Any] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Dict = Text("Model" , font_size=24 )
_snake_case : int = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
model.move_to([3, -1.0, 0] )
self.add(snake_case_ )
_snake_case : Union[str, Any] = []
_snake_case : List[Any] = []
_snake_case : Optional[int] = []
for i, rect in enumerate(snake_case_ ):
rect.set_stroke(snake_case_ )
_snake_case : Dict = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(snake_case_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=snake_case_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=snake_case_ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=snake_case_ , buff=0.0 )
self.add(snake_case_ )
model_cpu_arr.append(snake_case_ )
self.add(*snake_case_ , *snake_case_ , *snake_case_ )
_snake_case : int = [mem.copy() for i in range(6 )]
_snake_case : List[str] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : List[str] = Text("Loaded Checkpoint" , font_size=24 )
_snake_case : Optional[Any] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
checkpoint.move_to([3, 0.5, 0] )
self.add(snake_case_ )
_snake_case : Optional[int] = []
_snake_case : List[Any] = []
for i, rect in enumerate(snake_case_ ):
_snake_case : Optional[Any] = fill.copy().set_fill(snake_case_ , opacity=0.7 )
target.move_to(snake_case_ )
ckpt_arr.append(snake_case_ )
_snake_case : List[Any] = 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(snake_case_ )
self.add(*snake_case_ , *snake_case_ )
_snake_case : int = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_snake_case : str = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(snake_case_ , snake_case_ )
_snake_case : Dict = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(snake_case_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(snake_case_ )
_snake_case : 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=24 , )
step_a.move_to([2, 2, 0] )
_snake_case : str = [meta_mem.copy() for i in range(6 )]
_snake_case : Tuple = [meta_mem.copy() for i in range(6 )]
_snake_case : List[Any] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : List[str] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Optional[Any] = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
_snake_case : Any = Text("Disk" , font_size=24 )
_snake_case : Optional[int] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(snake_case_ , run_time=3 ) , Write(snake_case_ , run_time=1 ) , Create(snake_case_ , run_time=1 ) )
_snake_case : Optional[int] = []
for i, rect in enumerate(snake_case_ ):
_snake_case : str = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(snake_case_ , run_time=1.5 ) )
self.play(*snake_case_ )
self.play(FadeOut(snake_case_ ) )
_snake_case : Union[str, Any] = MarkupText(F'Then, the checkpoint is removed from memory\nthrough garbage collection.' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ , run_time=3 ) )
self.play(
FadeOut(snake_case_ , snake_case_ , *snake_case_ , *snake_case_ ) , )
self.wait()
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_a : Optional[int] = 16
_a : int = 32
def a__ ( a : Accelerator , a : int = 16 ):
"""simple docstring"""
_snake_case : Union[str, Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
_snake_case : Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(a : str ):
# max_length=None => use the model max length (it's actually the default)
_snake_case : List[Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=a , max_length=a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_snake_case : List[Any] = datasets.map(
a , batched=a , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_snake_case : Optional[int] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(a : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_snake_case : List[Any] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_snake_case : List[str] = 16
elif accelerator.mixed_precision != "no":
_snake_case : List[Any] = 8
else:
_snake_case : Any = None
return tokenizer.pad(
a , padding="longest" , max_length=a , pad_to_multiple_of=a , return_tensors="pt" , )
# Instantiate dataloaders.
_snake_case : Any = DataLoader(
tokenized_datasets["train"] , shuffle=a , collate_fn=a , batch_size=a )
_snake_case : Union[str, Any] = DataLoader(
tokenized_datasets["validation"] , shuffle=a , collate_fn=a , batch_size=a )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_a : Union[str, Any] = mocked_dataloaders # noqa: F811
def a__ ( a : Optional[Any] , a : Tuple ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , a ) == "1":
_snake_case : List[Any] = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_snake_case : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
_snake_case : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_snake_case : Any = config["lr"]
_snake_case : List[str] = int(config["num_epochs"] )
_snake_case : List[Any] = int(config["seed"] )
_snake_case : Tuple = int(config["batch_size"] )
set_seed(a )
_snake_case , _snake_case : Union[str, Any] = get_dataloaders(a , a )
_snake_case : List[str] = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
_snake_case : Optional[int] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_snake_case : List[Any] = batch_size // MAX_GPU_BATCH_SIZE
_snake_case : Optional[Any] = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_snake_case : Optional[Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=a )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_snake_case : Tuple = model.to(accelerator.device )
# Instantiate optimizer
_snake_case : Optional[Any] = AdamW(params=model.parameters() , lr=a )
# Instantiate scheduler
_snake_case : int = get_linear_schedule_with_warmup(
optimizer=a , num_warmup_steps=100 , num_training_steps=(len(a ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case : List[Any] = accelerator.prepare(
a , a , a , a , a )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_snake_case : Any = os.path.split(a )[-1].split("." )[0]
accelerator.init_trackers(a , a )
# Now we train the model
for epoch in range(a ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_snake_case : Union[str, Any] = 0
for step, batch in enumerate(a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_snake_case : Any = model(**a )
_snake_case : List[Any] = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_snake_case : int = loss / gradient_accumulation_steps
accelerator.backward(a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_snake_case : str = model(**a )
_snake_case : List[str] = outputs.logits.argmax(dim=-1 )
_snake_case , _snake_case : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=a , references=a , )
_snake_case : Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , a )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(a ),
"epoch": epoch,
} , step=a , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def a__ ( ):
"""simple docstring"""
_snake_case : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=a , default=a , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=a , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
_snake_case : str = parser.parse_args()
_snake_case : List[Any] = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(a , a )
if __name__ == "__main__":
main()
| 87 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
_a : Optional[Any] = [True] * 1_000_001
_a : Optional[Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_a : Optional[int] = False
i += 1
def a__ ( a : int ):
"""simple docstring"""
return seive[n]
def a__ ( a : int ):
"""simple docstring"""
return any(digit in "02468" for digit in str(a ) )
def a__ ( a : int = 1_000_000 ):
"""simple docstring"""
_snake_case : Union[str, Any] = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(a ) and not contains_an_even_digit(a ):
_snake_case : Any = str(a )
_snake_case : int = [int(str_num[j:] + str_num[:j] ) for j in range(len(a ) )]
if all(is_prime(a ) for i in list_nums ):
result.append(a )
return result
def a__ ( ):
"""simple docstring"""
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 87 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_snake_case : List[Any] = Vector()
def lowerCamelCase__ ( self ):
_snake_case : Any = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case_ ) , "(0,0,0,0,0,1)" )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case_ ) , 4 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2] )
_snake_case : List[str] = Vector([1, 2, 3, 4, 5] )
_snake_case : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_snake_case : Any = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
_snake_case : Any = 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 lowerCamelCase__ ( self ):
_snake_case : str = Vector([1, 2, 3] )
_snake_case : Union[str, Any] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Vector([1, 2, 3] )
_snake_case : List[Any] = Vector([2, -1, 4] ) # for test of dot product
_snake_case : Union[str, Any] = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Vector([1, 2, 3] )
_snake_case : Optional[Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case_ , snake_case_ ) ) , "(3,4,7)" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Vector([1, 0, 0, 0, 0, 0] )
_snake_case : Optional[int] = x.copy()
self.assertEqual(str(snake_case_ ) , str(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case_ ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : str = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : str = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self ):
_snake_case : str = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_snake_case : List[str] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : int = 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 lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = 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 lowerCamelCase__ ( 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()
| 87 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_a : Optional[int] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a : List[str] = parser.parse_args()
if args.model_type == "roberta":
_a : List[Any] = RobertaForMaskedLM.from_pretrained(args.model_name)
_a : Tuple = """roberta"""
elif args.model_type == "gpt2":
_a : List[str] = GPTaLMHeadModel.from_pretrained(args.model_name)
_a : List[str] = """transformer"""
_a : Dict = model.state_dict()
_a : str = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
_a : Any = state_dict[f'{prefix}.{param_name}']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
_a : int = f'{prefix}.embeddings.{w}.weight'
_a : Optional[int] = state_dict[param_name]
for w in ["weight", "bias"]:
_a : List[str] = f'{prefix}.embeddings.LayerNorm.{w}'
_a : List[Any] = state_dict[param_name]
# Transformer Blocks #
_a : Tuple = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
_a : Tuple = state_dict[
f'{prefix}.h.{teacher_idx}.{layer}.{w}'
]
_a : Optional[int] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
_a : str = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
_a : Union[str, Any] = state_dict[f'{layer}']
if args.vocab_transform:
for w in ["weight", "bias"]:
_a : List[str] = state_dict[f'lm_head.dense.{w}']
_a : Optional[int] = state_dict[f'lm_head.layer_norm.{w}']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
_a : int = state_dict[f'{prefix}.ln_f.{w}']
_a : int = state_dict["""lm_head.weight"""]
print(f'N layers selected for distillation: {std_idx}')
print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 87 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( a : float , a : float , a : float ):
"""simple docstring"""
_snake_case : Optional[Any] = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import math
from collections.abc import Callable
def a__ ( a : Callable[[float], float] , a : float , a : float ):
"""simple docstring"""
_snake_case : float = xa
_snake_case : float = xa
while True:
if x_n == x_na or function(a ) == function(a ):
raise ZeroDivisionError("float division by zero, could not find root" )
_snake_case : float = x_na - (
function(a ) / ((function(a ) - function(a )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
_snake_case : Optional[Any] = x_na
_snake_case : List[Any] = x_na
def a__ ( a : float ):
"""simple docstring"""
return math.pow(a , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 87 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Any = TextToVideoSDPipeline
__lowercase : str = TEXT_TO_IMAGE_PARAMS
__lowercase : int = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__lowercase : Optional[int] = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : str = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case : List[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , )
torch.manual_seed(0 )
_snake_case : Union[str, Any] = 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 )
_snake_case : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
_snake_case : Tuple = CLIPTextModel(snake_case_ )
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : str = torch.manual_seed(snake_case_ )
else:
_snake_case : Union[str, Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[Any] = self.get_dummy_components()
_snake_case : Tuple = TextToVideoSDPipeline(**snake_case_ )
_snake_case : List[str] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = self.get_dummy_inputs(snake_case_ )
_snake_case : Union[str, Any] = "np"
_snake_case : Dict = sd_pipe(**snake_case_ ).frames
_snake_case : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_snake_case : Dict = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=1E-2 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy" )
_snake_case : int = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_snake_case : Tuple = pipe.to("cuda" )
_snake_case : List[Any] = "Spiderman is surfing"
_snake_case : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : int = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="pt" ).frames
_snake_case : int = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def lowerCamelCase__ ( self ):
_snake_case : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy" )
_snake_case : str = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : int = pipe.to("cuda" )
_snake_case : Any = "Spiderman is surfing"
_snake_case : str = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : Any = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=2 , output_type="pt" ).frames
_snake_case : Optional[int] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 87 | 1 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_a : Dict = """scheduler_config.json"""
class _UpperCAmelCase ( _snake_case):
__lowercase : Tuple = 1
__lowercase : Any = 2
__lowercase : Optional[int] = 3
__lowercase : Optional[int] = 4
__lowercase : int = 5
@dataclass
class _UpperCAmelCase ( _snake_case):
__lowercase : jnp.ndarray
class _UpperCAmelCase :
__lowercase : Dict = SCHEDULER_CONFIG_NAME
__lowercase : Optional[int] = ["""dtype"""]
__lowercase : List[Any] = []
__lowercase : List[Any] = True
@classmethod
def lowerCamelCase__ ( cls , snake_case_ = None , snake_case_ = None , snake_case_=False , **snake_case_ , ):
_snake_case , _snake_case : Optional[int] = cls.load_config(
pretrained_model_name_or_path=snake_case_ , subfolder=snake_case_ , return_unused_kwargs=snake_case_ , **snake_case_ , )
_snake_case , _snake_case : Dict = cls.from_config(snake_case_ , return_unused_kwargs=snake_case_ , **snake_case_ )
if hasattr(snake_case_ , "create_state" ) and getattr(snake_case_ , "has_state" , snake_case_ ):
_snake_case : Optional[Any] = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = False , **snake_case_ ):
self.save_config(save_directory=snake_case_ , push_to_hub=snake_case_ , **snake_case_ )
@property
def lowerCamelCase__ ( self ):
return self._get_compatibles()
@classmethod
def lowerCamelCase__ ( cls ):
_snake_case : Union[str, Any] = list(set([cls.__name__] + cls._compatibles ) )
_snake_case : List[Any] = importlib.import_module(__name__.split("." )[0] )
_snake_case : Optional[int] = [
getattr(snake_case_ , snake_case_ ) for c in compatible_classes_str if hasattr(snake_case_ , snake_case_ )
]
return compatible_classes
def a__ ( a : jnp.ndarray , a : Tuple[int] ):
"""simple docstring"""
assert len(a ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a ) - x.ndim) ) , a )
def a__ ( a : int , a : str=0.999 , a : Optional[Any]=jnp.floataa ):
"""simple docstring"""
def alpha_bar(a : Optional[int] ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
_snake_case : Tuple = []
for i in range(a ):
_snake_case : Tuple = i / num_diffusion_timesteps
_snake_case : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(a ) / alpha_bar(a ) , a ) )
return jnp.array(a , dtype=a )
@flax.struct.dataclass
class _UpperCAmelCase :
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
@classmethod
def lowerCamelCase__ ( cls , snake_case_ ):
_snake_case : int = scheduler.config
if config.trained_betas is not None:
_snake_case : str = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
_snake_case : Optional[Any] = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_snake_case : Optional[Any] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_snake_case : Optional[Any] = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
_snake_case : List[str] = 1.0 - betas
_snake_case : List[Any] = jnp.cumprod(snake_case_ , axis=0 )
return cls(
alphas=snake_case_ , betas=snake_case_ , alphas_cumprod=snake_case_ , )
def a__ ( a : CommonSchedulerState , a : jnp.ndarray , a : jnp.ndarray , a : jnp.ndarray ):
"""simple docstring"""
_snake_case : Optional[int] = state.alphas_cumprod
_snake_case : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
_snake_case : Tuple = sqrt_alpha_prod.flatten()
_snake_case : Any = broadcast_to_shape_from_left(a , original_samples.shape )
_snake_case : Any = (1 - alphas_cumprod[timesteps]) ** 0.5
_snake_case : List[Any] = sqrt_one_minus_alpha_prod.flatten()
_snake_case : List[str] = broadcast_to_shape_from_left(a , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def a__ ( a : CommonSchedulerState , a : jnp.ndarray , a : jnp.ndarray , a : jnp.ndarray ):
"""simple docstring"""
_snake_case , _snake_case : Any = get_sqrt_alpha_prod(a , a , a , a )
_snake_case : List[Any] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def a__ ( a : CommonSchedulerState , a : jnp.ndarray , a : jnp.ndarray , a : jnp.ndarray ):
"""simple docstring"""
_snake_case , _snake_case : int = get_sqrt_alpha_prod(a , a , a , a )
_snake_case : Any = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 87 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """EncodecFeatureExtractor"""
__lowercase : str = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
_snake_case : Dict = self.feature_extractor
_snake_case : Any = False
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ , language=snake_case_ , no_timestamps=snake_case_ )
def __call__( self , *snake_case_ , **snake_case_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
_snake_case : str = kwargs.pop("audio" , snake_case_ )
_snake_case : Optional[int] = kwargs.pop("sampling_rate" , snake_case_ )
_snake_case : Optional[Any] = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Any = args[0]
_snake_case : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_snake_case : Any = self.tokenizer(snake_case_ , **snake_case_ )
if audio is not None:
_snake_case : Any = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_snake_case : str = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_snake_case : List[str] = audio_inputs["padding_mask"]
return inputs
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
_snake_case : Tuple = kwargs.pop("audio" , snake_case_ )
_snake_case : List[str] = kwargs.pop("padding_mask" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Tuple = args[0]
_snake_case : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(snake_case_ , padding_mask=snake_case_ )
else:
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = to_numpy(snake_case_ )
_snake_case , _snake_case , _snake_case : Tuple = audio_values.shape
if padding_mask is None:
return list(snake_case_ )
_snake_case : Optional[int] = to_numpy(snake_case_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_snake_case : Any = seq_len - padding_mask.shape[-1]
_snake_case : Optional[Any] = 1 - self.feature_extractor.padding_value
_snake_case : Optional[int] = np.pad(snake_case_ , ((0, 0), (0, difference)) , "constant" , constant_values=snake_case_ )
_snake_case : Any = audio_values.tolist()
for i in range(snake_case_ ):
_snake_case : Tuple = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_snake_case : Tuple = sliced_audio.reshape(snake_case_ , -1 )
return audio_values
| 87 | 1 |
"""simple docstring"""
def a__ ( a : str ):
"""simple docstring"""
return "".join(chr(ord(a ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""YolosFeatureExtractor"""]
_a : List[Any] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=14 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : List[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Union[str, Any] = seq_length
_snake_case : str = is_training
_snake_case : Union[str, Any] = use_token_type_ids
_snake_case : Optional[int] = use_input_mask
_snake_case : List[str] = use_labels
_snake_case : Optional[int] = use_mc_token_ids
_snake_case : Optional[int] = vocab_size
_snake_case : Optional[int] = hidden_size
_snake_case : str = num_hidden_layers
_snake_case : str = num_attention_heads
_snake_case : List[str] = intermediate_size
_snake_case : Any = hidden_act
_snake_case : Optional[int] = hidden_dropout_prob
_snake_case : Tuple = attention_probs_dropout_prob
_snake_case : Union[str, Any] = max_position_embeddings
_snake_case : List[str] = type_vocab_size
_snake_case : Optional[Any] = type_sequence_label_size
_snake_case : Optional[Any] = initializer_range
_snake_case : Optional[int] = num_labels
_snake_case : List[Any] = num_choices
_snake_case : Any = scope
_snake_case : Optional[Any] = self.vocab_size - 1
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : List[str] = None
if self.use_input_mask:
_snake_case : Dict = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : int = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : List[str] = None
if self.use_mc_token_ids:
_snake_case : Dict = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_snake_case : int = None
_snake_case : List[str] = None
_snake_case : Optional[Any] = None
if self.use_labels:
_snake_case : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = self.get_config()
_snake_case : Dict = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase__ ( self ):
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ ):
_snake_case : Dict = CTRLModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
model(snake_case_ , token_type_ids=snake_case_ , head_mask=snake_case_ )
model(snake_case_ , token_type_ids=snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ ):
_snake_case : int = CTRLLMHeadModel(snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Optional[Any] = model(snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Optional[int] = config_and_inputs
_snake_case : Optional[int] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
return config, inputs_dict
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ ):
_snake_case : int = self.num_labels
_snake_case : Any = CTRLForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Dict = model(snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class _UpperCAmelCase ( _snake_case , _snake_case , _snake_case , unittest.TestCase):
__lowercase : Any = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
__lowercase : Optional[int] = (CTRLLMHeadModel,) if is_torch_available() else ()
__lowercase : Optional[int] = (
{
"""feature-extraction""": CTRLModel,
"""text-classification""": CTRLForSequenceClassification,
"""text-generation""": CTRLLMHeadModel,
"""zero-shot""": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowercase : Any = True
__lowercase : List[Any] = False
__lowercase : List[str] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = CTRLModelTester(self )
_snake_case : Optional[int] = ConfigTester(self , config_class=snake_case_ , n_embd=37 )
def lowerCamelCase__ ( self ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case_ )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCamelCase__ ( self ):
pass
@slow
def lowerCamelCase__ ( self ):
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Union[str, Any] = CTRLModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@unittest.skip("The model doesn't support left padding" ) # and it's not used enough to be worth fixing :)
def lowerCamelCase__ ( self ):
pass
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = CTRLLMHeadModel.from_pretrained("ctrl" )
model.to(snake_case_ )
_snake_case : Any = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=snake_case_ ) # Legal the president is
_snake_case : List[str] = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_snake_case : List[Any] = model.generate(snake_case_ , do_sample=snake_case_ )
self.assertListEqual(output_ids[0].tolist() , snake_case_ )
| 87 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = dataset
_snake_case : str = process
_snake_case : int = params
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
_snake_case : Union[str, Any] = self.dataset[i]
_snake_case : Optional[Any] = self.process(snake_case_ , **self.params )
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
_snake_case : Union[str, Any] = loader
_snake_case : Tuple = infer
_snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_snake_case : int = None
_snake_case : int = loader_batch_size
# Internal bookkeeping
_snake_case : Any = None
_snake_case : Dict = None
def __len__( self ):
return len(self.loader )
def __iter__( self ):
_snake_case : int = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_snake_case : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
_snake_case : Tuple = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_snake_case : Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : int = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_snake_case : Tuple = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : Tuple = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
_snake_case : Tuple = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : List[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : Union[str, Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_snake_case : List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_snake_case : int = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def lowerCamelCase__ ( self ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_snake_case : Tuple = next(self.iterator )
_snake_case : Any = self.infer(snake_case_ , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Optional[int] = list(processed.keys() )[0]
_snake_case : List[str] = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = len(snake_case_ )
else:
_snake_case : Optional[int] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_snake_case : str = processed
_snake_case : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
_snake_case : Tuple = iter(self.loader )
_snake_case : List[Any] = None
return self
def lowerCamelCase__ ( self ):
if self.subiterator is None:
_snake_case : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_snake_case : Union[str, Any] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_snake_case : str = self.infer(next(self.iterator ) , **self.params )
_snake_case : Tuple = next(self.subiterator )
return processed
class _UpperCAmelCase ( _snake_case):
def __iter__( self ):
_snake_case : Optional[Any] = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
_snake_case : Optional[Any] = False
_snake_case : Tuple = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : str = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
_snake_case : List[str] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Tuple = list(processed.keys() )[0]
_snake_case : Tuple = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Any = len(snake_case_ )
else:
_snake_case : List[Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Dict = observed_batch_size
_snake_case : List[Any] = processed
_snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : int = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
_snake_case : Dict = processed
_snake_case : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ ):
_snake_case : str = dataset
_snake_case : Any = key
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return self.dataset[i][self.key]
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = dataset
_snake_case : Any = keya
_snake_case : int = keya
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 87 | 1 |
"""simple docstring"""
_a : Dict = {
"""Pillow""": """Pillow""",
"""accelerate""": """accelerate>=0.11.0""",
"""compel""": """compel==0.1.8""",
"""black""": """black~=23.1""",
"""datasets""": """datasets""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1""",
"""hf-doc-builder""": """hf-doc-builder>=0.3.0""",
"""huggingface-hub""": """huggingface-hub>=0.13.2""",
"""requests-mock""": """requests-mock==1.10.0""",
"""importlib_metadata""": """importlib_metadata""",
"""invisible-watermark""": """invisible-watermark""",
"""isort""": """isort>=5.5.4""",
"""jax""": """jax>=0.2.8,!=0.3.2""",
"""jaxlib""": """jaxlib>=0.1.65""",
"""Jinja2""": """Jinja2""",
"""k-diffusion""": """k-diffusion>=0.0.12""",
"""torchsde""": """torchsde""",
"""note_seq""": """note_seq""",
"""librosa""": """librosa""",
"""numpy""": """numpy""",
"""omegaconf""": """omegaconf""",
"""parameterized""": """parameterized""",
"""protobuf""": """protobuf>=3.20.3,<4""",
"""pytest""": """pytest""",
"""pytest-timeout""": """pytest-timeout""",
"""pytest-xdist""": """pytest-xdist""",
"""ruff""": """ruff>=0.0.241""",
"""safetensors""": """safetensors""",
"""sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
"""scipy""": """scipy""",
"""onnx""": """onnx""",
"""regex""": """regex!=2019.12.17""",
"""requests""": """requests""",
"""tensorboard""": """tensorboard""",
"""torch""": """torch>=1.4""",
"""torchvision""": """torchvision""",
"""transformers""": """transformers>=4.25.1""",
"""urllib3""": """urllib3<=2.0.0""",
}
| 87 |
"""simple docstring"""
def a__ ( a : int ):
"""simple docstring"""
if not isinstance(a , a ):
raise TypeError("Input value must be an 'int' type" )
_snake_case : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Optional[Any] = LayoutLMTokenizer
__lowercase : List[Any] = LayoutLMTokenizerFast
__lowercase : str = True
__lowercase : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_snake_case : Dict = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_snake_case : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def lowerCamelCase__ ( self , **snake_case_ ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : Dict = "UNwant\u00E9d,running"
_snake_case : List[Any] = "unwanted, running"
return input_text, output_text
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.tokenizer_class(self.vocab_file )
_snake_case : Tuple = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(snake_case_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , [7, 4, 5, 10, 8, 9] )
def lowerCamelCase__ ( self ):
pass
| 87 |
"""simple docstring"""
from __future__ import annotations
import requests
_a : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( a : str , a : int = 1 , a : str = "new" , a : list | None = None ):
"""simple docstring"""
_snake_case : Any = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(a ) - valid_terms ) ):
_snake_case : Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(a )
_snake_case : int = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"User-agent": "A random string"} , )
if response.status_code == 429:
raise requests.HTTPError
_snake_case : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(a )}
_snake_case : Tuple = {}
for id_ in range(a ):
_snake_case : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 87 | 1 |
"""simple docstring"""
import os
from math import logaa
def a__ ( a : str = "base_exp.txt" ):
"""simple docstring"""
_snake_case : float = 0
_snake_case : int = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a ) , a ) ) ):
_snake_case , _snake_case : List[str] = list(map(a , line.split("," ) ) )
if x * logaa(a ) > largest:
_snake_case : List[Any] = x * logaa(a )
_snake_case : Union[str, Any] = i + 1
return result
if __name__ == "__main__":
print(solution())
| 87 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(a ), magnitude * sin(a )]
return [magnitude * cos(radians(a ) ), magnitude * sin(radians(a ) )]
def a__ ( a : NDArray[floataa] , a : NDArray[floataa] , a : float = 10**-1 ):
"""simple docstring"""
_snake_case : NDArray[floataa] = cross(a , a )
_snake_case : float = sum(a )
return abs(a ) < eps
if __name__ == "__main__":
# Test to check if it works
_a : Tuple = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
_a : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_a : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
_a : List[Any] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_a : List[str] = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
_a : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_a : Optional[Any] = logging.get_logger(__name__)
def a__ ( a : Optional[int] ):
"""simple docstring"""
_snake_case : Any = torch.load(a , map_location="cpu" )
if "model" in sd.keys():
_snake_case : Union[str, Any] = torch.load(a , map_location="cpu" )["model"]
# pop unnecessary weights
_snake_case : str = [
"decoder.version",
"decoder.output_projection.weight",
]
for key in keys_to_delete:
if key in sd:
sd.pop(a )
_snake_case : Dict = {
"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:
_snake_case : Union[str, Any] = sd.pop(a )
_snake_case : Optional[int] = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
_snake_case : List[Any] = sd[key]
# We split QKV in separate Q,K,V
_snake_case : List[str] = key.replace(".qkv_proj." , ".q_proj." )
_snake_case : Any = key.replace(".qkv_proj." , ".k_proj." )
_snake_case : Dict = key.replace(".qkv_proj." , ".v_proj." )
_snake_case : Dict = 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
_snake_case , _snake_case , _snake_case : int = torch.split(a , depth // 3 , dim=0 )
_snake_case : Dict = q
_snake_case : Dict = k
_snake_case : Optional[Any] = v
del sd[key]
return sd
@torch.no_grad()
def a__ ( a : List[Any] , a : int , a : str=None ):
"""simple docstring"""
_snake_case : Optional[Any] = load_checkpoint(a )
if config is not None:
_snake_case : Optional[int] = OPTConfig.from_pretrained(a )
else:
_snake_case : List[Any] = OPTConfig()
_snake_case : Optional[Any] = OPTModel(a ).half().eval()
model.load_state_dict(a )
# Check results
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_a : Any = 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.""")
_a : Optional[Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """openai-gpt"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=4_04_78 , snake_case_=5_12 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_="cls_index" , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=0.1 , **snake_case_ , ):
_snake_case : Tuple = vocab_size
_snake_case : Dict = n_positions
_snake_case : Any = n_embd
_snake_case : Any = n_layer
_snake_case : Optional[int] = n_head
_snake_case : Union[str, Any] = afn
_snake_case : Dict = resid_pdrop
_snake_case : str = embd_pdrop
_snake_case : Union[str, Any] = attn_pdrop
_snake_case : str = layer_norm_epsilon
_snake_case : Union[str, Any] = initializer_range
_snake_case : Any = summary_type
_snake_case : List[str] = summary_use_proj
_snake_case : Optional[int] = summary_activation
_snake_case : Union[str, Any] = summary_first_dropout
_snake_case : Optional[int] = summary_proj_to_labels
super().__init__(**snake_case_ )
| 87 | 1 |
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _UpperCAmelCase ( _snake_case):
__lowercase : Dict = """philschmid/bart-large-cnn-samsum"""
__lowercase : Tuple = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
__lowercase : Any = """summarizer"""
__lowercase : List[Any] = AutoTokenizer
__lowercase : Optional[int] = AutoModelForSeqaSeqLM
__lowercase : int = ["""text"""]
__lowercase : List[str] = ["""text"""]
def lowerCamelCase__ ( self , snake_case_ ):
return self.pre_processor(snake_case_ , return_tensors="pt" , truncation=snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
return self.model.generate(**snake_case_ )[0]
def lowerCamelCase__ ( self , snake_case_ ):
return self.pre_processor.decode(snake_case_ , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
| 87 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_a : int = []
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}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_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'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""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"""),
]
)
def a__ ( a : List[str] , a : int , a : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = state_dict.pop(a )
_snake_case : Union[str, Any] = val
def a__ ( a : Tuple ):
"""simple docstring"""
_snake_case : Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_snake_case : Dict = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
_snake_case : Tuple = value
else:
_snake_case : Dict = value
return new_state_dict
def a__ ( a : int ):
"""simple docstring"""
_snake_case : Any = ""
# 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)
_snake_case : str = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : str = 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
_snake_case : int = in_proj_weight[:256, :]
_snake_case : List[str] = in_proj_bias[:256]
_snake_case : Optional[Any] = in_proj_weight[256:512, :]
_snake_case : List[str] = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : Dict = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_snake_case : List[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_snake_case : Union[str, Any] = in_proj_weight[:256, :]
_snake_case : Tuple = in_proj_bias[:256]
_snake_case : int = in_proj_weight[256:512, :]
_snake_case : int = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : str = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_snake_case : Dict = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
_snake_case : Optional[int] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_snake_case : Dict = in_proj_weight_cross_attn[:256, :]
_snake_case : Any = in_proj_bias_cross_attn[:256]
_snake_case : Union[str, Any] = in_proj_weight_cross_attn[256:512, :]
_snake_case : Optional[int] = in_proj_bias_cross_attn[256:512]
_snake_case : Any = in_proj_weight_cross_attn[-256:, :]
_snake_case : str = in_proj_bias_cross_attn[-256:]
def a__ ( a : str , a : int ):
"""simple docstring"""
_snake_case , _snake_case : List[str] = image.size
_snake_case : Dict = max(a , a )
_snake_case : Union[str, Any] = 800 if "detection" in checkpoint_url else 1_000
_snake_case : Any = target_max_size / current_max_size
_snake_case : str = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( a : str ):
"""simple docstring"""
_snake_case : str = F.to_tensor(a )
_snake_case : Union[str, Any] = F.normalize(a , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( a : Optional[Any] , a : Any , a : Union[str, Any] ):
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
_snake_case : Tuple = torch.hub.load_state_dict_from_url(a , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(a , a , a )
_snake_case : Union[str, Any] = rename_backbone_keys(a )
# query, key and value matrices need special treatment
read_in_q_k_v(a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_snake_case : int = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_snake_case : Optional[int] = state_dict.pop(a )
_snake_case : Any = val
# create HuggingFace model and load state dict
_snake_case : Tuple = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
_snake_case : Any = 15
_snake_case : int = 2
_snake_case : Optional[Any] = {0: "table", 1: "table rotated"}
_snake_case : Union[str, Any] = idalabel
_snake_case : Union[str, Any] = {v: k for k, v in idalabel.items()}
else:
_snake_case : Any = 125
_snake_case : Union[str, Any] = 6
_snake_case : List[str] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
_snake_case : Any = idalabel
_snake_case : Optional[int] = {v: k for k, v in idalabel.items()}
_snake_case : Union[str, Any] = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1_000 )
_snake_case : str = TableTransformerForObjectDetection(a )
model.load_state_dict(a )
model.eval()
# verify our conversion
_snake_case : Optional[int] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
_snake_case : Optional[Any] = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=a )
_snake_case : Dict = Image.open(a ).convert("RGB" )
_snake_case : Union[str, Any] = normalize(resize(a , a ) ).unsqueeze(0 )
_snake_case : str = model(a )
if "detection" in checkpoint_url:
_snake_case : int = (1, 15, 3)
_snake_case : List[str] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
_snake_case : List[str] = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
_snake_case : Union[str, Any] = (1, 125, 7)
_snake_case : str = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
_snake_case : Optional[Any] = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
_snake_case : int = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(a )
image_processor.push_to_hub(a )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint 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."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : Any = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=3 , snake_case_=32 , snake_case_=3 , snake_case_=10 , snake_case_=[10, 20, 30, 40] , snake_case_=[1, 1, 2, 1] , snake_case_=True , snake_case_=True , snake_case_="relu" , snake_case_=3 , snake_case_=None , ):
_snake_case : List[str] = parent
_snake_case : List[Any] = batch_size
_snake_case : List[Any] = image_size
_snake_case : Optional[Any] = num_channels
_snake_case : Optional[int] = embeddings_size
_snake_case : Optional[int] = hidden_sizes
_snake_case : List[str] = depths
_snake_case : List[str] = is_training
_snake_case : Any = use_labels
_snake_case : Optional[int] = hidden_act
_snake_case : str = num_labels
_snake_case : Tuple = scope
_snake_case : Union[str, Any] = len(snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : Dict = None
if self.use_labels:
_snake_case : Any = ids_tensor([self.batch_size] , self.num_labels )
_snake_case : Dict = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFRegNetModel(config=snake_case_ )
_snake_case : int = model(snake_case_ , training=snake_case_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Tuple = self.num_labels
_snake_case : Tuple = TFRegNetForImageClassification(snake_case_ )
_snake_case : str = model(snake_case_ , labels=snake_case_ , training=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case : str = config_and_inputs
_snake_case : Optional[int] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : List[str] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
__lowercase : str = (
{"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
__lowercase : Optional[int] = False
__lowercase : List[Any] = False
__lowercase : str = False
__lowercase : Optional[Any] = False
__lowercase : Dict = False
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = TFRegNetModelTester(self )
_snake_case : Union[str, Any] = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowerCamelCase__ ( self ):
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def lowerCamelCase__ ( self ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def lowerCamelCase__ ( self ):
super().test_keras_fit()
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : List[str] = model_class(snake_case_ )
_snake_case : Tuple = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : Union[str, Any] = [*signature.parameters.keys()]
_snake_case : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = model_class(snake_case_ )
_snake_case : str = model(**self._prepare_for_class(snake_case_ , snake_case_ ) , training=snake_case_ )
_snake_case : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case : Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(snake_case_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_snake_case , _snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case : str = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_snake_case : Optional[Any] = layer_type
_snake_case : Tuple = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case : int = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(snake_case_ , snake_case_ , snake_case_ , snake_case_={} ):
_snake_case : List[str] = model(snake_case_ , return_dict=snake_case_ , **snake_case_ )
_snake_case : Optional[int] = model(snake_case_ , return_dict=snake_case_ , **snake_case_ ).to_tuple()
def recursive_check(snake_case_ , snake_case_ ):
if isinstance(snake_case_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(snake_case_ , snake_case_ ):
recursive_check(snake_case_ , snake_case_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(snake_case_ , snake_case_ ) ) , msg=(
"Tuple and dict output are not equal. Difference:"
F' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'
) , )
recursive_check(snake_case_ , snake_case_ )
for model_class in self.all_model_classes:
_snake_case : int = model_class(snake_case_ )
_snake_case : Optional[int] = self._prepare_for_class(snake_case_ , snake_case_ )
_snake_case : Dict = self._prepare_for_class(snake_case_ , snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ )
_snake_case : Tuple = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_snake_case : str = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ )
_snake_case : List[str] = self._prepare_for_class(snake_case_ , snake_case_ )
_snake_case : str = self._prepare_for_class(snake_case_ , snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ , {"output_hidden_states": True} )
_snake_case : Tuple = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_snake_case : List[Any] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ , {"output_hidden_states": True} )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Tuple = TFRegNetModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def a__ ( ):
"""simple docstring"""
_snake_case : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase):
@cached_property
def lowerCamelCase__ ( self ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_snake_case : Union[str, Any] = self.default_image_processor
_snake_case : List[Any] = prepare_img()
_snake_case : List[str] = image_processor(images=snake_case_ , return_tensors="tf" )
# forward pass
_snake_case : Union[str, Any] = model(**snake_case_ , training=snake_case_ )
# verify the logits
_snake_case : Union[str, Any] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_snake_case : Dict = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , snake_case_ , atol=1E-4 )
| 87 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 | 1 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_a : Dict = logging.get_logger(__name__)
@add_end_docstrings(_snake_case)
class _UpperCAmelCase ( _snake_case):
def __init__( self , *snake_case_ , **snake_case_ ):
super().__init__(*snake_case_ , **snake_case_ )
requires_backends(self , "decord" )
self.check_model_type(snake_case_ )
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=None ):
_snake_case : Optional[Any] = {}
if frame_sampling_rate is not None:
_snake_case : Dict = frame_sampling_rate
if num_frames is not None:
_snake_case : List[str] = num_frames
_snake_case : Dict = {}
if top_k is not None:
_snake_case : Any = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , snake_case_ , **snake_case_ ):
return super().__call__(snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_=None , snake_case_=1 ):
if num_frames is None:
_snake_case : Optional[int] = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
_snake_case : str = BytesIO(requests.get(snake_case_ ).content )
_snake_case : Optional[int] = VideoReader(snake_case_ )
videoreader.seek(0 )
_snake_case : List[Any] = 0
_snake_case : Optional[int] = num_frames * frame_sampling_rate - 1
_snake_case : Union[str, Any] = np.linspace(snake_case_ , snake_case_ , num=snake_case_ , dtype=np.intaa )
_snake_case : Optional[int] = videoreader.get_batch(snake_case_ ).asnumpy()
_snake_case : Union[str, Any] = list(snake_case_ )
_snake_case : str = self.image_processor(snake_case_ , return_tensors=self.framework )
return model_inputs
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : List[Any] = self.model(**snake_case_ )
return model_outputs
def lowerCamelCase__ ( self , snake_case_ , snake_case_=5 ):
if top_k > self.model.config.num_labels:
_snake_case : Tuple = self.model.config.num_labels
if self.framework == "pt":
_snake_case : List[str] = model_outputs.logits.softmax(-1 )[0]
_snake_case , _snake_case : Optional[Any] = probs.topk(snake_case_ )
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
_snake_case : List[Any] = scores.tolist()
_snake_case : Any = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_ , snake_case_ )]
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87 | 1 |
"""simple docstring"""
def a__ ( a : int = 1_000 ):
"""simple docstring"""
return sum(e for e in range(3 , a ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'{solution() = }')
| 87 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_a : int = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _UpperCAmelCase ( _snake_case):
@staticmethod
def lowerCamelCase__ ( snake_case_ ):
_snake_case : Dict = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=snake_case_ , required=snake_case_ , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=snake_case_ , required=snake_case_ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=snake_case_ , required=snake_case_ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=snake_case_ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=snake_case_ , default=snake_case_ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : str = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
_snake_case : Optional[int] = model_type
_snake_case : Any = tf_checkpoint
_snake_case : Optional[int] = pytorch_dump_output
_snake_case : Tuple = config
_snake_case : Tuple = finetuning_task_name
def lowerCamelCase__ ( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case : int = self._tf_checkpoint
_snake_case : Optional[Any] = ""
else:
_snake_case : Optional[int] = self._tf_checkpoint
_snake_case : List[str] = ""
convert_transfo_xl_checkpoint_to_pytorch(
snake_case_ , self._config , self._pytorch_dump_output , snake_case_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 87 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : List[Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( _snake_case):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
"The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use SegformerImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 87 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
_a : Dict = 10
def a__ ( a : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Optional[int] = max(a )
while placement <= max_digit:
# declare and initialize empty buckets
_snake_case : list[list] = [[] for _ in range(a )]
# split list_of_ints between the buckets
for i in list_of_ints:
_snake_case : Tuple = int((i / placement) % RADIX )
buckets[tmp].append(a )
# put each buckets' contents into list_of_ints
_snake_case : List[str] = 0
for b in range(a ):
for i in buckets[b]:
_snake_case : List[str] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_a : str = 299_792_458
# Symbols
_a, _a, _a, _a : Dict = symbols("""ct x y z""")
def a__ ( a : float ):
"""simple docstring"""
if velocity > c:
raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("Speed must be greater than or equal to 1!" )
return velocity / c
def a__ ( a : float ):
"""simple docstring"""
return 1 / sqrt(1 - beta(a ) ** 2 )
def a__ ( a : float ):
"""simple docstring"""
return np.array(
[
[gamma(a ), -gamma(a ) * beta(a ), 0, 0],
[-gamma(a ) * beta(a ), gamma(a ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def a__ ( a : float , a : np.ndarray | None = None ):
"""simple docstring"""
if event is None:
_snake_case : str = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(a ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
_a : Union[str, Any] = transform(29_979_245)
print("""Example of four vector: """)
print(f'ct\' = {four_vector[0]}')
print(f'x\' = {four_vector[1]}')
print(f'y\' = {four_vector[2]}')
print(f'z\' = {four_vector[3]}')
# Substitute symbols with numerical values
_a : Union[str, Any] = {ct: c, x: 1, y: 1, z: 1}
_a : str = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'\n{numerical_vector}')
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : List[str] = {
"""configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
"""tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int = ["""BertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : str = [
"""BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BertForMaskedLM""",
"""BertForMultipleChoice""",
"""BertForNextSentencePrediction""",
"""BertForPreTraining""",
"""BertForQuestionAnswering""",
"""BertForSequenceClassification""",
"""BertForTokenClassification""",
"""BertLayer""",
"""BertLMHeadModel""",
"""BertModel""",
"""BertPreTrainedModel""",
"""load_tf_weights_in_bert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = [
"""TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBertEmbeddings""",
"""TFBertForMaskedLM""",
"""TFBertForMultipleChoice""",
"""TFBertForNextSentencePrediction""",
"""TFBertForPreTraining""",
"""TFBertForQuestionAnswering""",
"""TFBertForSequenceClassification""",
"""TFBertForTokenClassification""",
"""TFBertLMHeadModel""",
"""TFBertMainLayer""",
"""TFBertModel""",
"""TFBertPreTrainedModel""",
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int = ["""TFBertTokenizer"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Any = [
"""FlaxBertForCausalLM""",
"""FlaxBertForMaskedLM""",
"""FlaxBertForMultipleChoice""",
"""FlaxBertForNextSentencePrediction""",
"""FlaxBertForPreTraining""",
"""FlaxBertForQuestionAnswering""",
"""FlaxBertForSequenceClassification""",
"""FlaxBertForTokenClassification""",
"""FlaxBertModel""",
"""FlaxBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
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
_a : Union[str, Any] = """src/transformers"""
_a : str = """docs/source/en"""
_a : int = """."""
def a__ ( a : Dict , a : Optional[int] , a : List[Any] ):
"""simple docstring"""
with open(a , "r" , encoding="utf-8" , newline="\n" ) as f:
_snake_case : str = f.readlines()
# Find the start prompt.
_snake_case : Optional[Any] = 0
while not lines[start_index].startswith(a ):
start_index += 1
start_index += 1
_snake_case : List[Any] = start_index
while not lines[end_index].startswith(a ):
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 |
_a : Union[str, Any] = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_a : Optional[Any] = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_a : str = 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.
_a : Tuple = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_a : Optional[int] = direct_transformers_import(TRANSFORMERS_PATH)
def a__ ( a : Union[str, Any] ):
"""simple docstring"""
_snake_case : Optional[int] = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , a )
return [m.group(0 ) for m in matches]
def a__ ( a : Dict , a : Union[str, Any] ):
"""simple docstring"""
_snake_case : List[str] = 2 if text == "✅" or text == "❌" else len(a )
_snake_case : List[Any] = (width - text_length) // 2
_snake_case : List[str] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def a__ ( ):
"""simple docstring"""
_snake_case : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_snake_case : Union[str, Any] = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
_snake_case : List[str] = {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.
_snake_case : Tuple = collections.defaultdict(a )
_snake_case : Optional[Any] = collections.defaultdict(a )
_snake_case : int = collections.defaultdict(a )
_snake_case : List[str] = collections.defaultdict(a )
_snake_case : List[str] = collections.defaultdict(a )
# Let's lookup through all transformers object (once).
for attr_name in dir(a ):
_snake_case : Union[str, Any] = None
if attr_name.endswith("Tokenizer" ):
_snake_case : int = slow_tokenizers
_snake_case : Any = attr_name[:-9]
elif attr_name.endswith("TokenizerFast" ):
_snake_case : int = fast_tokenizers
_snake_case : List[Any] = attr_name[:-13]
elif _re_tf_models.match(a ) is not None:
_snake_case : List[str] = tf_models
_snake_case : List[Any] = _re_tf_models.match(a ).groups()[0]
elif _re_flax_models.match(a ) is not None:
_snake_case : int = flax_models
_snake_case : Any = _re_flax_models.match(a ).groups()[0]
elif _re_pt_models.match(a ) is not None:
_snake_case : Tuple = pt_models
_snake_case : int = _re_pt_models.match(a ).groups()[0]
if lookup_dict is not None:
while len(a ) > 0:
if attr_name in model_name_to_prefix.values():
_snake_case : Any = True
break
# Try again after removing the last word in the name
_snake_case : Dict = "".join(camel_case_split(a )[:-1] )
# Let's build that table!
_snake_case : Tuple = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
_snake_case : 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).
_snake_case : List[Any] = [len(a ) + 2 for c in columns]
_snake_case : List[str] = max([len(a ) for name in model_names] ) + 2
# Build the table per se
_snake_case : List[str] = "|" + "|".join([_center_text(a , a ) for c, w in zip(a , a )] ) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths] ) + "|\n"
_snake_case : str = {True: "✅", False: "❌"}
for name in model_names:
_snake_case : Any = model_name_to_prefix[name]
_snake_case : List[str] = [
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(a , a ) for l, w in zip(a , a )] ) + "|\n"
return table
def a__ ( a : Optional[Any]=False ):
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : Optional[int] = _find_text_in_file(
filename=os.path.join(a , "index.md" ) , start_prompt="<!--This table is updated automatically from the auto modules" , end_prompt="<!-- End table-->" , )
_snake_case : Optional[Any] = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(a , "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__":
_a : int = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_a : Union[str, Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Union[str, Any] = {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
),
"""distilbert-base-uncased-finetuned-sst-2-english""": (
"""https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
),
}
class _UpperCAmelCase ( _snake_case):
__lowercase : List[str] = """distilbert"""
__lowercase : Optional[Any] = {
"""hidden_size""": """dim""",
"""num_attention_heads""": """n_heads""",
"""num_hidden_layers""": """n_layers""",
}
def __init__( self , snake_case_=3_05_22 , snake_case_=5_12 , snake_case_=False , snake_case_=6 , snake_case_=12 , snake_case_=7_68 , snake_case_=4 * 7_68 , snake_case_=0.1 , snake_case_=0.1 , snake_case_="gelu" , snake_case_=0.02 , snake_case_=0.1 , snake_case_=0.2 , snake_case_=0 , **snake_case_ , ):
_snake_case : str = vocab_size
_snake_case : List[Any] = max_position_embeddings
_snake_case : Tuple = sinusoidal_pos_embds
_snake_case : int = n_layers
_snake_case : Any = n_heads
_snake_case : List[str] = dim
_snake_case : Optional[int] = hidden_dim
_snake_case : List[Any] = dropout
_snake_case : str = attention_dropout
_snake_case : Optional[Any] = activation
_snake_case : List[str] = initializer_range
_snake_case : Union[str, Any] = qa_dropout
_snake_case : Tuple = seq_classif_dropout
super().__init__(**snake_case_ , pad_token_id=snake_case_ )
class _UpperCAmelCase ( _snake_case):
@property
def lowerCamelCase__ ( self ):
if self.task == "multiple-choice":
_snake_case : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
_snake_case : str = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 87 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unittest.TestCase):
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : List[str] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , )
return model
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.dummy_uncond_unet
_snake_case : List[Any] = DDIMScheduler()
_snake_case : Union[str, Any] = self.dummy_vq_model
_snake_case : Optional[Any] = LDMPipeline(unet=snake_case_ , vqvae=snake_case_ , scheduler=snake_case_ )
ldm.to(snake_case_ )
ldm.set_progress_bar_config(disable=snake_case_ )
_snake_case : List[str] = torch.manual_seed(0 )
_snake_case : int = ldm(generator=snake_case_ , num_inference_steps=2 , output_type="numpy" ).images
_snake_case : str = torch.manual_seed(0 )
_snake_case : List[str] = ldm(generator=snake_case_ , num_inference_steps=2 , output_type="numpy" , return_dict=snake_case_ )[0]
_snake_case : Dict = image[0, -3:, -3:, -1]
_snake_case : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : str = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
_snake_case : str = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : Tuple = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
ldm.to(snake_case_ )
ldm.set_progress_bar_config(disable=snake_case_ )
_snake_case : Dict = torch.manual_seed(0 )
_snake_case : Tuple = ldm(generator=snake_case_ , num_inference_steps=5 , output_type="numpy" ).images
_snake_case : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
_snake_case : str = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447] )
_snake_case : Optional[Any] = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : str = """pixel_values"""
__lowercase : Tuple = False
__lowercase : Optional[int] = TimmBackboneConfig
def __init__( self , snake_case_ , **snake_case_ ):
requires_backends(self , "timm" )
super().__init__(snake_case_ )
_snake_case : Any = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name." )
if config.backbone not in timm.list_models():
raise ValueError(F'backbone {config.backbone} is not supported by timm.' )
if hasattr(snake_case_ , "out_features" ) and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )
_snake_case : int = getattr(snake_case_ , "use_pretrained_backbone" , snake_case_ )
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )
# We just take the final layer by default. This matches the default for the transformers models.
_snake_case : Optional[int] = config.out_indices if getattr(snake_case_ , "out_indices" , snake_case_ ) is not None else (-1,)
_snake_case : Optional[Any] = timm.create_model(
config.backbone , pretrained=snake_case_ , features_only=config.features_only , in_chans=config.num_channels , out_indices=snake_case_ , **snake_case_ , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
_snake_case : Optional[int] = self._backbone.return_layers
_snake_case : List[Any] = {layer["module"]: str(snake_case_ ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(snake_case_ )
@classmethod
def lowerCamelCase__ ( cls , snake_case_ , *snake_case_ , **snake_case_ ):
requires_backends(cls , ["vision", "timm"] )
from ...models.timm_backbone import TimmBackboneConfig
_snake_case : Dict = kwargs.pop("config" , TimmBackboneConfig() )
_snake_case : str = kwargs.pop("use_timm_backbone" , snake_case_ )
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones" )
_snake_case : List[Any] = kwargs.pop("num_channels" , config.num_channels )
_snake_case : List[Any] = kwargs.pop("features_only" , config.features_only )
_snake_case : Union[str, Any] = kwargs.pop("use_pretrained_backbone" , config.use_pretrained_backbone )
_snake_case : Union[str, Any] = kwargs.pop("out_indices" , config.out_indices )
_snake_case : List[str] = TimmBackboneConfig(
backbone=snake_case_ , num_channels=snake_case_ , features_only=snake_case_ , use_pretrained_backbone=snake_case_ , out_indices=snake_case_ , )
return super()._from_config(snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
pass
def lowerCamelCase__ ( self , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=None , **snake_case_ ):
_snake_case : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case : Optional[Any] = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
_snake_case : Tuple = self._all_layers
_snake_case : Any = self._backbone(snake_case_ , **snake_case_ )
_snake_case : List[str] = self._return_layers
_snake_case : Optional[int] = tuple(hidden_states[i] for i in self.out_indices )
else:
_snake_case : Dict = self._backbone(snake_case_ , **snake_case_ )
_snake_case : Optional[int] = None
_snake_case : List[str] = tuple(snake_case_ )
_snake_case : List[str] = tuple(snake_case_ ) if hidden_states is not None else None
if not return_dict:
_snake_case : int = (feature_maps,)
if output_hidden_states:
_snake_case : List[Any] = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=snake_case_ , hidden_states=snake_case_ , attentions=snake_case_ )
| 87 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 | 1 |
"""simple docstring"""
def a__ ( a : Tuple , a : List[str] ):
"""simple docstring"""
_snake_case : str = [0 for i in range(r + 1 )]
# nc0 = 1
_snake_case : Optional[int] = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
_snake_case : Tuple = min(a , a )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 87 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_snake_case : List[Any] = Vector()
def lowerCamelCase__ ( self ):
_snake_case : Any = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case_ ) , "(0,0,0,0,0,1)" )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case_ ) , 4 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2] )
_snake_case : List[str] = Vector([1, 2, 3, 4, 5] )
_snake_case : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_snake_case : Any = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
_snake_case : Any = 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 lowerCamelCase__ ( self ):
_snake_case : str = Vector([1, 2, 3] )
_snake_case : Union[str, Any] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Vector([1, 2, 3] )
_snake_case : List[Any] = Vector([2, -1, 4] ) # for test of dot product
_snake_case : Union[str, Any] = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Vector([1, 2, 3] )
_snake_case : Optional[Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case_ , snake_case_ ) ) , "(3,4,7)" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Vector([1, 0, 0, 0, 0, 0] )
_snake_case : Optional[int] = x.copy()
self.assertEqual(str(snake_case_ ) , str(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case_ ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : str = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : str = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self ):
_snake_case : str = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_snake_case : List[str] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : int = 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 lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = 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 lowerCamelCase__ ( 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()
| 87 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Union[str, Any] = UnCLIPImageVariationPipeline
__lowercase : Optional[int] = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
__lowercase : List[Any] = IMAGE_VARIATION_BATCH_PARAMS
__lowercase : Any = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
__lowercase : str = False
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return self.time_input_dim
@property
def lowerCamelCase__ ( self ):
return self.time_input_dim * 4
@property
def lowerCamelCase__ ( self ):
return 1_00
@property
def lowerCamelCase__ ( self ):
_snake_case : str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(snake_case_ )
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Optional[int] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(snake_case_ )
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : int = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
_snake_case : int = UnCLIPTextProjModel(**snake_case_ )
return model
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Union[str, Any] = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
_snake_case : Any = UNetaDConditionModel(**snake_case_ )
return model
@property
def lowerCamelCase__ ( self ):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Tuple = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def lowerCamelCase__ ( self ):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
_snake_case : List[str] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def lowerCamelCase__ ( self ):
_snake_case : Any = self.dummy_decoder
_snake_case : Union[str, Any] = self.dummy_text_proj
_snake_case : Union[str, Any] = self.dummy_text_encoder
_snake_case : List[str] = self.dummy_tokenizer
_snake_case : Union[str, Any] = self.dummy_super_res_first
_snake_case : str = self.dummy_super_res_last
_snake_case : int = UnCLIPScheduler(
variance_type="learned_range" , prediction_type="epsilon" , num_train_timesteps=10_00 , )
_snake_case : Any = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="epsilon" , num_train_timesteps=10_00 , )
_snake_case : Any = CLIPImageProcessor(crop_size=32 , size=32 )
_snake_case : Optional[Any] = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 , snake_case_=True ):
_snake_case : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
if str(snake_case_ ).startswith("mps" ):
_snake_case : List[Any] = torch.manual_seed(snake_case_ )
else:
_snake_case : int = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
if pil_image:
_snake_case : Optional[int] = input_image * 0.5 + 0.5
_snake_case : Union[str, Any] = input_image.clamp(0 , 1 )
_snake_case : List[str] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_snake_case : Optional[int] = DiffusionPipeline.numpy_to_pil(snake_case_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def lowerCamelCase__ ( self ):
_snake_case : List[str] = "cpu"
_snake_case : List[Any] = self.get_dummy_components()
_snake_case : Any = self.pipeline_class(**snake_case_ )
_snake_case : Tuple = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : List[Any] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : Any = pipe(**snake_case_ )
_snake_case : int = output.images
_snake_case : Optional[Any] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : Optional[Any] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
_snake_case : str = image[0, -3:, -3:, -1]
_snake_case : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : List[str] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_snake_case : int = "cpu"
_snake_case : str = self.get_dummy_components()
_snake_case : Union[str, Any] = self.pipeline_class(**snake_case_ )
_snake_case : List[Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : List[str] = pipe(**snake_case_ )
_snake_case : Optional[Any] = output.images
_snake_case : int = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : Optional[int] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
_snake_case : Optional[int] = image[0, -3:, -3:, -1]
_snake_case : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : Any = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_snake_case : List[str] = "cpu"
_snake_case : str = self.get_dummy_components()
_snake_case : Tuple = self.pipeline_class(**snake_case_ )
_snake_case : Optional[Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Optional[Any] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : Tuple = [
pipeline_inputs["image"],
pipeline_inputs["image"],
]
_snake_case : int = pipe(**snake_case_ )
_snake_case : Dict = output.images
_snake_case : str = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : Tuple = [
tuple_pipeline_inputs["image"],
tuple_pipeline_inputs["image"],
]
_snake_case : List[Any] = pipe(
**snake_case_ , return_dict=snake_case_ , )[0]
_snake_case : Optional[Any] = image[0, -3:, -3:, -1]
_snake_case : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
_snake_case : Optional[Any] = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = torch.device("cpu" )
class _UpperCAmelCase :
__lowercase : List[str] = 1
_snake_case : Any = self.get_dummy_components()
_snake_case : List[Any] = self.pipeline_class(**snake_case_ )
_snake_case : Union[str, Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Dict = torch.Generator(device=snake_case_ ).manual_seed(0 )
_snake_case : List[Any] = pipe.decoder.dtype
_snake_case : int = 1
_snake_case : Optional[int] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
_snake_case : List[str] = pipe.prepare_latents(
snake_case_ , dtype=snake_case_ , device=snake_case_ , generator=snake_case_ , latents=snake_case_ , scheduler=DummyScheduler() )
_snake_case : Optional[int] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
_snake_case : Optional[int] = pipe.prepare_latents(
snake_case_ , dtype=snake_case_ , device=snake_case_ , generator=snake_case_ , latents=snake_case_ , scheduler=DummyScheduler() )
_snake_case : Any = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
_snake_case : str = pipe(
**snake_case_ , decoder_latents=snake_case_ , super_res_latents=snake_case_ ).images
_snake_case : Optional[int] = self.get_dummy_inputs(snake_case_ , pil_image=snake_case_ )
# Don't pass image, instead pass embedding
_snake_case : List[Any] = pipeline_inputs.pop("image" )
_snake_case : List[str] = pipe.image_encoder(snake_case_ ).image_embeds
_snake_case : Dict = pipe(
**snake_case_ , decoder_latents=snake_case_ , super_res_latents=snake_case_ , image_embeddings=snake_case_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def lowerCamelCase__ ( self ):
_snake_case : str = torch_device == "cpu"
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
_snake_case : Optional[int] = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=snake_case_ , expected_max_diff=snake_case_ )
@skip_mps
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = torch_device == "cpu"
_snake_case : Optional[Any] = True
_snake_case : List[Any] = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
test_max_difference=snake_case_ , relax_max_difference=snake_case_ , additional_params_copy_to_batched_inputs=snake_case_ , )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
_snake_case : Dict = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=snake_case_ , additional_params_copy_to_batched_inputs=snake_case_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=snake_case_ )
@skip_mps
def lowerCamelCase__ ( self ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCamelCase__ ( self ):
return super().test_save_load_local()
@skip_mps
def lowerCamelCase__ ( self ):
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_snake_case : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
_snake_case : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )
_snake_case : Any = UnCLIPImageVariationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations" , torch_dtype=torch.floataa )
_snake_case : Union[str, Any] = pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
_snake_case : Dict = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : Tuple = pipeline(
snake_case_ , generator=snake_case_ , output_type="np" , )
_snake_case : Tuple = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ , 15 )
| 87 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( a : float , a : float , a : float ):
"""simple docstring"""
_snake_case : Optional[Any] = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_a : Tuple = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
"""tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = [
"""GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXJapaneseForCausalLM""",
"""GPTNeoXJapaneseLayer""",
"""GPTNeoXJapaneseModel""",
"""GPTNeoXJapanesePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_a : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Any = TextToVideoSDPipeline
__lowercase : str = TEXT_TO_IMAGE_PARAMS
__lowercase : int = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__lowercase : Optional[int] = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : str = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case : List[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , )
torch.manual_seed(0 )
_snake_case : Union[str, Any] = 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 )
_snake_case : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
_snake_case : Tuple = CLIPTextModel(snake_case_ )
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : str = torch.manual_seed(snake_case_ )
else:
_snake_case : Union[str, Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[Any] = self.get_dummy_components()
_snake_case : Tuple = TextToVideoSDPipeline(**snake_case_ )
_snake_case : List[str] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = self.get_dummy_inputs(snake_case_ )
_snake_case : Union[str, Any] = "np"
_snake_case : Dict = sd_pipe(**snake_case_ ).frames
_snake_case : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_snake_case : Dict = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=1E-2 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy" )
_snake_case : int = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_snake_case : Tuple = pipe.to("cuda" )
_snake_case : List[Any] = "Spiderman is surfing"
_snake_case : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : int = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="pt" ).frames
_snake_case : int = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def lowerCamelCase__ ( self ):
_snake_case : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy" )
_snake_case : str = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : int = pipe.to("cuda" )
_snake_case : Any = "Spiderman is surfing"
_snake_case : str = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : Any = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=2 , output_type="pt" ).frames
_snake_case : Optional[int] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : int = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = ["""ViTFeatureExtractor"""]
_a : List[Any] = ["""ViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[str] = [
"""VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTForImageClassification""",
"""ViTForMaskedImageModeling""",
"""ViTModel""",
"""ViTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = [
"""TFViTForImageClassification""",
"""TFViTModel""",
"""TFViTPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = [
"""FlaxViTForImageClassification""",
"""FlaxViTModel""",
"""FlaxViTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
_a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """EncodecFeatureExtractor"""
__lowercase : str = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
_snake_case : Dict = self.feature_extractor
_snake_case : Any = False
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ , language=snake_case_ , no_timestamps=snake_case_ )
def __call__( self , *snake_case_ , **snake_case_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
_snake_case : str = kwargs.pop("audio" , snake_case_ )
_snake_case : Optional[int] = kwargs.pop("sampling_rate" , snake_case_ )
_snake_case : Optional[Any] = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Any = args[0]
_snake_case : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_snake_case : Any = self.tokenizer(snake_case_ , **snake_case_ )
if audio is not None:
_snake_case : Any = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_snake_case : str = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_snake_case : List[str] = audio_inputs["padding_mask"]
return inputs
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
_snake_case : Tuple = kwargs.pop("audio" , snake_case_ )
_snake_case : List[str] = kwargs.pop("padding_mask" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Tuple = args[0]
_snake_case : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(snake_case_ , padding_mask=snake_case_ )
else:
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = to_numpy(snake_case_ )
_snake_case , _snake_case , _snake_case : Tuple = audio_values.shape
if padding_mask is None:
return list(snake_case_ )
_snake_case : Optional[int] = to_numpy(snake_case_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_snake_case : Any = seq_len - padding_mask.shape[-1]
_snake_case : Optional[Any] = 1 - self.feature_extractor.padding_value
_snake_case : Optional[int] = np.pad(snake_case_ , ((0, 0), (0, difference)) , "constant" , constant_values=snake_case_ )
_snake_case : Any = audio_values.tolist()
for i in range(snake_case_ ):
_snake_case : Tuple = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_snake_case : Tuple = sliced_audio.reshape(snake_case_ , -1 )
return audio_values
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""YolosFeatureExtractor"""]
_a : List[Any] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from torch import nn
def a__ ( a : Optional[Any] ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'Unsupported activation function: {act_fn}' )
| 87 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = dataset
_snake_case : str = process
_snake_case : int = params
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
_snake_case : Union[str, Any] = self.dataset[i]
_snake_case : Optional[Any] = self.process(snake_case_ , **self.params )
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
_snake_case : Union[str, Any] = loader
_snake_case : Tuple = infer
_snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_snake_case : int = None
_snake_case : int = loader_batch_size
# Internal bookkeeping
_snake_case : Any = None
_snake_case : Dict = None
def __len__( self ):
return len(self.loader )
def __iter__( self ):
_snake_case : int = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_snake_case : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
_snake_case : Tuple = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_snake_case : Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : int = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_snake_case : Tuple = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : Tuple = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
_snake_case : Tuple = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : List[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : Union[str, Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_snake_case : List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_snake_case : int = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def lowerCamelCase__ ( self ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_snake_case : Tuple = next(self.iterator )
_snake_case : Any = self.infer(snake_case_ , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Optional[int] = list(processed.keys() )[0]
_snake_case : List[str] = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = len(snake_case_ )
else:
_snake_case : Optional[int] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_snake_case : str = processed
_snake_case : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
_snake_case : Tuple = iter(self.loader )
_snake_case : List[Any] = None
return self
def lowerCamelCase__ ( self ):
if self.subiterator is None:
_snake_case : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_snake_case : Union[str, Any] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_snake_case : str = self.infer(next(self.iterator ) , **self.params )
_snake_case : Tuple = next(self.subiterator )
return processed
class _UpperCAmelCase ( _snake_case):
def __iter__( self ):
_snake_case : Optional[Any] = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
_snake_case : Optional[Any] = False
_snake_case : Tuple = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : str = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
_snake_case : List[str] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Tuple = list(processed.keys() )[0]
_snake_case : Tuple = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Any = len(snake_case_ )
else:
_snake_case : List[Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Dict = observed_batch_size
_snake_case : List[Any] = processed
_snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : int = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
_snake_case : Dict = processed
_snake_case : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ ):
_snake_case : str = dataset
_snake_case : Any = key
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return self.dataset[i][self.key]
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = dataset
_snake_case : Any = keya
_snake_case : int = keya
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 87 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( _snake_case):
__lowercase : int = (PNDMScheduler,)
__lowercase : List[Any] = (("""num_inference_steps""", 5_0),)
def lowerCamelCase__ ( self , **snake_case_ ):
_snake_case : Tuple = {
"num_train_timesteps": 10_00,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**snake_case_ )
return config
def lowerCamelCase__ ( self , snake_case_=0 , **snake_case_ ):
_snake_case : List[str] = dict(self.forward_default_kwargs )
_snake_case : Dict = kwargs.pop("num_inference_steps" , snake_case_ )
_snake_case : Optional[Any] = self.dummy_sample
_snake_case : Optional[Any] = 0.1 * sample
_snake_case : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_snake_case : Optional[int] = self.get_scheduler_config(**snake_case_ )
_snake_case : Tuple = scheduler_class(**snake_case_ )
scheduler.set_timesteps(snake_case_ )
# copy over dummy past residuals
_snake_case : int = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case_ )
_snake_case : Any = scheduler_class.from_pretrained(snake_case_ )
new_scheduler.set_timesteps(snake_case_ )
# copy over dummy past residuals
_snake_case : Optional[Any] = dummy_past_residuals[:]
_snake_case : int = scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
_snake_case : Union[str, Any] = new_scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_snake_case : Tuple = scheduler.step_plms(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
_snake_case : str = new_scheduler.step_plms(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self , snake_case_=0 , **snake_case_ ):
_snake_case : List[str] = dict(self.forward_default_kwargs )
_snake_case : str = kwargs.pop("num_inference_steps" , snake_case_ )
_snake_case : str = self.dummy_sample
_snake_case : List[Any] = 0.1 * sample
_snake_case : Tuple = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_snake_case : Tuple = self.get_scheduler_config()
_snake_case : int = scheduler_class(**snake_case_ )
scheduler.set_timesteps(snake_case_ )
# copy over dummy past residuals (must be after setting timesteps)
_snake_case : Optional[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case_ )
_snake_case : Tuple = scheduler_class.from_pretrained(snake_case_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(snake_case_ )
# copy over dummy past residual (must be after setting timesteps)
_snake_case : Dict = dummy_past_residuals[:]
_snake_case : Union[str, Any] = scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
_snake_case : str = new_scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_snake_case : List[str] = scheduler.step_plms(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
_snake_case : List[Any] = new_scheduler.step_plms(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self , **snake_case_ ):
_snake_case : Dict = self.scheduler_classes[0]
_snake_case : Optional[int] = self.get_scheduler_config(**snake_case_ )
_snake_case : int = scheduler_class(**snake_case_ )
_snake_case : Optional[Any] = 10
_snake_case : int = self.dummy_model()
_snake_case : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(snake_case_ )
for i, t in enumerate(scheduler.prk_timesteps ):
_snake_case : List[Any] = model(snake_case_ , snake_case_ )
_snake_case : Union[str, Any] = scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
_snake_case : str = model(snake_case_ , snake_case_ )
_snake_case : Tuple = scheduler.step_plms(snake_case_ , snake_case_ , snake_case_ ).prev_sample
return sample
def lowerCamelCase__ ( self ):
_snake_case : Any = dict(self.forward_default_kwargs )
_snake_case : List[Any] = kwargs.pop("num_inference_steps" , snake_case_ )
for scheduler_class in self.scheduler_classes:
_snake_case : List[str] = self.get_scheduler_config()
_snake_case : Union[str, Any] = scheduler_class(**snake_case_ )
_snake_case : Tuple = self.dummy_sample
_snake_case : Tuple = 0.1 * sample
if num_inference_steps is not None and hasattr(snake_case_ , "set_timesteps" ):
scheduler.set_timesteps(snake_case_ )
elif num_inference_steps is not None and not hasattr(snake_case_ , "set_timesteps" ):
_snake_case : Union[str, Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_snake_case : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
_snake_case : Dict = dummy_past_residuals[:]
_snake_case : Dict = scheduler.step_prk(snake_case_ , 0 , snake_case_ , **snake_case_ ).prev_sample
_snake_case : Any = scheduler.step_prk(snake_case_ , 1 , snake_case_ , **snake_case_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
_snake_case : List[Any] = scheduler.step_plms(snake_case_ , 0 , snake_case_ , **snake_case_ ).prev_sample
_snake_case : List[Any] = scheduler.step_plms(snake_case_ , 1 , snake_case_ , **snake_case_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase__ ( self ):
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=snake_case_ )
def lowerCamelCase__ ( self ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=snake_case_ )
_snake_case : str = self.scheduler_classes[0]
_snake_case : int = self.get_scheduler_config(steps_offset=1 )
_snake_case : str = scheduler_class(**snake_case_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , )
def lowerCamelCase__ ( self ):
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case_ , beta_end=snake_case_ )
def lowerCamelCase__ ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=snake_case_ )
def lowerCamelCase__ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case_ )
def lowerCamelCase__ ( self ):
for t in [1, 5, 10]:
self.check_over_forward(time_step=snake_case_ )
def lowerCamelCase__ ( self ):
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=snake_case_ )
def lowerCamelCase__ ( self ):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
_snake_case : Optional[int] = 27
for scheduler_class in self.scheduler_classes:
_snake_case : List[str] = self.dummy_sample
_snake_case : Union[str, Any] = 0.1 * sample
_snake_case : int = self.get_scheduler_config()
_snake_case : Optional[int] = scheduler_class(**snake_case_ )
scheduler.set_timesteps(snake_case_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
_snake_case : List[str] = scheduler.step_prk(snake_case_ , snake_case_ , snake_case_ ).prev_sample
def lowerCamelCase__ ( self ):
with self.assertRaises(snake_case_ ):
_snake_case : Union[str, Any] = self.scheduler_classes[0]
_snake_case : Optional[int] = self.get_scheduler_config()
_snake_case : Any = scheduler_class(**snake_case_ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.full_loop()
_snake_case : Tuple = torch.sum(torch.abs(snake_case_ ) )
_snake_case : List[Any] = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 198.1318 ) < 1E-2
assert abs(result_mean.item() - 0.2580 ) < 1E-3
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = self.full_loop(prediction_type="v_prediction" )
_snake_case : Optional[Any] = torch.sum(torch.abs(snake_case_ ) )
_snake_case : Optional[Any] = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 67.3986 ) < 1E-2
assert abs(result_mean.item() - 0.0878 ) < 1E-3
def lowerCamelCase__ ( self ):
# We specify different beta, so that the first alpha is 0.99
_snake_case : List[str] = self.full_loop(set_alpha_to_one=snake_case_ , beta_start=0.01 )
_snake_case : Optional[Any] = torch.sum(torch.abs(snake_case_ ) )
_snake_case : Dict = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 230.0399 ) < 1E-2
assert abs(result_mean.item() - 0.2995 ) < 1E-3
def lowerCamelCase__ ( self ):
# We specify different beta, so that the first alpha is 0.99
_snake_case : Union[str, Any] = self.full_loop(set_alpha_to_one=snake_case_ , beta_start=0.01 )
_snake_case : Tuple = torch.sum(torch.abs(snake_case_ ) )
_snake_case : Optional[Any] = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 186.9482 ) < 1E-2
assert abs(result_mean.item() - 0.2434 ) < 1E-3
| 87 |
"""simple docstring"""
def a__ ( a : int ):
"""simple docstring"""
if not isinstance(a , a ):
raise TypeError("Input value must be an 'int' type" )
_snake_case : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Any = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
_a : Any = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def a__ ( a : Optional[int] , a : str , a : List[Any] , a : Dict , a : str ):
"""simple docstring"""
for attribute in key.split("." ):
_snake_case : Optional[int] = getattr(a , a )
if weight_type is not None:
_snake_case : Union[str, Any] = getattr(a , a ).shape
else:
_snake_case : List[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
_snake_case : List[str] = value
elif weight_type == "weight_g":
_snake_case : Union[str, Any] = value
elif weight_type == "weight_v":
_snake_case : Union[str, Any] = value
elif weight_type == "bias":
_snake_case : Optional[Any] = value
else:
_snake_case : str = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def a__ ( a : Optional[int] , a : Dict ):
"""simple docstring"""
_snake_case : Optional[Any] = []
_snake_case : Dict = fairseq_model.state_dict()
_snake_case : Optional[int] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
_snake_case : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
a , a , a , a , hf_model.config.feat_extract_norm == "group" , )
_snake_case : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_snake_case : Optional[Any] = True
if "*" in mapped_key:
_snake_case : Tuple = name.split(a )[0].split("." )[-2]
_snake_case : Any = mapped_key.replace("*" , a )
if "weight_g" in name:
_snake_case : Any = "weight_g"
elif "weight_v" in name:
_snake_case : Any = "weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
_snake_case : List[Any] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_snake_case : Union[str, Any] = "weight"
else:
_snake_case : Dict = None
set_recursively(a , a , a , a , a )
continue
if not is_used:
unused_weights.append(a )
logger.warning(f'Unused weights: {unused_weights}' )
def a__ ( a : Tuple , a : Dict , a : Any , a : Optional[int] , a : Optional[Any] ):
"""simple docstring"""
_snake_case : int = full_name.split("conv_layers." )[-1]
_snake_case : List[Any] = name.split("." )
_snake_case : str = int(items[0] )
_snake_case : str = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
_snake_case : Union[str, Any] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
_snake_case : Tuple = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
_snake_case : Dict = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
_snake_case : str = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(a )
@torch.no_grad()
def a__ ( a : Tuple , a : Union[str, Any] , a : Union[str, Any]=None ):
"""simple docstring"""
_snake_case : Union[str, Any] = torch.load(a )
_snake_case : Optional[Any] = WavLMConfigOrig(checkpoint["cfg"] )
_snake_case : Tuple = WavLMOrig(a )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
_snake_case : Union[str, Any] = WavLMConfig.from_pretrained(a )
else:
_snake_case : Optional[Any] = WavLMConfig()
_snake_case : str = WavLMModel(a )
recursively_load_weights(a , a )
hf_wavlm.save_pretrained(a )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
_a : Optional[int] = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 87 |
"""simple docstring"""
from __future__ import annotations
import requests
_a : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( a : str , a : int = 1 , a : str = "new" , a : list | None = None ):
"""simple docstring"""
_snake_case : Any = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(a ) - valid_terms ) ):
_snake_case : Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(a )
_snake_case : int = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"User-agent": "A random string"} , )
if response.status_code == 429:
raise requests.HTTPError
_snake_case : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(a )}
_snake_case : Tuple = {}
for id_ in range(a ):
_snake_case : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 87 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : Union[str, Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
_a : List[str] = {
"""vocab_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
},
"""merges_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
},
}
_a : Optional[Any] = {
"""allenai/longformer-base-4096""": 4_096,
"""allenai/longformer-large-4096""": 4_096,
"""allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
"""allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
"""allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def a__ ( ):
"""simple docstring"""
_snake_case : int = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_snake_case : Tuple = bs[:]
_snake_case : Optional[int] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(a )
cs.append(2**8 + n )
n += 1
_snake_case : Dict = [chr(a ) for n in cs]
return dict(zip(a , a ) )
def a__ ( a : Union[str, Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = set()
_snake_case : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_snake_case : Optional[Any] = char
return pairs
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = VOCAB_FILES_NAMES
__lowercase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowercase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Any = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case_ , snake_case_ , snake_case_="replace" , snake_case_="<s>" , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_="<mask>" , snake_case_=False , **snake_case_ , ):
_snake_case : str = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else bos_token
_snake_case : Dict = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else eos_token
_snake_case : Dict = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else sep_token
_snake_case : str = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else cls_token
_snake_case : Union[str, Any] = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else unk_token
_snake_case : List[str] = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case : List[Any] = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
super().__init__(
errors=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , add_prefix_space=snake_case_ , **snake_case_ , )
with open(snake_case_ , encoding="utf-8" ) as vocab_handle:
_snake_case : Optional[int] = json.load(snake_case_ )
_snake_case : str = {v: k for k, v in self.encoder.items()}
_snake_case : Union[str, Any] = errors # how to handle errors in decoding
_snake_case : str = bytes_to_unicode()
_snake_case : Optional[Any] = {v: k for k, v in self.byte_encoder.items()}
with open(snake_case_ , encoding="utf-8" ) as merges_handle:
_snake_case : Optional[Any] = merges_handle.read().split("\n" )[1:-1]
_snake_case : Any = [tuple(merge.split() ) for merge in bpe_merges]
_snake_case : Tuple = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
_snake_case : Union[str, Any] = {}
_snake_case : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_snake_case : int = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def lowerCamelCase__ ( self ):
return len(self.encoder )
def lowerCamelCase__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase__ ( self , snake_case_ ):
if token in self.cache:
return self.cache[token]
_snake_case : Tuple = tuple(snake_case_ )
_snake_case : str = get_pairs(snake_case_ )
if not pairs:
return token
while True:
_snake_case : Dict = min(snake_case_ , key=lambda snake_case_ : self.bpe_ranks.get(snake_case_ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_snake_case , _snake_case : Optional[Any] = bigram
_snake_case : Tuple = []
_snake_case : Tuple = 0
while i < len(snake_case_ ):
try:
_snake_case : int = word.index(snake_case_ , snake_case_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_snake_case : List[str] = j
if word[i] == first and i < len(snake_case_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_snake_case : Union[str, Any] = tuple(snake_case_ )
_snake_case : Optional[Any] = new_word
if len(snake_case_ ) == 1:
break
else:
_snake_case : int = get_pairs(snake_case_ )
_snake_case : int = " ".join(snake_case_ )
_snake_case : Optional[int] = word
return word
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : Optional[Any] = []
for token in re.findall(self.pat , snake_case_ ):
_snake_case : Optional[int] = "".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(snake_case_ ).split(" " ) )
return bpe_tokens
def lowerCamelCase__ ( self , snake_case_ ):
return self.encoder.get(snake_case_ , self.encoder.get(self.unk_token ) )
def lowerCamelCase__ ( self , snake_case_ ):
return self.decoder.get(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : Optional[Any] = "".join(snake_case_ )
_snake_case : Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_snake_case : List[Any] = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_snake_case : Any = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(snake_case_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case_ , ensure_ascii=snake_case_ ) + "\n" )
_snake_case : Any = 0
with open(snake_case_ , "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 snake_case_ : 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 : Dict = token_index
writer.write(" ".join(snake_case_ ) + "\n" )
index += 1
return vocab_file, merge_file
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case : List[str] = [self.cls_token_id]
_snake_case : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1, 1] + ([0] * len(snake_case_ )) + [1]
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = [self.sep_token_id]
_snake_case : Tuple = [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 , snake_case_ , snake_case_=False , **snake_case_ ):
_snake_case : List[str] = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case_ ) > 0 and not text[0].isspace()):
_snake_case : Union[str, Any] = " " + text
return (text, kwargs)
| 87 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(a ), magnitude * sin(a )]
return [magnitude * cos(radians(a ) ), magnitude * sin(radians(a ) )]
def a__ ( a : NDArray[floataa] , a : NDArray[floataa] , a : float = 10**-1 ):
"""simple docstring"""
_snake_case : NDArray[floataa] = cross(a , a )
_snake_case : float = sum(a )
return abs(a ) < eps
if __name__ == "__main__":
# Test to check if it works
_a : Tuple = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
_a : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_a : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
_a : List[Any] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_a : List[str] = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
_a : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """openai-gpt"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=4_04_78 , snake_case_=5_12 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_="cls_index" , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=0.1 , **snake_case_ , ):
_snake_case : Tuple = vocab_size
_snake_case : Dict = n_positions
_snake_case : Any = n_embd
_snake_case : Any = n_layer
_snake_case : Optional[int] = n_head
_snake_case : Union[str, Any] = afn
_snake_case : Dict = resid_pdrop
_snake_case : str = embd_pdrop
_snake_case : Union[str, Any] = attn_pdrop
_snake_case : str = layer_norm_epsilon
_snake_case : Union[str, Any] = initializer_range
_snake_case : Any = summary_type
_snake_case : List[str] = summary_use_proj
_snake_case : Optional[int] = summary_activation
_snake_case : Union[str, Any] = summary_first_dropout
_snake_case : Optional[int] = summary_proj_to_labels
super().__init__(**snake_case_ )
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : List[str] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict = ["""ConvNextFeatureExtractor"""]
_a : Optional[Any] = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : str = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 87 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_a : int = []
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}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_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'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""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"""),
]
)
def a__ ( a : List[str] , a : int , a : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = state_dict.pop(a )
_snake_case : Union[str, Any] = val
def a__ ( a : Tuple ):
"""simple docstring"""
_snake_case : Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_snake_case : Dict = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
_snake_case : Tuple = value
else:
_snake_case : Dict = value
return new_state_dict
def a__ ( a : int ):
"""simple docstring"""
_snake_case : Any = ""
# 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)
_snake_case : str = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : str = 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
_snake_case : int = in_proj_weight[:256, :]
_snake_case : List[str] = in_proj_bias[:256]
_snake_case : Optional[Any] = in_proj_weight[256:512, :]
_snake_case : List[str] = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : Dict = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_snake_case : List[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_snake_case : Union[str, Any] = in_proj_weight[:256, :]
_snake_case : Tuple = in_proj_bias[:256]
_snake_case : int = in_proj_weight[256:512, :]
_snake_case : int = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : str = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_snake_case : Dict = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
_snake_case : Optional[int] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_snake_case : Dict = in_proj_weight_cross_attn[:256, :]
_snake_case : Any = in_proj_bias_cross_attn[:256]
_snake_case : Union[str, Any] = in_proj_weight_cross_attn[256:512, :]
_snake_case : Optional[int] = in_proj_bias_cross_attn[256:512]
_snake_case : Any = in_proj_weight_cross_attn[-256:, :]
_snake_case : str = in_proj_bias_cross_attn[-256:]
def a__ ( a : str , a : int ):
"""simple docstring"""
_snake_case , _snake_case : List[str] = image.size
_snake_case : Dict = max(a , a )
_snake_case : Union[str, Any] = 800 if "detection" in checkpoint_url else 1_000
_snake_case : Any = target_max_size / current_max_size
_snake_case : str = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( a : str ):
"""simple docstring"""
_snake_case : str = F.to_tensor(a )
_snake_case : Union[str, Any] = F.normalize(a , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( a : Optional[Any] , a : Any , a : Union[str, Any] ):
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
_snake_case : Tuple = torch.hub.load_state_dict_from_url(a , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(a , a , a )
_snake_case : Union[str, Any] = rename_backbone_keys(a )
# query, key and value matrices need special treatment
read_in_q_k_v(a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_snake_case : int = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_snake_case : Optional[int] = state_dict.pop(a )
_snake_case : Any = val
# create HuggingFace model and load state dict
_snake_case : Tuple = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
_snake_case : Any = 15
_snake_case : int = 2
_snake_case : Optional[Any] = {0: "table", 1: "table rotated"}
_snake_case : Union[str, Any] = idalabel
_snake_case : Union[str, Any] = {v: k for k, v in idalabel.items()}
else:
_snake_case : Any = 125
_snake_case : Union[str, Any] = 6
_snake_case : List[str] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
_snake_case : Any = idalabel
_snake_case : Optional[int] = {v: k for k, v in idalabel.items()}
_snake_case : Union[str, Any] = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1_000 )
_snake_case : str = TableTransformerForObjectDetection(a )
model.load_state_dict(a )
model.eval()
# verify our conversion
_snake_case : Optional[int] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
_snake_case : Optional[Any] = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=a )
_snake_case : Dict = Image.open(a ).convert("RGB" )
_snake_case : Union[str, Any] = normalize(resize(a , a ) ).unsqueeze(0 )
_snake_case : str = model(a )
if "detection" in checkpoint_url:
_snake_case : int = (1, 15, 3)
_snake_case : List[str] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
_snake_case : List[str] = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
_snake_case : Union[str, Any] = (1, 125, 7)
_snake_case : str = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
_snake_case : Optional[Any] = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
_snake_case : int = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(a )
image_processor.push_to_hub(a )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint 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."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : Any = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 87 | 1 |
"""simple docstring"""
def a__ ( a : int ):
"""simple docstring"""
if not isinstance(a , a ):
raise TypeError("Input value must be an 'int' type" )
_snake_case : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 | 1 |
"""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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_a : str = logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase :
def __init__( self , snake_case_=False , snake_case_=False , snake_case_=6.0 , snake_case_=None , snake_case_=False , snake_case_=False , snake_case_=None , snake_case_="fp4" , snake_case_=False , **snake_case_ , ):
_snake_case : str = load_in_abit
_snake_case : int = load_in_abit
_snake_case : Any = llm_inta_threshold
_snake_case : Any = llm_inta_skip_modules
_snake_case : Optional[int] = llm_inta_enable_fpaa_cpu_offload
_snake_case : Optional[int] = llm_inta_has_fpaa_weight
_snake_case : Dict = bnb_abit_quant_type
_snake_case : Union[str, Any] = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
_snake_case : List[Any] = torch.floataa
elif isinstance(snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = getattr(snake_case_ , snake_case_ )
elif isinstance(snake_case_ , torch.dtype ):
_snake_case : Optional[int] = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def lowerCamelCase__ ( self ):
if not isinstance(self.llm_inta_threshold , snake_case_ ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , snake_case_ ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , snake_case_ ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , snake_case_ ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , snake_case_ ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , snake_case_ ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def lowerCamelCase__ ( self ):
return self.load_in_abit or self.load_in_abit
def lowerCamelCase__ ( self ):
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def lowerCamelCase__ ( cls , snake_case_ , snake_case_ , **snake_case_ ):
_snake_case : List[Any] = cls(**snake_case_ )
_snake_case : Dict = []
for key, value in kwargs.items():
if hasattr(snake_case_ , snake_case_ ):
setattr(snake_case_ , snake_case_ , snake_case_ )
to_remove.append(snake_case_ )
for key in to_remove:
kwargs.pop(snake_case_ , snake_case_ )
if return_unused_kwargs:
return config, kwargs
else:
return config
def lowerCamelCase__ ( self , snake_case_ ):
with open(snake_case_ , "w" , encoding="utf-8" ) as writer:
_snake_case : List[Any] = self.to_dict()
_snake_case : Any = json.dumps(snake_case_ , indent=2 , sort_keys=snake_case_ ) + "\n"
writer.write(snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = copy.deepcopy(self.__dict__ )
_snake_case : Union[str, Any] = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self ):
return F'{self.__class__.__name__} {self.to_json_string()}'
def lowerCamelCase__ ( self , snake_case_ = True ):
if use_diff is True:
_snake_case : Optional[int] = self.to_diff_dict()
else:
_snake_case : Union[str, Any] = self.to_dict()
return json.dumps(snake_case_ , indent=2 , sort_keys=snake_case_ ) + "\n"
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.to_dict()
# get the default config dict
_snake_case : int = BitsAndBytesConfig().to_dict()
_snake_case : Dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
_snake_case : Optional[int] = value
return serializable_config_dict
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87 | 1 |
"""simple docstring"""
_a : int = [0, 2, 4, 6, 8]
_a : Dict = [1, 3, 5, 7, 9]
def a__ ( a : int , a : int , a : list[int] , a : int ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_snake_case : int = 0
for digit in range(10 ):
_snake_case : Any = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a , a )
return result
_snake_case : int = 0
for digita in range(10 ):
_snake_case : Any = digita
if (remainder + digita) % 2 == 0:
_snake_case : List[str] = ODD_DIGITS
else:
_snake_case : List[str] = EVEN_DIGITS
for digita in other_parity_digits:
_snake_case : List[Any] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a , a , )
return result
def a__ ( a : int = 9 ):
"""simple docstring"""
_snake_case : List[Any] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a , 0 , [0] * length , a )
return result
if __name__ == "__main__":
print(f'{solution() = }')
| 87 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_a : int = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _UpperCAmelCase ( _snake_case):
@staticmethod
def lowerCamelCase__ ( snake_case_ ):
_snake_case : Dict = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=snake_case_ , required=snake_case_ , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=snake_case_ , required=snake_case_ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=snake_case_ , required=snake_case_ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=snake_case_ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=snake_case_ , default=snake_case_ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : str = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
_snake_case : Optional[int] = model_type
_snake_case : Any = tf_checkpoint
_snake_case : Optional[int] = pytorch_dump_output
_snake_case : Tuple = config
_snake_case : Tuple = finetuning_task_name
def lowerCamelCase__ ( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case : int = self._tf_checkpoint
_snake_case : Optional[Any] = ""
else:
_snake_case : Optional[int] = self._tf_checkpoint
_snake_case : List[str] = ""
convert_transfo_xl_checkpoint_to_pytorch(
snake_case_ , self._config , self._pytorch_dump_output , snake_case_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 87 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : int = {"""vocab_file""": """sentencepiece.bpe.model"""}
_a : List[Any] = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
_a : Optional[Any] = {
"""moussaKam/mbarthez""": 1_024,
"""moussaKam/barthez""": 1_024,
"""moussaKam/barthez-orangesum-title""": 1_024,
}
_a : Union[str, Any] = """▁"""
class _UpperCAmelCase ( _snake_case):
__lowercase : Union[str, Any] = VOCAB_FILES_NAMES
__lowercase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case_ , snake_case_="<s>" , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_="<mask>" , snake_case_ = None , **snake_case_ , ):
# Mask token behave like a normal word, i.e. include the space before it
_snake_case : List[str] = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
_snake_case : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
_snake_case : Optional[Any] = vocab_file
_snake_case : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case_ ) )
_snake_case : Optional[Any] = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
_snake_case : Any = len(self.sp_model ) - 1
_snake_case : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case : List[Any] = [self.cls_token_id]
_snake_case : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1, 1] + ([0] * len(snake_case_ )) + [1]
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Dict = [self.sep_token_id]
_snake_case : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase__ ( self ):
return len(self.sp_model )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase__ ( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_snake_case : Optional[int] = self.sp_model.PieceToId(snake_case_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase__ ( self , snake_case_ ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : Optional[Any] = []
_snake_case : List[Any] = ""
_snake_case : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case_ ) + token
_snake_case : Tuple = True
_snake_case : int = []
else:
current_sub_tokens.append(snake_case_ )
_snake_case : Dict = False
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def __getstate__( self ):
_snake_case : Dict = self.__dict__.copy()
_snake_case : int = None
return state
def __setstate__( self , snake_case_ ):
_snake_case : str = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_snake_case : List[str] = {}
_snake_case : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_snake_case : Any = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , "wb" ) as fi:
_snake_case : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 87 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _UpperCAmelCase ( unittest.TestCase):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=3 , snake_case_=2_24 , snake_case_=30 , snake_case_=4_00 , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=[0.5, 0.5, 0.5] , snake_case_=[0.5, 0.5, 0.5] , ):
_snake_case : List[str] = size if size is not None else {"height": 18, "width": 18}
_snake_case : str = parent
_snake_case : str = batch_size
_snake_case : Dict = num_channels
_snake_case : Optional[int] = image_size
_snake_case : List[Any] = min_resolution
_snake_case : int = max_resolution
_snake_case : Any = do_resize
_snake_case : Dict = size
_snake_case : Dict = do_normalize
_snake_case : List[str] = image_mean
_snake_case : Optional[int] = image_std
def lowerCamelCase__ ( self ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Tuple = ViTImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = EfficientFormerImageProcessorTester(self )
@property
def lowerCamelCase__ ( self ):
return self.image_proc_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self ):
_snake_case : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , "image_mean" ) )
self.assertTrue(hasattr(snake_case_ , "image_std" ) )
self.assertTrue(hasattr(snake_case_ , "do_normalize" ) )
self.assertTrue(hasattr(snake_case_ , "do_resize" ) )
self.assertTrue(hasattr(snake_case_ , "size" ) )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
# Initialize image_processor
_snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , Image.Image )
# Test not batched input
_snake_case : Dict = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_snake_case : Optional[int] = image_processor(snake_case_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def lowerCamelCase__ ( self ):
# Initialize image_processor
_snake_case : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_snake_case : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=snake_case_ , numpify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , np.ndarray )
# Test not batched input
_snake_case : Dict = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_snake_case : Tuple = image_processor(snake_case_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def lowerCamelCase__ ( self ):
# Initialize image_processor
_snake_case : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_snake_case : Optional[int] = prepare_image_inputs(self.image_proc_tester , equal_resolution=snake_case_ , torchify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , torch.Tensor )
# Test not batched input
_snake_case : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_snake_case : Dict = image_processor(snake_case_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 87 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_a : Tuple = TypeVar("""KEY""")
_a : Any = TypeVar("""VAL""")
@dataclass(frozen=_snake_case , slots=_snake_case)
class _UpperCAmelCase ( Generic[KEY, VAL]):
__lowercase : KEY
__lowercase : VAL
class _UpperCAmelCase ( _Item):
def __init__( self ):
super().__init__(snake_case_ , snake_case_ )
def __bool__( self ):
return False
_a : List[Any] = _DeletedItem()
class _UpperCAmelCase ( MutableMapping[KEY, VAL]):
def __init__( self , snake_case_ = 8 , snake_case_ = 0.75 ):
_snake_case : Tuple = initial_block_size
_snake_case : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
_snake_case : List[str] = capacity_factor
_snake_case : Optional[Any] = 0
def lowerCamelCase__ ( self , snake_case_ ):
return hash(snake_case_ ) % len(self._buckets )
def lowerCamelCase__ ( self , snake_case_ ):
return (ind + 1) % len(self._buckets )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = self._buckets[ind]
if not stored:
_snake_case : Optional[int] = _Item(snake_case_ , snake_case_ )
self._len += 1
return True
elif stored.key == key:
_snake_case : Dict = _Item(snake_case_ , snake_case_ )
return True
else:
return False
def lowerCamelCase__ ( self ):
_snake_case : int = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(snake_case_ )
def lowerCamelCase__ ( self ):
if len(self._buckets ) <= self._initial_block_size:
return False
_snake_case : Union[str, Any] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : Union[str, Any] = self._buckets
_snake_case : Optional[Any] = [None] * new_size
_snake_case : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def lowerCamelCase__ ( self ):
self._resize(len(self._buckets ) * 2 )
def lowerCamelCase__ ( self ):
self._resize(len(self._buckets ) // 2 )
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : List[str] = self._get_bucket_index(snake_case_ )
for _ in range(len(self._buckets ) ):
yield ind
_snake_case : List[str] = self._get_next_ind(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ ):
for ind in self._iterate_buckets(snake_case_ ):
if self._try_set(snake_case_ , snake_case_ , snake_case_ ):
break
def __setitem__( self , snake_case_ , snake_case_ ):
if self._is_full():
self._size_up()
self._add_item(snake_case_ , snake_case_ )
def __delitem__( self , snake_case_ ):
for ind in self._iterate_buckets(snake_case_ ):
_snake_case : Tuple = self._buckets[ind]
if item is None:
raise KeyError(snake_case_ )
if item is _deleted:
continue
if item.key == key:
_snake_case : Dict = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , snake_case_ ):
for ind in self._iterate_buckets(snake_case_ ):
_snake_case : Any = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(snake_case_ )
def __len__( self ):
return self._len
def __iter__( self ):
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
_snake_case : Optional[int] = " ,".join(
F'{item.key}: {item.val}' for item in self._buckets if item )
return F'HashMap({val_string})'
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def a__ ( a : Any , a : Tuple , a : Union[str, Any] ):
"""simple docstring"""
_snake_case : Optional[int] = 0
if start < end:
_snake_case : Any = randint(a , a )
_snake_case : List[Any] = a[end]
_snake_case : int = a[pivot]
_snake_case : Optional[Any] = temp
_snake_case , _snake_case : Tuple = _in_place_partition(a , a , a )
count += _in_place_quick_sort(a , a , p - 1 )
count += _in_place_quick_sort(a , p + 1 , a )
return count
def a__ ( a : Tuple , a : Tuple , a : List[str] ):
"""simple docstring"""
_snake_case : Tuple = 0
_snake_case : str = randint(a , a )
_snake_case : Optional[int] = a[end]
_snake_case : Tuple = a[pivot]
_snake_case : Union[str, Any] = temp
_snake_case : Any = start - 1
for index in range(a , a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_snake_case : Union[str, Any] = new_pivot_index + 1
_snake_case : Any = a[new_pivot_index]
_snake_case : List[str] = a[index]
_snake_case : Optional[Any] = temp
_snake_case : Tuple = a[new_pivot_index + 1]
_snake_case : int = a[end]
_snake_case : int = temp
return new_pivot_index + 1, count
_a : Tuple = TemporaryFile()
_a : Tuple = 100 # 1000 elements are to be sorted
_a, _a : int = 0, 1 # mean and standard deviation
_a : Any = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
_a : Any = np.load(outfile)
_a : Optional[int] = len(M) - 1
_a : Dict = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 87 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : int = CodeGenTokenizer
__lowercase : Any = CodeGenTokenizerFast
__lowercase : List[Any] = True
__lowercase : List[Any] = {"""add_prefix_space""": True}
__lowercase : Union[str, Any] = False
def lowerCamelCase__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_snake_case : List[str] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_snake_case : Optional[int] = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
_snake_case : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_snake_case : List[Any] = {"unk_token": "<unk>"}
_snake_case : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_snake_case : List[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 lowerCamelCase__ ( self , **snake_case_ ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowerCamelCase__ ( self , **snake_case_ ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
_snake_case : List[str] = "lower newer"
_snake_case : Optional[Any] = "lower newer"
return input_text, output_text
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_snake_case : Union[str, Any] = "lower newer"
_snake_case : Any = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_snake_case : Optional[Any] = tokenizer.tokenize(snake_case_ , add_prefix_space=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_snake_case : Union[str, Any] = tokens + [tokenizer.unk_token]
_snake_case : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , snake_case_ )
def lowerCamelCase__ ( self ):
if not self.test_rust_tokenizer:
return
_snake_case : Optional[Any] = self.get_tokenizer()
_snake_case : List[Any] = self.get_rust_tokenizer(add_prefix_space=snake_case_ )
_snake_case : Tuple = "lower newer"
# Testing tokenization
_snake_case : Dict = tokenizer.tokenize(snake_case_ , add_prefix_space=snake_case_ )
_snake_case : Tuple = rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
# Testing conversion to ids without special tokens
_snake_case : Optional[Any] = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ , add_prefix_space=snake_case_ )
_snake_case : Optional[Any] = rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
# Testing conversion to ids with special tokens
_snake_case : Any = self.get_rust_tokenizer(add_prefix_space=snake_case_ )
_snake_case : Tuple = tokenizer.encode(snake_case_ , add_prefix_space=snake_case_ )
_snake_case : Any = rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
# Testing the unknown token
_snake_case : Optional[Any] = tokens + [rust_tokenizer.unk_token]
_snake_case : int = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case_ ) , snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase__ ( self , snake_case_=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_snake_case : str = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
# Simple input
_snake_case : List[str] = "This is a simple input"
_snake_case : Any = ["This is a simple input 1", "This is a simple input 2"]
_snake_case : Union[str, Any] = ("This is a simple input", "This is a pair")
_snake_case : Dict = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Simple input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Simple input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" , )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Pair input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" , )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
_snake_case : int = "This is a simple input"
_snake_case : int = ["This is a simple input looooooooong", "This is a simple input"]
_snake_case : Any = ("This is a simple input", "This is a pair")
_snake_case : Optional[int] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_snake_case : int = tokenizer.pad_token_id
_snake_case : Any = tokenizer(snake_case_ , padding="max_length" , max_length=30 , return_tensors="np" )
_snake_case : Any = tokenizer(snake_case_ , padding=snake_case_ , truncate=snake_case_ , return_tensors="np" )
_snake_case : Any = tokenizer(*snake_case_ , padding="max_length" , max_length=60 , return_tensors="np" )
_snake_case : Tuple = tokenizer(snake_case_ , padding=snake_case_ , truncate=snake_case_ , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase__ ( self ):
_snake_case : int = "$$$"
_snake_case : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=snake_case_ , add_bos_token=snake_case_ )
_snake_case : Union[str, Any] = "This is a simple input"
_snake_case : str = ["This is a simple input 1", "This is a simple input 2"]
_snake_case : Union[str, Any] = tokenizer.bos_token_id
_snake_case : Dict = tokenizer(snake_case_ )
_snake_case : Tuple = tokenizer(snake_case_ )
self.assertEqual(out_s.input_ids[0] , snake_case_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_snake_case : Optional[Any] = tokenizer.decode(out_s.input_ids )
_snake_case : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , snake_case_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_snake_case : Any = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_snake_case : Tuple = "\nif len_a > len_b: result = a\nelse: result = b"
_snake_case : Optional[Any] = tokenizer.encode(snake_case_ )
_snake_case : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_snake_case : List[Any] = tokenizer.decode(snake_case_ , truncate_before_pattern=snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
pass
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 | 1 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_a : Union[str, Any] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class _UpperCAmelCase ( nn.Module):
def __init__( self , snake_case_ ):
super().__init__()
_snake_case : str = torchvision.models.resnetaaa(pretrained=snake_case_ )
_snake_case : List[str] = list(model.children() )[:-2]
_snake_case : List[str] = nn.Sequential(*snake_case_ )
_snake_case : List[str] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCamelCase__ ( self , snake_case_ ):
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
_snake_case : int = self.pool(self.model(snake_case_ ) )
_snake_case : List[str] = torch.flatten(snake_case_ , start_dim=2 )
_snake_case : Optional[int] = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Tuple = [json.loads(snake_case_ ) for l in open(snake_case_ )]
_snake_case : Dict = os.path.dirname(snake_case_ )
_snake_case : List[str] = tokenizer
_snake_case : List[Any] = labels
_snake_case : str = len(snake_case_ )
_snake_case : Tuple = max_seq_length
_snake_case : str = transforms
def __len__( self ):
return len(self.data )
def __getitem__( self , snake_case_ ):
_snake_case : List[Any] = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] , add_special_tokens=snake_case_ ) )
_snake_case , _snake_case , _snake_case : int = sentence[0], sentence[1:-1], sentence[-1]
_snake_case : Union[str, Any] = sentence[: self.max_seq_length]
_snake_case : str = torch.zeros(self.n_classes )
_snake_case : str = 1
_snake_case : Any = Image.open(os.path.join(self.data_dir , self.data[index]["img"] ) ).convert("RGB" )
_snake_case : List[Any] = self.transforms(snake_case_ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCamelCase__ ( self ):
_snake_case : str = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def a__ ( a : Tuple ):
"""simple docstring"""
_snake_case : Optional[Any] = [len(row["sentence"] ) for row in batch]
_snake_case , _snake_case : Optional[Any] = len(a ), max(a )
_snake_case : Any = torch.zeros(a , a , dtype=torch.long )
_snake_case : List[Any] = torch.zeros(a , a , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(a , a ) ):
_snake_case : str = input_row["sentence"]
_snake_case : List[Any] = 1
_snake_case : Tuple = torch.stack([row["image"] for row in batch] )
_snake_case : Dict = torch.stack([row["label"] for row in batch] )
_snake_case : Any = torch.stack([row["image_start_token"] for row in batch] )
_snake_case : str = torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def a__ ( ):
"""simple docstring"""
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def a__ ( ):
"""simple docstring"""
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46777044, 0.44531429, 0.40661017] , std=[0.12221994, 0.12145835, 0.14380469] , ),
] )
| 87 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : str = {
"""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:
_a : Optional[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
_a : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
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
_a : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def a__ ( a : str , a : str ):
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
_snake_case : Optional[int] = XLMProphetNetForConditionalGenerationOld.from_pretrained(a )
_snake_case , _snake_case : Optional[int] = XLMProphetNetForConditionalGeneration.from_pretrained(
a , output_loading_info=a )
else:
_snake_case : Optional[Any] = ProphetNetForConditionalGenerationOld.from_pretrained(a )
_snake_case , _snake_case : List[Any] = ProphetNetForConditionalGeneration.from_pretrained(
a , output_loading_info=a )
_snake_case : Dict = ["key_proj", "value_proj", "query_proj"]
_snake_case : Optional[Any] = {
"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"]:
_snake_case : Optional[int] = key.split("." )
if attributes[0] == "lm_head":
_snake_case : Optional[int] = prophet
_snake_case : List[str] = prophet_old
else:
_snake_case : int = prophet.prophetnet
_snake_case : int = prophet_old.model
_snake_case : Dict = False
for attribute in attributes:
if attribute in mapping:
_snake_case : Tuple = mapping[attribute]
if not hasattr(a , a ) and len(a ) > 0:
_snake_case : Tuple = attribute
elif hasattr(a , a ):
_snake_case : List[Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
_snake_case : List[str] = old_model.weight
logger.info(f'{attribute} is initialized.' )
_snake_case : Optional[int] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
_snake_case : int = old_model.bias
logger.info(f'{attribute} is initialized' )
_snake_case : Union[str, Any] = True
break
elif attribute in special_keys and hasattr(a , "in_proj_weight" ):
_snake_case : List[Any] = old_model.in_proj_weight.shape[0] // 3
_snake_case : List[str] = getattr(a , a )
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":
_snake_case : Union[str, Any] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
_snake_case : List[str] = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
_snake_case : Tuple = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
_snake_case : str = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
_snake_case : Optional[int] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
_snake_case : Optional[int] = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
_snake_case : Union[str, Any] = 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] == 512, "We want 512 position_embeddings."
_snake_case : List[Any] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
_snake_case : Dict = True
break
if attribute.isdigit():
_snake_case : Optional[int] = model[int(a )]
_snake_case : List[str] = old_model[int(a )]
else:
_snake_case : Optional[Any] = getattr(a , a )
if old_attribute == "":
_snake_case : int = old_model
else:
if not hasattr(a , a ):
raise ValueError(f'{old_model} does not have {old_attribute}' )
_snake_case : str = getattr(a , a )
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(a )
if __name__ == "__main__":
_a : List[Any] = 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."""
)
_a : int = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 87 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_snake_case : List[Any] = Vector()
def lowerCamelCase__ ( self ):
_snake_case : Any = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case_ ) , "(0,0,0,0,0,1)" )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case_ ) , 4 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2] )
_snake_case : List[str] = Vector([1, 2, 3, 4, 5] )
_snake_case : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_snake_case : Any = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
_snake_case : Any = 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 lowerCamelCase__ ( self ):
_snake_case : str = Vector([1, 2, 3] )
_snake_case : Union[str, Any] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Vector([1, 2, 3] )
_snake_case : List[Any] = Vector([2, -1, 4] ) # for test of dot product
_snake_case : Union[str, Any] = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Vector([1, 2, 3] )
_snake_case : Optional[Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case_ , snake_case_ ) ) , "(3,4,7)" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Vector([1, 0, 0, 0, 0, 0] )
_snake_case : Optional[int] = x.copy()
self.assertEqual(str(snake_case_ ) , str(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case_ ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : str = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : str = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self ):
_snake_case : str = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_snake_case : List[str] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : int = 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 lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = 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 lowerCamelCase__ ( 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()
| 87 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( a : float , a : float , a : float ):
"""simple docstring"""
_snake_case : Optional[Any] = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=0.2 , snake_case_=0.2 ):
_snake_case : Optional[Any] = bp_numa
_snake_case : List[str] = bp_numa
_snake_case : str = bp_numa
_snake_case : List[str] = conva_get[:2]
_snake_case : Dict = conva_get[2]
_snake_case : List[Any] = size_pa
_snake_case : Optional[int] = rate_w
_snake_case : Dict = rate_t
_snake_case : Union[str, Any] = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
_snake_case : Any = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
_snake_case : Tuple = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
_snake_case : Any = -2 * np.random.rand(self.conva[1] ) + 1
_snake_case : List[str] = -2 * np.random.rand(self.num_bpa ) + 1
_snake_case : int = -2 * np.random.rand(self.num_bpa ) + 1
def lowerCamelCase__ ( self , snake_case_ ):
# save model dict with pickle
_snake_case : int = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(snake_case_ , "wb" ) as f:
pickle.dump(snake_case_ , snake_case_ )
print(F'Model saved： {save_path}' )
@classmethod
def lowerCamelCase__ ( cls , snake_case_ ):
# read saved model
with open(snake_case_ , "rb" ) as f:
_snake_case : List[str] = pickle.load(snake_case_ ) # noqa: S301
_snake_case : Tuple = model_dic.get("conv1" )
conv_get.append(model_dic.get("step_conv1" ) )
_snake_case : str = model_dic.get("size_pooling1" )
_snake_case : Dict = model_dic.get("num_bp1" )
_snake_case : Any = model_dic.get("num_bp2" )
_snake_case : Optional[int] = model_dic.get("num_bp3" )
_snake_case : str = model_dic.get("rate_weight" )
_snake_case : Optional[int] = model_dic.get("rate_thre" )
# create model instance
_snake_case : int = CNN(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# modify model parameter
_snake_case : int = model_dic.get("w_conv1" )
_snake_case : Any = model_dic.get("wkj" )
_snake_case : str = model_dic.get("vji" )
_snake_case : int = model_dic.get("thre_conv1" )
_snake_case : Any = model_dic.get("thre_bp2" )
_snake_case : List[Any] = model_dic.get("thre_bp3" )
return conv_ins
def lowerCamelCase__ ( self , snake_case_ ):
return 1 / (1 + np.exp(-1 * x ))
def lowerCamelCase__ ( self , snake_case_ ):
return round(snake_case_ , 3 )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
# convolution process
_snake_case : Any = convs[0]
_snake_case : Optional[Any] = convs[1]
_snake_case : int = np.shape(snake_case_ )[0]
# get the data slice of original image data, data_focus
_snake_case : int = []
for i_focus in range(0 , size_data - size_conv + 1 , snake_case_ ):
for j_focus in range(0 , size_data - size_conv + 1 , snake_case_ ):
_snake_case : Union[str, Any] = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(snake_case_ )
# calculate the feature map of every single kernel, and saved as list of matrix
_snake_case : Optional[int] = []
_snake_case : Dict = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(snake_case_ ):
_snake_case : int = []
for i_focus in range(len(snake_case_ ) ):
_snake_case : Tuple = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(snake_case_ ) )
_snake_case : int = np.asmatrix(snake_case_ ).reshape(
snake_case_ , snake_case_ )
data_featuremap.append(snake_case_ )
# expanding the data slice to One dimenssion
_snake_case : Optional[int] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(snake_case_ ) )
_snake_case : Optional[int] = np.asarray(snake_case_ )
return focus_list, data_featuremap
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_="average_pool" ):
# pooling process
_snake_case : List[str] = len(featuremaps[0] )
_snake_case : List[str] = int(size_map / size_pooling )
_snake_case : Optional[Any] = []
for i_map in range(len(snake_case_ ) ):
_snake_case : Union[str, Any] = featuremaps[i_map]
_snake_case : Union[str, Any] = []
for i_focus in range(0 , snake_case_ , snake_case_ ):
for j_focus in range(0 , snake_case_ , snake_case_ ):
_snake_case : List[str] = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(snake_case_ ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(snake_case_ ) )
_snake_case : List[Any] = np.asmatrix(snake_case_ ).reshape(snake_case_ , snake_case_ )
featuremap_pooled.append(snake_case_ )
return featuremap_pooled
def lowerCamelCase__ ( self , snake_case_ ):
# expanding three dimension data to one dimension list
_snake_case : List[str] = []
for i in range(len(snake_case_ ) ):
_snake_case : Optional[int] = np.shape(data[i] )
_snake_case : Dict = data[i].reshape(1 , shapes[0] * shapes[1] )
_snake_case : Any = data_listed.getA().tolist()[0]
data_expanded.extend(snake_case_ )
_snake_case : int = np.asarray(snake_case_ )
return data_expanded
def lowerCamelCase__ ( self , snake_case_ ):
# expanding matrix to one dimension list
_snake_case : Union[str, Any] = np.asarray(snake_case_ )
_snake_case : str = np.shape(snake_case_ )
_snake_case : int = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = []
_snake_case : Union[str, Any] = 0
for i_map in range(snake_case_ ):
_snake_case : Optional[int] = np.ones((size_map, size_map) )
for i in range(0 , snake_case_ , snake_case_ ):
for j in range(0 , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = pd_pool[
i_pool
]
_snake_case : Dict = i_pool + 1
_snake_case : Optional[Any] = np.multiply(
snake_case_ , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(snake_case_ )
return pd_all
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=bool ):
# model traning
print("----------------------Start Training-------------------------" )
print((" - - Shape: Train_Data ", np.shape(snake_case_ )) )
print((" - - Shape: Teach_Data ", np.shape(snake_case_ )) )
_snake_case : Any = 0
_snake_case : List[str] = []
_snake_case : int = 1_00_00
while rp < n_repeat and mse >= error_accuracy:
_snake_case : List[Any] = 0
print(F'-------------Learning Time {rp}--------------' )
for p in range(len(snake_case_ ) ):
# print('------------Learning Image: %d--------------'%p)
_snake_case : Dict = np.asmatrix(datas_train[p] )
_snake_case : Optional[Any] = np.asarray(datas_teach[p] )
_snake_case , _snake_case : Tuple = self.convolute(
snake_case_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_snake_case : List[str] = self.pooling(snake_case_ , self.size_poolinga )
_snake_case : Union[str, Any] = np.shape(snake_case_ )
_snake_case : Tuple = self._expand(snake_case_ )
_snake_case : Optional[Any] = data_bp_input
_snake_case : Dict = np.dot(snake_case_ , self.vji.T ) - self.thre_bpa
_snake_case : Any = self.sig(snake_case_ )
_snake_case : List[Any] = np.dot(snake_case_ , self.wkj.T ) - self.thre_bpa
_snake_case : List[str] = self.sig(snake_case_ )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
_snake_case : List[str] = np.multiply(
(data_teach - bp_outa) , np.multiply(snake_case_ , (1 - bp_outa) ) )
_snake_case : Dict = np.multiply(
np.dot(snake_case_ , self.wkj ) , np.multiply(snake_case_ , (1 - bp_outa) ) )
_snake_case : str = np.dot(snake_case_ , self.vji )
_snake_case : int = pd_i_all / (self.size_poolinga * self.size_poolinga)
_snake_case : List[Any] = pd_conva_pooled.T.getA().tolist()
_snake_case : int = self._calculate_gradient_from_pool(
snake_case_ , snake_case_ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
_snake_case : int = self._expand_mat(pd_conva_all[k_conv] )
_snake_case : Union[str, Any] = self.rate_weight * np.dot(snake_case_ , snake_case_ )
_snake_case : Optional[Any] = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
_snake_case : Tuple = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
_snake_case : List[str] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
_snake_case : Optional[Any] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
_snake_case : Dict = self.thre_bpa - pd_k_all * self.rate_thre
_snake_case : List[Any] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
_snake_case : Any = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
_snake_case : int = rp + 1
_snake_case : Tuple = error_count / patterns
all_mse.append(snake_case_ )
def draw_error():
_snake_case : Tuple = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(snake_case_ , "+-" )
plt.plot(snake_case_ , "r--" )
plt.xlabel("Learning Times" )
plt.ylabel("All_mse" )
plt.grid(snake_case_ , alpha=0.5 )
plt.show()
print("------------------Training Complished---------------------" )
print((" - - Training epoch: ", rp, F' - - Mse: {mse:.6f}') )
if draw_e:
draw_error()
return mse
def lowerCamelCase__ ( self , snake_case_ ):
# model predict
_snake_case : Dict = []
print("-------------------Start Testing-------------------------" )
print((" - - Shape: Test_Data ", np.shape(snake_case_ )) )
for p in range(len(snake_case_ ) ):
_snake_case : Union[str, Any] = np.asmatrix(datas_test[p] )
_snake_case , _snake_case : Dict = self.convolute(
snake_case_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_snake_case : int = self.pooling(snake_case_ , self.size_poolinga )
_snake_case : str = self._expand(snake_case_ )
_snake_case : Tuple = data_bp_input
_snake_case : Dict = bp_outa * self.vji.T - self.thre_bpa
_snake_case : List[Any] = self.sig(snake_case_ )
_snake_case : Union[str, Any] = bp_outa * self.wkj.T - self.thre_bpa
_snake_case : int = self.sig(snake_case_ )
produce_out.extend(bp_outa.getA().tolist() )
_snake_case : Optional[int] = [list(map(self.do_round , snake_case_ ) ) for each in produce_out]
return np.asarray(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
# return the data of image after convoluting process so we can check it out
_snake_case : Any = np.asmatrix(snake_case_ )
_snake_case , _snake_case : Tuple = self.convolute(
snake_case_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
_snake_case : List[str] = self.pooling(snake_case_ , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 87 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : Any = TextToVideoSDPipeline
__lowercase : str = TEXT_TO_IMAGE_PARAMS
__lowercase : int = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__lowercase : Optional[int] = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : str = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case : List[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , )
torch.manual_seed(0 )
_snake_case : Union[str, Any] = 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 )
_snake_case : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
_snake_case : Tuple = CLIPTextModel(snake_case_ )
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : str = torch.manual_seed(snake_case_ )
else:
_snake_case : Union[str, Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[Any] = self.get_dummy_components()
_snake_case : Tuple = TextToVideoSDPipeline(**snake_case_ )
_snake_case : List[str] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : int = self.get_dummy_inputs(snake_case_ )
_snake_case : Union[str, Any] = "np"
_snake_case : Dict = sd_pipe(**snake_case_ ).frames
_snake_case : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_snake_case : Dict = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_ , expected_max_diff=1E-2 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy" )
_snake_case : int = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_snake_case : Tuple = pipe.to("cuda" )
_snake_case : List[Any] = "Spiderman is surfing"
_snake_case : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : int = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="pt" ).frames
_snake_case : int = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def lowerCamelCase__ ( self ):
_snake_case : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy" )
_snake_case : str = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
_snake_case : int = pipe.to("cuda" )
_snake_case : Any = "Spiderman is surfing"
_snake_case : str = torch.Generator(device="cpu" ).manual_seed(0 )
_snake_case : Any = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=2 , output_type="pt" ).frames
_snake_case : Optional[int] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 87 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self , snake_case_ , snake_case_ ):
_snake_case : Tuple = jnp.ones((batch_size, length) ) / length
return scores
def lowerCamelCase__ ( self ):
_snake_case : Dict = None
_snake_case : int = 20
_snake_case : Tuple = self._get_uniform_logits(batch_size=2 , length=snake_case_ )
# tweak scores to not be uniform anymore
_snake_case : Optional[Any] = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
_snake_case : Tuple = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
_snake_case : Union[str, Any] = jax.nn.softmax(snake_case_ , axis=-1 )
_snake_case : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
_snake_case : Optional[int] = FlaxTemperatureLogitsWarper(temperature=1.3 )
_snake_case : Optional[Any] = jax.nn.softmax(temp_dist_warper_sharper(snake_case_ , scores.copy() , cur_len=snake_case_ ) , axis=-1 )
_snake_case : Dict = jax.nn.softmax(temp_dist_warper_smoother(snake_case_ , scores.copy() , cur_len=snake_case_ ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = None
_snake_case : Tuple = 10
_snake_case : str = 2
# create ramp distribution
_snake_case : Any = np.broadcast_to(np.arange(snake_case_ )[None, :] , (batch_size, vocab_size) ).copy()
_snake_case : Tuple = ramp_logits[1:, : vocab_size // 2] + vocab_size
_snake_case : Union[str, Any] = FlaxTopKLogitsWarper(3 )
_snake_case : Dict = top_k_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
_snake_case : Any = 5
_snake_case : Optional[int] = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
_snake_case : List[str] = np.broadcast_to(np.arange(snake_case_ )[None, :] , (batch_size, length) ).copy()
_snake_case : str = top_k_warp_safety_check(snake_case_ , snake_case_ , cur_len=snake_case_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def lowerCamelCase__ ( self ):
_snake_case : Any = None
_snake_case : Any = 10
_snake_case : str = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
_snake_case : List[str] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
_snake_case : Tuple = FlaxTopPLogitsWarper(0.8 )
_snake_case : Tuple = np.exp(top_p_warp(snake_case_ , snake_case_ , cur_len=snake_case_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
_snake_case : Any = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1E-3 ) )
# check edge cases with negative and extreme logits
_snake_case : Optional[int] = np.broadcast_to(np.arange(snake_case_ )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
_snake_case : Dict = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
_snake_case : Any = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
_snake_case : int = top_p_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def lowerCamelCase__ ( self ):
_snake_case : Any = 20
_snake_case : Dict = 4
_snake_case : Union[str, Any] = 0
_snake_case : Tuple = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=snake_case_ )
# check that min length is applied at length 5
_snake_case : Optional[int] = ids_tensor((batch_size, 20) , vocab_size=20 )
_snake_case : int = 5
_snake_case : int = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : Any = min_dist_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf" )] )
# check that min length is not applied anymore at length 15
_snake_case : Optional[int] = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : Union[str, Any] = 15
_snake_case : Optional[Any] = min_dist_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertFalse(jnp.isinf(snake_case_ ).any() )
def lowerCamelCase__ ( self ):
_snake_case : int = 20
_snake_case : Dict = 4
_snake_case : Optional[int] = 0
_snake_case : str = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_ )
# check that all scores are -inf except the bos_token_id score
_snake_case : List[str] = ids_tensor((batch_size, 1) , vocab_size=20 )
_snake_case : Union[str, Any] = 1
_snake_case : Any = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : List[Any] = logits_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
_snake_case : Optional[Any] = 3
_snake_case : Dict = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : List[str] = logits_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertFalse(jnp.isinf(snake_case_ ).any() )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = 20
_snake_case : List[str] = 4
_snake_case : Union[str, Any] = 0
_snake_case : Dict = 5
_snake_case : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case_ , eos_token_id=snake_case_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
_snake_case : List[str] = ids_tensor((batch_size, 4) , vocab_size=20 )
_snake_case : Union[str, Any] = 4
_snake_case : Optional[Any] = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : Optional[Any] = logits_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
_snake_case : Union[str, Any] = 3
_snake_case : List[Any] = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : int = logits_processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
self.assertFalse(jnp.isinf(snake_case_ ).any() )
def lowerCamelCase__ ( self ):
_snake_case : Any = 4
_snake_case : Any = 10
_snake_case : List[Any] = 15
_snake_case : List[Any] = 2
_snake_case : Dict = 1
_snake_case : Optional[Any] = 15
# dummy input_ids and scores
_snake_case : Any = ids_tensor((batch_size, sequence_length) , snake_case_ )
_snake_case : int = input_ids.copy()
_snake_case : Optional[int] = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : Tuple = scores.copy()
# instantiate all dist processors
_snake_case : Optional[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
_snake_case : Dict = FlaxTopKLogitsWarper(3 )
_snake_case : Dict = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_snake_case : Dict = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=snake_case_ )
_snake_case : Any = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_ )
_snake_case : int = FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case_ , eos_token_id=snake_case_ )
_snake_case : Any = 10
# no processor list
_snake_case : List[str] = temp_dist_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Tuple = top_k_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : int = top_p_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Optional[int] = min_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Union[str, Any] = bos_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : List[str] = eos_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
# with processor list
_snake_case : int = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_snake_case : Any = processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
# scores should be equal
self.assertTrue(jnp.allclose(snake_case_ , snake_case_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = 4
_snake_case : Union[str, Any] = 10
_snake_case : Optional[int] = 15
_snake_case : List[Any] = 2
_snake_case : List[Any] = 1
_snake_case : Optional[Any] = 15
# dummy input_ids and scores
_snake_case : Any = ids_tensor((batch_size, sequence_length) , snake_case_ )
_snake_case : Dict = input_ids.copy()
_snake_case : Tuple = self._get_uniform_logits(snake_case_ , snake_case_ )
_snake_case : int = scores.copy()
# instantiate all dist processors
_snake_case : str = FlaxTemperatureLogitsWarper(temperature=0.5 )
_snake_case : Optional[Any] = FlaxTopKLogitsWarper(3 )
_snake_case : Any = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_snake_case : str = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=snake_case_ )
_snake_case : str = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_ )
_snake_case : Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case_ , eos_token_id=snake_case_ )
_snake_case : Dict = 10
# no processor list
def run_no_processor_list(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = temp_dist_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : str = top_k_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : int = top_p_warp(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Tuple = min_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Optional[Any] = bos_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
_snake_case : Optional[Any] = eos_dist_proc(snake_case_ , snake_case_ , cur_len=snake_case_ )
return scores
# with processor list
def run_processor_list(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_snake_case : Optional[int] = processor(snake_case_ , snake_case_ , cur_len=snake_case_ )
return scores
_snake_case : Tuple = jax.jit(snake_case_ )
_snake_case : List[str] = jax.jit(snake_case_ )
_snake_case : int = jitted_run_no_processor_list(snake_case_ , snake_case_ , snake_case_ )
_snake_case : int = jitted_run_processor_list(snake_case_ , snake_case_ , snake_case_ )
# scores should be equal
self.assertTrue(jnp.allclose(snake_case_ , snake_case_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 87 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _UpperCAmelCase ( _snake_case):
__lowercase : int = """EncodecFeatureExtractor"""
__lowercase : str = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
_snake_case : Dict = self.feature_extractor
_snake_case : Any = False
def lowerCamelCase__ ( self , snake_case_=None , snake_case_=None , snake_case_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ , language=snake_case_ , no_timestamps=snake_case_ )
def __call__( self , *snake_case_ , **snake_case_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
_snake_case : str = kwargs.pop("audio" , snake_case_ )
_snake_case : Optional[int] = kwargs.pop("sampling_rate" , snake_case_ )
_snake_case : Optional[Any] = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Any = args[0]
_snake_case : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_snake_case : Any = self.tokenizer(snake_case_ , **snake_case_ )
if audio is not None:
_snake_case : Any = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_snake_case : str = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_snake_case : List[str] = audio_inputs["padding_mask"]
return inputs
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
_snake_case : Tuple = kwargs.pop("audio" , snake_case_ )
_snake_case : List[str] = kwargs.pop("padding_mask" , snake_case_ )
if len(snake_case_ ) > 0:
_snake_case : Tuple = args[0]
_snake_case : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(snake_case_ , padding_mask=snake_case_ )
else:
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : Optional[int] = to_numpy(snake_case_ )
_snake_case , _snake_case , _snake_case : Tuple = audio_values.shape
if padding_mask is None:
return list(snake_case_ )
_snake_case : Optional[int] = to_numpy(snake_case_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_snake_case : Any = seq_len - padding_mask.shape[-1]
_snake_case : Optional[Any] = 1 - self.feature_extractor.padding_value
_snake_case : Optional[int] = np.pad(snake_case_ , ((0, 0), (0, difference)) , "constant" , constant_values=snake_case_ )
_snake_case : Any = audio_values.tolist()
for i in range(snake_case_ ):
_snake_case : Tuple = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_snake_case : Tuple = sliced_audio.reshape(snake_case_ , -1 )
return audio_values
| 87 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( _snake_case):
@staticmethod
@abstractmethod
def lowerCamelCase__ ( snake_case_ ):
raise NotImplementedError()
@abstractmethod
def lowerCamelCase__ ( self ):
raise NotImplementedError()
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""YolosFeatureExtractor"""]
_a : List[Any] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
_a : List[Any] = {
"""return_dict""": False,
"""output_hidden_states""": True,
"""output_attentions""": True,
"""torchscript""": True,
"""torch_dtype""": """float16""",
"""use_bfloat16""": True,
"""tf_legacy_loss""": True,
"""pruned_heads""": {"""a""": 1},
"""tie_word_embeddings""": False,
"""is_decoder""": True,
"""cross_attention_hidden_size""": 128,
"""add_cross_attention""": True,
"""tie_encoder_decoder""": True,
"""max_length""": 50,
"""min_length""": 3,
"""do_sample""": True,
"""early_stopping""": True,
"""num_beams""": 3,
"""num_beam_groups""": 3,
"""diversity_penalty""": 0.5,
"""temperature""": 2.0,
"""top_k""": 10,
"""top_p""": 0.7,
"""typical_p""": 0.2,
"""repetition_penalty""": 0.8,
"""length_penalty""": 0.8,
"""no_repeat_ngram_size""": 5,
"""encoder_no_repeat_ngram_size""": 5,
"""bad_words_ids""": [1, 2, 3],
"""num_return_sequences""": 3,
"""chunk_size_feed_forward""": 5,
"""output_scores""": True,
"""return_dict_in_generate""": True,
"""forced_bos_token_id""": 2,
"""forced_eos_token_id""": 3,
"""remove_invalid_values""": True,
"""architectures""": ["""BertModel"""],
"""finetuning_task""": """translation""",
"""id2label""": {0: """label"""},
"""label2id""": {"""label""": """0"""},
"""tokenizer_class""": """BertTokenizerFast""",
"""prefix""": """prefix""",
"""bos_token_id""": 6,
"""pad_token_id""": 7,
"""eos_token_id""": 8,
"""sep_token_id""": 9,
"""decoder_start_token_id""": 10,
"""exponential_decay_length_penalty""": (5, 1.01),
"""suppress_tokens""": [0, 1],
"""begin_suppress_tokens""": 2,
"""task_specific_params""": {"""translation""": """some_params"""},
"""problem_type""": """regression""",
}
@is_staging_test
class _UpperCAmelCase ( unittest.TestCase):
@classmethod
def lowerCamelCase__ ( cls ):
_snake_case : Union[str, Any] = TOKEN
HfFolder.save_token(snake_case_ )
@classmethod
def lowerCamelCase__ ( cls ):
try:
delete_repo(token=cls._token , repo_id="test-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config" )
except HTTPError:
pass
def lowerCamelCase__ ( self ):
_snake_case : str = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("test-config" , use_auth_token=self._token )
_snake_case : List[Any] = BertConfig.from_pretrained(F'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case_ , getattr(snake_case_ , snake_case_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(snake_case_ , repo_id="test-config" , push_to_hub=snake_case_ , use_auth_token=self._token )
_snake_case : Tuple = BertConfig.from_pretrained(F'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case_ , getattr(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token )
_snake_case : List[Any] = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case_ , getattr(snake_case_ , snake_case_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
snake_case_ , repo_id="valid_org/test-config-org" , push_to_hub=snake_case_ , use_auth_token=self._token )
_snake_case : List[Any] = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case_ , getattr(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
CustomConfig.register_for_auto_class()
_snake_case : List[Any] = CustomConfig(attribute=42 )
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"} )
_snake_case : Optional[int] = AutoConfig.from_pretrained(F'{USER}/test-dynamic-config' , trust_remote_code=snake_case_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig" )
self.assertEqual(new_config.attribute , 42 )
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_snake_case : Optional[Any] = c.n_embd + 1 # int
_snake_case : Tuple = c.resid_pdrop + 1.0 # float
_snake_case : int = not c.scale_attn_weights # bool
_snake_case : str = c.summary_type + "foo" # str
c.update_from_string(
F'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}' )
self.assertEqual(snake_case_ , c.n_embd , "mismatch for key: n_embd" )
self.assertEqual(snake_case_ , c.resid_pdrop , "mismatch for key: resid_pdrop" )
self.assertEqual(snake_case_ , c.scale_attn_weights , "mismatch for key: scale_attn_weights" )
self.assertEqual(snake_case_ , c.summary_type , "mismatch for key: summary_type" )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = PretrainedConfig()
_snake_case : Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
snake_case_ , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"] )
_snake_case : int = [key for key, value in config_common_kwargs.items() if value == getattr(snake_case_ , snake_case_ )]
if len(snake_case_ ) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
F' {", ".join(snake_case_ )}.' )
def lowerCamelCase__ ( self ):
with self.assertRaises(snake_case_ ):
# config is in subfolder, the following should not work without specifying the subfolder
_snake_case : Optional[int] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" )
_snake_case : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert" )
self.assertIsNotNone(snake_case_ )
def lowerCamelCase__ ( self ):
# A mock response for an HTTP head request to emulate server down
_snake_case : Optional[int] = mock.Mock()
_snake_case : Dict = 5_00
_snake_case : Union[str, Any] = {}
_snake_case : List[Any] = HTTPError
_snake_case : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_snake_case : Optional[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=snake_case_ ) as mock_head:
_snake_case : str = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCamelCase__ ( self ):
# This test is for deprecated behavior and can be removed in v5
_snake_case : Union[str, Any] = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = AutoConfig.from_pretrained("bert-base-cased" )
_snake_case : Dict = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(snake_case_ )
_snake_case : Optional[Any] = 2
json.dump(configuration.to_dict() , open(os.path.join(snake_case_ , "config.4.0.0.json" ) , "w" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_snake_case : List[Any] = AutoConfig.from_pretrained(snake_case_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_snake_case : Optional[Any] = ["config.42.0.0.json"]
_snake_case : str = 7_68
configuration.save_pretrained(snake_case_ )
shutil.move(os.path.join(snake_case_ , "config.4.0.0.json" ) , os.path.join(snake_case_ , "config.42.0.0.json" ) )
_snake_case : Any = AutoConfig.from_pretrained(snake_case_ )
self.assertEqual(new_configuration.hidden_size , 7_68 )
def lowerCamelCase__ ( self ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_snake_case : Optional[Any] = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
_snake_case : Tuple = "v4.0.0"
_snake_case , _snake_case : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
snake_case_ , return_unused_kwargs=snake_case_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(snake_case_ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_snake_case : str = "v3.0.0"
_snake_case : Optional[int] = old_transformers.models.auto.AutoConfig.from_pretrained(snake_case_ )
self.assertEqual(old_configuration.hidden_size , 7_68 )
| 87 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = dataset
_snake_case : str = process
_snake_case : int = params
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
_snake_case : Union[str, Any] = self.dataset[i]
_snake_case : Optional[Any] = self.process(snake_case_ , **self.params )
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
_snake_case : Union[str, Any] = loader
_snake_case : Tuple = infer
_snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_snake_case : int = None
_snake_case : int = loader_batch_size
# Internal bookkeeping
_snake_case : Any = None
_snake_case : Dict = None
def __len__( self ):
return len(self.loader )
def __iter__( self ):
_snake_case : int = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_snake_case : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
_snake_case : Tuple = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_snake_case : Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : int = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_snake_case : Tuple = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : Tuple = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
_snake_case : Tuple = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : List[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_snake_case : Union[str, Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_snake_case : List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_snake_case : int = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def lowerCamelCase__ ( self ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_snake_case : Tuple = next(self.iterator )
_snake_case : Any = self.infer(snake_case_ , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Optional[int] = list(processed.keys() )[0]
_snake_case : List[str] = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = len(snake_case_ )
else:
_snake_case : Optional[int] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_snake_case : str = processed
_snake_case : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
_snake_case : Tuple = iter(self.loader )
_snake_case : List[Any] = None
return self
def lowerCamelCase__ ( self ):
if self.subiterator is None:
_snake_case : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_snake_case : Union[str, Any] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_snake_case : str = self.infer(next(self.iterator ) , **self.params )
_snake_case : Tuple = next(self.subiterator )
return processed
class _UpperCAmelCase ( _snake_case):
def __iter__( self ):
_snake_case : Optional[Any] = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
_snake_case : Optional[Any] = False
_snake_case : Tuple = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : str = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
_snake_case : List[str] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Tuple = list(processed.keys() )[0]
_snake_case : Tuple = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Any = len(snake_case_ )
else:
_snake_case : List[Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_snake_case : Dict = observed_batch_size
_snake_case : List[Any] = processed
_snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : int = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
_snake_case : Dict = processed
_snake_case : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ ):
_snake_case : str = dataset
_snake_case : Any = key
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return self.dataset[i][self.key]
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = dataset
_snake_case : Any = keya
_snake_case : int = keya
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 87 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 |
"""simple docstring"""
def a__ ( a : int ):
"""simple docstring"""
if not isinstance(a , a ):
raise TypeError("Input value must be an 'int' type" )
_snake_case : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
from __future__ import annotations
import requests
_a : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( a : str , a : int = 1 , a : str = "new" , a : list | None = None ):
"""simple docstring"""
_snake_case : Any = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(a ) - valid_terms ) ):
_snake_case : Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(a )
_snake_case : int = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"User-agent": "A random string"} , )
if response.status_code == 429:
raise requests.HTTPError
_snake_case : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(a )}
_snake_case : Tuple = {}
for id_ in range(a ):
_snake_case : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 87 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : Union[str, Any] = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = [
"""NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NezhaForNextSentencePrediction""",
"""NezhaForMaskedLM""",
"""NezhaForPreTraining""",
"""NezhaForMultipleChoice""",
"""NezhaForQuestionAnswering""",
"""NezhaForSequenceClassification""",
"""NezhaForTokenClassification""",
"""NezhaModel""",
"""NezhaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
_a : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a__ ( a : float , a : float , a : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(a ), magnitude * sin(a )]
return [magnitude * cos(radians(a ) ), magnitude * sin(radians(a ) )]
def a__ ( a : NDArray[floataa] , a : NDArray[floataa] , a : float = 10**-1 ):
"""simple docstring"""
_snake_case : NDArray[floataa] = cross(a , a )
_snake_case : float = sum(a )
return abs(a ) < eps
if __name__ == "__main__":
# Test to check if it works
_a : Tuple = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
_a : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_a : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
_a : List[Any] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_a : List[str] = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
_a : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : int = KandinskyVaaPipeline
__lowercase : List[str] = [
"""image_embeds""",
"""negative_image_embeds""",
]
__lowercase : str = ["""image_embeds""", """negative_image_embeds"""]
__lowercase : Optional[int] = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__lowercase : str = False
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return 32
@property
def lowerCamelCase__ ( self ):
return self.time_input_dim
@property
def lowerCamelCase__ ( self ):
return self.time_input_dim * 4
@property
def lowerCamelCase__ ( self ):
return 1_00
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : int = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
_snake_case : Optional[Any] = UNetaDConditionModel(**snake_case_ )
return model
@property
def lowerCamelCase__ ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase__ ( self ):
_snake_case : str = self.dummy_unet
_snake_case : Optional[Any] = self.dummy_movq
_snake_case : Optional[int] = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="linear" , beta_start=0.00085 , beta_end=0.012 , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , steps_offset=1 , prediction_type="epsilon" , thresholding=snake_case_ , )
_snake_case : Optional[int] = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
_snake_case : Dict = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_snake_case : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case_ )
if str(snake_case_ ).startswith("mps" ):
_snake_case : Dict = torch.manual_seed(snake_case_ )
else:
_snake_case : Optional[int] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : Optional[int] = {
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : List[str] = "cpu"
_snake_case : str = self.get_dummy_components()
_snake_case : str = self.pipeline_class(**snake_case_ )
_snake_case : str = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Any = pipe(**self.get_dummy_inputs(snake_case_ ) )
_snake_case : Any = output.images
_snake_case : Any = pipe(
**self.get_dummy_inputs(snake_case_ ) , return_dict=snake_case_ , )[0]
_snake_case : List[Any] = image[0, -3:, -3:, -1]
_snake_case : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : int = np.array(
[0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_snake_case : List[str] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy" )
_snake_case : Dict = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(snake_case_ )
_snake_case : Dict = KandinskyVaaPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
_snake_case : Dict = pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
_snake_case : Tuple = "red cat, 4k photo"
_snake_case : Optional[Any] = torch.Generator(device="cuda" ).manual_seed(0 )
_snake_case , _snake_case : Optional[int] = pipe_prior(
snake_case_ , generator=snake_case_ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
_snake_case : Any = torch.Generator(device="cuda" ).manual_seed(0 )
_snake_case : Any = pipeline(
image_embeds=snake_case_ , negative_image_embeds=snake_case_ , generator=snake_case_ , num_inference_steps=1_00 , output_type="np" , )
_snake_case : Optional[int] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """openai-gpt"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=4_04_78 , snake_case_=5_12 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_="cls_index" , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=0.1 , **snake_case_ , ):
_snake_case : Tuple = vocab_size
_snake_case : Dict = n_positions
_snake_case : Any = n_embd
_snake_case : Any = n_layer
_snake_case : Optional[int] = n_head
_snake_case : Union[str, Any] = afn
_snake_case : Dict = resid_pdrop
_snake_case : str = embd_pdrop
_snake_case : Union[str, Any] = attn_pdrop
_snake_case : str = layer_norm_epsilon
_snake_case : Union[str, Any] = initializer_range
_snake_case : Any = summary_type
_snake_case : List[str] = summary_use_proj
_snake_case : Optional[int] = summary_activation
_snake_case : Union[str, Any] = summary_first_dropout
_snake_case : Optional[int] = summary_proj_to_labels
super().__init__(**snake_case_ )
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
_a : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def a__ ( a : str , a : int = 1 , a : str = "new" , a : list | None = None ):
"""simple docstring"""
_snake_case : Any = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(a ) - valid_terms ) ):
_snake_case : Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(a )
_snake_case : int = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"User-agent": "A random string"} , )
if response.status_code == 429:
raise requests.HTTPError
_snake_case : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(a )}
_snake_case : Tuple = {}
for id_ in range(a ):
_snake_case : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 87 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_a : int = []
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}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_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'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""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"""),
]
)
def a__ ( a : List[str] , a : int , a : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = state_dict.pop(a )
_snake_case : Union[str, Any] = val
def a__ ( a : Tuple ):
"""simple docstring"""
_snake_case : Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_snake_case : Dict = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
_snake_case : Tuple = value
else:
_snake_case : Dict = value
return new_state_dict
def a__ ( a : int ):
"""simple docstring"""
_snake_case : Any = ""
# 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)
_snake_case : str = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : str = 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
_snake_case : int = in_proj_weight[:256, :]
_snake_case : List[str] = in_proj_bias[:256]
_snake_case : Optional[Any] = in_proj_weight[256:512, :]
_snake_case : List[str] = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : Dict = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_snake_case : List[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
_snake_case : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_snake_case : Union[str, Any] = in_proj_weight[:256, :]
_snake_case : Tuple = in_proj_bias[:256]
_snake_case : int = in_proj_weight[256:512, :]
_snake_case : int = in_proj_bias[256:512]
_snake_case : Dict = in_proj_weight[-256:, :]
_snake_case : str = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_snake_case : Dict = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
_snake_case : Optional[int] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_snake_case : Dict = in_proj_weight_cross_attn[:256, :]
_snake_case : Any = in_proj_bias_cross_attn[:256]
_snake_case : Union[str, Any] = in_proj_weight_cross_attn[256:512, :]
_snake_case : Optional[int] = in_proj_bias_cross_attn[256:512]
_snake_case : Any = in_proj_weight_cross_attn[-256:, :]
_snake_case : str = in_proj_bias_cross_attn[-256:]
def a__ ( a : str , a : int ):
"""simple docstring"""
_snake_case , _snake_case : List[str] = image.size
_snake_case : Dict = max(a , a )
_snake_case : Union[str, Any] = 800 if "detection" in checkpoint_url else 1_000
_snake_case : Any = target_max_size / current_max_size
_snake_case : str = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( a : str ):
"""simple docstring"""
_snake_case : str = F.to_tensor(a )
_snake_case : Union[str, Any] = F.normalize(a , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( a : Optional[Any] , a : Any , a : Union[str, Any] ):
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
_snake_case : Tuple = torch.hub.load_state_dict_from_url(a , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(a , a , a )
_snake_case : Union[str, Any] = rename_backbone_keys(a )
# query, key and value matrices need special treatment
read_in_q_k_v(a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_snake_case : int = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_snake_case : Optional[int] = state_dict.pop(a )
_snake_case : Any = val
# create HuggingFace model and load state dict
_snake_case : Tuple = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
_snake_case : Any = 15
_snake_case : int = 2
_snake_case : Optional[Any] = {0: "table", 1: "table rotated"}
_snake_case : Union[str, Any] = idalabel
_snake_case : Union[str, Any] = {v: k for k, v in idalabel.items()}
else:
_snake_case : Any = 125
_snake_case : Union[str, Any] = 6
_snake_case : List[str] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
_snake_case : Any = idalabel
_snake_case : Optional[int] = {v: k for k, v in idalabel.items()}
_snake_case : Union[str, Any] = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1_000 )
_snake_case : str = TableTransformerForObjectDetection(a )
model.load_state_dict(a )
model.eval()
# verify our conversion
_snake_case : Optional[int] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
_snake_case : Optional[Any] = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=a )
_snake_case : Dict = Image.open(a ).convert("RGB" )
_snake_case : Union[str, Any] = normalize(resize(a , a ) ).unsqueeze(0 )
_snake_case : str = model(a )
if "detection" in checkpoint_url:
_snake_case : int = (1, 15, 3)
_snake_case : List[str] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
_snake_case : List[str] = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
_snake_case : Union[str, Any] = (1, 125, 7)
_snake_case : str = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
_snake_case : Optional[Any] = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , a , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
_snake_case : int = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(a )
image_processor.push_to_hub(a )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint 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."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : Any = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 87 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
_a : Optional[int] = tuple[int, int]
class _UpperCAmelCase :
def __init__( self ):
_snake_case : Any = []
_snake_case : int = set()
def lowerCamelCase__ ( self ):
if not self.empty():
return self.elements[0][0]
else:
return float("inf" )
def lowerCamelCase__ ( self ):
return len(self.elements ) == 0
def lowerCamelCase__ ( self , snake_case_ , snake_case_ ):
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(snake_case_ )
else:
# update
# print("update", item)
_snake_case : int = []
((_snake_case) , (_snake_case)) : Dict = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((_snake_case) , (_snake_case)) : Any = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def lowerCamelCase__ ( self , snake_case_ ):
if item in self.set:
self.set.remove(snake_case_ )
_snake_case : str = []
((_snake_case) , (_snake_case)) : Optional[Any] = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((_snake_case) , (_snake_case)) : Optional[Any] = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def lowerCamelCase__ ( self ):
return self.elements[0][1]
def lowerCamelCase__ ( self ):
((_snake_case) , (_snake_case)) : int = heapq.heappop(self.elements )
self.set.remove(snake_case_ )
return (priority, item)
def a__ ( a : TPos , a : TPos ):
"""simple docstring"""
_snake_case : str = np.array(a )
_snake_case : Optional[int] = np.array(a )
return np.linalg.norm(a - b )
def a__ ( a : TPos , a : TPos ):
"""simple docstring"""
return consistent_heuristic(a , a ) // t
def a__ ( a : TPos , a : TPos ):
"""simple docstring"""
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def a__ ( a : TPos , a : int , a : TPos , a : dict[TPos, float] ):
"""simple docstring"""
_snake_case : List[str] = g_function[start] + Wa * heuristics[i](a , a )
return ans
def a__ ( a : Dict , a : List[Any] , a : Tuple ):
"""simple docstring"""
_snake_case : List[str] = np.chararray((n, n) )
for i in range(a ):
for j in range(a ):
_snake_case : List[Any] = "*"
for i in range(a ):
for j in range(a ):
if (j, (n - 1) - i) in blocks:
_snake_case : List[Any] = "#"
_snake_case : List[Any] = "-"
_snake_case : Dict = back_pointer[goal]
while x != start:
((_snake_case) , (_snake_case)) : List[Any] = x
# print(x)
_snake_case : Optional[Any] = "-"
_snake_case : int = back_pointer[x]
_snake_case : Optional[Any] = "-"
for i in range(a ):
for j in range(a ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=" " )
print("<-- End position" , end=" " )
else:
print(grid[i][j] , end=" " )
print()
print("^" )
print("Start position" )
print()
print("# is an obstacle" )
print("- is the path taken by algorithm" )
print("PATH TAKEN BY THE ALGORITHM IS:-" )
_snake_case : List[str] = back_pointer[goal]
while x != start:
print(a , end=" " )
_snake_case : Tuple = back_pointer[x]
print(a )
sys.exit()
def a__ ( a : TPos ):
"""simple docstring"""
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def a__ ( a : Optional[int] , a : List[str] , a : List[Any] , a : Optional[Any] , a : Tuple , a : Optional[Any] , a : Any , a : List[str] , ):
"""simple docstring"""
for itera in range(a ):
open_list[itera].remove_element(a )
# print("s", s)
# print("j", j)
((_snake_case) , (_snake_case)) : int = s
_snake_case : Union[str, Any] = (x - 1, y)
_snake_case : Optional[Any] = (x + 1, y)
_snake_case : List[Any] = (x, y + 1)
_snake_case : int = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(a ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(a )
_snake_case : Optional[int] = -1
_snake_case : List[str] = float("inf" )
if valid(a ) and g_function[neighbours] > g_function[s] + 1:
_snake_case : int = g_function[s] + 1
_snake_case : str = s
if neighbours not in close_list_anchor:
open_list[0].put(a , key(a , 0 , a , a ) )
if neighbours not in close_list_inad:
for var in range(1 , a ):
if key(a , a , a , a ) <= Wa * key(
a , 0 , a , a ):
open_list[j].put(
a , key(a , a , a , a ) )
def a__ ( ):
"""simple docstring"""
_snake_case : Optional[int] = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
_a : str = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
_a : Union[str, Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
_a : int = make_common_ground()
_a : Tuple = blocks_blk
# hyper parameters
_a : Any = 1
_a : List[Any] = 1
_a : List[Any] = 20
_a : List[str] = 3 # one consistent and two other inconsistent
# start and end destination
_a : List[Any] = (0, 0)
_a : int = (n - 1, n - 1)
_a : Any = 1
def a__ ( a : TPos , a : TPos , a : int ):
"""simple docstring"""
_snake_case : Tuple = {start: 0, goal: float("inf" )}
_snake_case : List[str] = {start: -1, goal: -1}
_snake_case : Any = []
_snake_case : Union[str, Any] = set()
for i in range(a ):
open_list.append(PriorityQueue() )
open_list[i].put(a , key(a , a , a , a ) )
_snake_case : list[int] = []
_snake_case : list[int] = []
while open_list[0].minkey() < float("inf" ):
for i in range(1 , a ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("inf" ):
do_something(a , a , a )
else:
_snake_case , _snake_case : Optional[Any] = open_list[i].top_show()
visited.add(a )
expand_state(
a , a , a , a , a , a , a , a , )
close_list_inad.append(a )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("inf" ):
do_something(a , a , a )
else:
_snake_case : Any = open_list[0].top_show()
visited.add(a )
expand_state(
a , 0 , a , a , a , a , a , a , )
close_list_anchor.append(a )
print("No path found to goal" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(a ):
if (j, i) in blocks:
print("#" , end=" " )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("*" , end=" " )
else:
print("-" , end=" " )
else:
print("*" , end=" " )
if (j, i) == (n - 1, n - 1):
print("<-- End position" , end=" " )
print()
print("^" )
print("Start position" )
print()
print("# is an obstacle" )
print("- is the path taken by algorithm" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 87 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 87 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _UpperCAmelCase ( _snake_case):
def lowerCamelCase__ ( self , snake_case_ ):
return 0.0
def a__ ( a : np.ndarray , a : int ):
"""simple docstring"""
_snake_case : Any = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_snake_case : Union[str, Any] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a__ ( a : FilterType , a : int ):
"""simple docstring"""
_snake_case : List[Any] = 512
_snake_case : Dict = [1] + [0] * (size - 1)
_snake_case : Union[str, Any] = [filter_type.process(a ) for item in inputs]
_snake_case : List[str] = [0] * (samplerate - size) # zero-padding
outputs += filler
_snake_case : List[Any] = np.abs(np.fft.fft(a ) )
_snake_case : List[Any] = 20 * np.logaa(a )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_snake_case : Tuple = get_bounds(a , a )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(a )
plt.show()
def a__ ( a : FilterType , a : int ):
"""simple docstring"""
_snake_case : str = 512
_snake_case : Optional[Any] = [1] + [0] * (size - 1)
_snake_case : Optional[Any] = [filter_type.process(a ) for item in inputs]
_snake_case : Union[str, Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_snake_case : Dict = np.angle(np.fft.fft(a ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(a , -2 * pi ) )
plt.show()
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_a : Any = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def a__ ( a : np.ndarray , a : float , a : int = 16_000 ):
"""simple docstring"""
_snake_case : List[str] = int(round(sample_rate * max_length ) )
if len(a ) <= sample_length:
return wav
_snake_case : str = randint(0 , len(a ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _UpperCAmelCase :
__lowercase : Optional[str] = field(default=_snake_case , metadata={"""help""": """Name of a dataset from the datasets package"""})
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """A file containing the training audio paths and labels."""})
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """A file containing the validation audio paths and labels."""})
__lowercase : str = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
__lowercase : str = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
__lowercase : str = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
__lowercase : str = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
__lowercase : Optional[int] = field(
default=_snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__lowercase : Optional[int] = field(
default=_snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
__lowercase : float = field(
default=2_0 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _UpperCAmelCase :
__lowercase : str = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
__lowercase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__lowercase : Optional[str] = field(
default=_snake_case , metadata={"""help""": """Name or path of preprocessor config."""})
__lowercase : bool = field(
default=_snake_case , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
__lowercase : bool = field(
default=_snake_case , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
__lowercase : bool = field(
default=_snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
__lowercase : Optional[bool] = field(
default=_snake_case , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
__lowercase : bool = field(
default=_snake_case , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowerCamelCase__ ( self ):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`." , snake_case_ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`." )
def a__ ( ):
"""simple docstring"""
_snake_case : Optional[Any] = 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.
_snake_case , _snake_case , _snake_case : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_snake_case , _snake_case , _snake_case : Optional[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification" , 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()
_snake_case : Dict = training_args.get_process_log_level()
logger.setLevel(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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_snake_case : List[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_snake_case : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"Use --overwrite_output_dir to train from scratch." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Initialize our dataset and prepare it for the audio classification task.
_snake_case : List[str] = DatasetDict()
_snake_case : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_snake_case : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
"Make sure to set `--label_column_name` to the correct text column - one of "
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_snake_case : Optional[Any] = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_snake_case : Optional[int] = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_snake_case : int = feature_extractor.model_input_names[0]
def train_transforms(a : int ):
_snake_case : Any = []
for audio in batch[data_args.audio_column_name]:
_snake_case : Optional[int] = random_subsample(
audio["array"] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(a )
_snake_case : Dict = feature_extractor(a , sampling_rate=feature_extractor.sampling_rate )
_snake_case : Dict = {model_input_name: inputs.get(a )}
_snake_case : Dict = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(a : Optional[int] ):
_snake_case : List[Any] = [audio["array"] for audio in batch[data_args.audio_column_name]]
_snake_case : Dict = feature_extractor(a , sampling_rate=feature_extractor.sampling_rate )
_snake_case : int = {model_input_name: inputs.get(a )}
_snake_case : Any = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_snake_case : Optional[int] = raw_datasets["train"].features[data_args.label_column_name].names
_snake_case , _snake_case : int = {}, {}
for i, label in enumerate(a ):
_snake_case : int = str(a )
_snake_case : int = label
# Load the accuracy metric from the datasets package
_snake_case : int = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(a : List[str] ):
_snake_case : List[str] = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=a , references=eval_pred.label_ids )
_snake_case : List[str] = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(a ) , labelaid=a , idalabel=a , finetuning_task="audio-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_snake_case : List[str] = AutoModelForAudioClassification.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 , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_snake_case : List[Any] = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(a , output_all_columns=a )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_snake_case : Union[str, Any] = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(a , output_all_columns=a )
# Initialize our trainer
_snake_case : Tuple = Trainer(
model=a , args=a , train_dataset=raw_datasets["train"] if training_args.do_train else None , eval_dataset=raw_datasets["eval"] if training_args.do_eval else None , compute_metrics=a , tokenizer=a , )
# Training
if training_args.do_train:
_snake_case : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
_snake_case : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_snake_case : List[str] = last_checkpoint
_snake_case : Union[str, Any] = trainer.train(resume_from_checkpoint=a )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_snake_case : Optional[int] = trainer.evaluate()
trainer.log_metrics("eval" , a )
trainer.save_metrics("eval" , a )
# Write model card and (optionally) push to hub
_snake_case : Any = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**a )
else:
trainer.create_model_card(**a )
if __name__ == "__main__":
main()
| 87 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_a : int = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _UpperCAmelCase ( _snake_case):
@staticmethod
def lowerCamelCase__ ( snake_case_ ):
_snake_case : Dict = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=snake_case_ , required=snake_case_ , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=snake_case_ , required=snake_case_ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=snake_case_ , required=snake_case_ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=snake_case_ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=snake_case_ , default=snake_case_ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , *snake_case_ , ):
_snake_case : str = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
_snake_case : Optional[int] = model_type
_snake_case : Any = tf_checkpoint
_snake_case : Optional[int] = pytorch_dump_output
_snake_case : Tuple = config
_snake_case : Tuple = finetuning_task_name
def lowerCamelCase__ ( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case : int = self._tf_checkpoint
_snake_case : Optional[Any] = ""
else:
_snake_case : Optional[int] = self._tf_checkpoint
_snake_case : List[str] = ""
convert_transfo_xl_checkpoint_to_pytorch(
snake_case_ , self._config , self._pytorch_dump_output , snake_case_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 87 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Tuple = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( _snake_case):
__lowercase : List[str] = """gptj"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=5_04_00 , snake_case_=20_48 , snake_case_=40_96 , snake_case_=28 , snake_case_=16 , snake_case_=64 , snake_case_=None , snake_case_="gelu_new" , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_=True , snake_case_=5_02_56 , snake_case_=5_02_56 , snake_case_=False , **snake_case_ , ):
_snake_case : int = vocab_size
_snake_case : Union[str, Any] = n_positions
_snake_case : int = n_embd
_snake_case : int = n_layer
_snake_case : str = n_head
_snake_case : Tuple = n_inner
_snake_case : Union[str, Any] = rotary_dim
_snake_case : List[Any] = activation_function
_snake_case : Dict = resid_pdrop
_snake_case : Optional[Any] = embd_pdrop
_snake_case : int = attn_pdrop
_snake_case : Any = layer_norm_epsilon
_snake_case : Optional[int] = initializer_range
_snake_case : int = use_cache
_snake_case : Any = bos_token_id
_snake_case : List[Any] = eos_token_id
super().__init__(
bos_token_id=snake_case_ , eos_token_id=snake_case_ , tie_word_embeddings=snake_case_ , **snake_case_ )
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ = "default" , snake_case_ = None , snake_case_ = False , ):
super().__init__(snake_case_ , task=snake_case_ , patching_specs=snake_case_ , use_past=snake_case_ )
if not getattr(self._config , "pad_token_id" , snake_case_ ):
# TODO: how to do that better?
_snake_case : List[Any] = 0
@property
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction="inputs" )
_snake_case : Union[str, Any] = {0: "batch", 1: "past_sequence + sequence"}
else:
_snake_case : Optional[Any] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowerCamelCase__ ( self ):
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
return self._config.n_head
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
_snake_case : List[Any] = super(snake_case_ , self ).generate_dummy_inputs(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
# We need to order the input in the way they appears in the forward()
_snake_case : Optional[Any] = 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
_snake_case , _snake_case : List[str] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
_snake_case : Tuple = seqlen + 2
_snake_case : Tuple = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_snake_case : str = [
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(self.num_layers )
]
_snake_case : List[Any] = common_inputs["attention_mask"]
if self.use_past:
_snake_case : int = ordered_inputs["attention_mask"].dtype
_snake_case : Optional[Any] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
return 13
| 87 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a__ ( a : List[str] , a : Any ):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_snake_case : Any = flax_key_tuple[:-1] + ("weight",)
_snake_case : str = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
_snake_case : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_snake_case : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_snake_case : Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( a : List[Any] , a : Union[str, Any] , a : List[str] ):
"""simple docstring"""
if "metadata" in layer:
_snake_case : Optional[int] = layer.split("metadata" )
_snake_case : Optional[int] = "".join(split_layer[0] )[:-1]
_snake_case : int = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
_snake_case : Any = layer.split("kvstore" )
_snake_case : str = "".join(split_layer[0] )[:-1]
_snake_case : Any = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
_snake_case : List[Any] = layer.split("/" )
_snake_case : Tuple = "/".join(split_layer[:-1] )
_snake_case : int = (split_layer[-1],)
if "kvstore/path" in layer:
_snake_case : Optional[Any] = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
_snake_case : Tuple = "file"
else:
_snake_case : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( a : List[Any] , a : List[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = rename_keys(a )
_snake_case : int = {}
for k, v in current_block.items():
_snake_case : Optional[int] = v
_snake_case : Optional[int] = new_current_block
torch.save(a , a )
def a__ ( a : Dict , a : Tuple , a : List[str] , a : int , a : str = WEIGHTS_NAME ):
"""simple docstring"""
_snake_case : Any = convert_file_size_to_int(a )
_snake_case : Tuple = []
_snake_case : Optional[int] = {}
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
_snake_case : Any = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
_snake_case : Optional[Any] = flatten_dict(a , sep="/" )
_snake_case : Optional[Any] = {}
for layer in checkpoint_info.keys():
_snake_case , _snake_case , _snake_case : int = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
_snake_case : Dict = content
else:
_snake_case : Tuple = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_snake_case : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_snake_case : Dict = torch.tensor(a )
_snake_case : Dict = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_snake_case , _snake_case : Optional[int] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
_snake_case : Optional[Any] = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_snake_case : Any = os.path.join(
a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
_snake_case : List[Any] = {}
_snake_case : str = 0
_snake_case : List[str] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_snake_case : int = os.path.join(a , weights_name.replace(".bin" , f'-{len(a )+1:05d}-of-???.bin' ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_snake_case : str = {}
_snake_case : Any = {}
for idx, shard in enumerate(a ):
_snake_case : Optional[int] = weights_name.replace(
".bin" , f'-{idx+1:05d}-of-{len(a ):05d}.bin' ) # len(sharded_state_dicts):05d}
_snake_case : Dict = os.path.join(a , weights_name.replace(".bin" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(a , os.path.join(a , a ) )
_snake_case : Dict = shard
for key in shard:
_snake_case : int = shard_file
# Add the metadata
_snake_case : List[Any] = {"total_size": total_size}
_snake_case : Any = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
_snake_case : Union[str, Any] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
_a : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_snake_case : List[str] = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
_snake_case : str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
_snake_case : List[Any] = TaTokenizer.from_pretrained("t5-small" )
_snake_case : Optional[Any] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
_snake_case : Dict = tokenizer(a , return_tensors="pt" ).input_ids
_snake_case : List[Any] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 87 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Union[str, Any] = logging.get_logger(__name__)
_a : Optional[Any] = {
"""kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """align_text_model"""
def __init__( self , snake_case_=3_05_22 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_=30_72 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=2 , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0 , snake_case_="absolute" , snake_case_=True , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Optional[int] = vocab_size
_snake_case : List[str] = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : Dict = num_attention_heads
_snake_case : List[Any] = hidden_act
_snake_case : Optional[int] = intermediate_size
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Tuple = attention_probs_dropout_prob
_snake_case : Dict = max_position_embeddings
_snake_case : Optional[Any] = type_vocab_size
_snake_case : Union[str, Any] = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : List[str] = position_embedding_type
_snake_case : int = use_cache
_snake_case : Union[str, Any] = pad_token_id
@classmethod
def lowerCamelCase__ ( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
_snake_case , _snake_case : Union[str, Any] = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
_snake_case : Dict = 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 _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """align_vision_model"""
def __init__( self , snake_case_ = 3 , snake_case_ = 6_00 , snake_case_ = 2.0 , snake_case_ = 3.1 , snake_case_ = 8 , snake_case_ = [3, 3, 5, 3, 5, 5, 3] , snake_case_ = [32, 16, 24, 40, 80, 1_12, 1_92] , snake_case_ = [16, 24, 40, 80, 1_12, 1_92, 3_20] , snake_case_ = [] , snake_case_ = [1, 2, 2, 2, 1, 2, 1] , snake_case_ = [1, 2, 2, 3, 3, 4, 1] , snake_case_ = [1, 6, 6, 6, 6, 6, 6] , snake_case_ = 0.25 , snake_case_ = "swish" , snake_case_ = 25_60 , snake_case_ = "mean" , snake_case_ = 0.02 , snake_case_ = 0.001 , snake_case_ = 0.99 , snake_case_ = 0.2 , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Optional[int] = num_channels
_snake_case : Any = image_size
_snake_case : List[Any] = width_coefficient
_snake_case : Tuple = depth_coefficient
_snake_case : int = depth_divisor
_snake_case : Any = kernel_sizes
_snake_case : Dict = in_channels
_snake_case : Union[str, Any] = out_channels
_snake_case : Optional[int] = depthwise_padding
_snake_case : Optional[Any] = strides
_snake_case : Union[str, Any] = num_block_repeats
_snake_case : Tuple = expand_ratios
_snake_case : Tuple = squeeze_expansion_ratio
_snake_case : Union[str, Any] = hidden_act
_snake_case : int = hidden_dim
_snake_case : Optional[Any] = pooling_type
_snake_case : Union[str, Any] = initializer_range
_snake_case : List[str] = batch_norm_eps
_snake_case : Optional[int] = batch_norm_momentum
_snake_case : str = drop_connect_rate
_snake_case : str = sum(snake_case_ ) * 4
@classmethod
def lowerCamelCase__ ( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
_snake_case , _snake_case : List[Any] = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
_snake_case : List[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(snake_case_ , **snake_case_ )
class _UpperCAmelCase ( _snake_case):
__lowercase : Tuple = """align"""
__lowercase : Dict = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=6_40 , snake_case_=1.0 , snake_case_=0.02 , **snake_case_ , ):
super().__init__(**snake_case_ )
if text_config is None:
_snake_case : Union[str, Any] = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
_snake_case : int = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
_snake_case : str = AlignTextConfig(**snake_case_ )
_snake_case : Dict = AlignVisionConfig(**snake_case_ )
_snake_case : int = projection_dim
_snake_case : int = temperature_init_value
_snake_case : Dict = initializer_range
@classmethod
def lowerCamelCase__ ( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = copy.deepcopy(self.__dict__ )
_snake_case : List[str] = self.text_config.to_dict()
_snake_case : Dict = self.vision_config.to_dict()
_snake_case : Dict = self.__class__.model_type
return output
| 87 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowercase : Optional[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_=False ):
_snake_case : Union[str, Any] = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_snake_case : Optional[Any] = parent
_snake_case : List[Any] = batch_size
_snake_case : Optional[int] = seq_length
_snake_case : Dict = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : List[Any] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Dict = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Optional[int] = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : Optional[int] = initializer_range
_snake_case : List[Any] = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : Optional[int] = scope
_snake_case : Any = embedding_size
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Optional[Any] = None
if self.use_input_mask:
_snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : List[str] = None
if self.use_token_type_ids:
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Dict = None
_snake_case : Tuple = None
_snake_case : str = None
if self.use_labels:
_snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Dict = TFMobileBertModel(config=snake_case_ )
_snake_case : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
_snake_case : Union[str, Any] = [input_ids, input_mask]
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Dict = model(snake_case_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : List[Any] = TFMobileBertForMaskedLM(config=snake_case_ )
_snake_case : Tuple = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[str] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=snake_case_ )
_snake_case : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Tuple = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = TFMobileBertForPreTraining(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : str = self.num_labels
_snake_case : str = TFMobileBertForSequenceClassification(config=snake_case_ )
_snake_case : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Optional[int] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Any = self.num_choices
_snake_case : Tuple = TFMobileBertForMultipleChoice(config=snake_case_ )
_snake_case : List[Any] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : List[str] = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : Tuple = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_snake_case : int = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_snake_case : Optional[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = self.num_labels
_snake_case : Optional[int] = TFMobileBertForTokenClassification(config=snake_case_ )
_snake_case : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = TFMobileBertForQuestionAnswering(config=snake_case_ )
_snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_snake_case : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) : Tuple = config_and_inputs
_snake_case : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : int = TFMobileBertModelTest.TFMobileBertModelTester(self )
_snake_case : Optional[Any] = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_snake_case : str = TFMobileBertModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_tf
class _UpperCAmelCase ( unittest.TestCase):
@slow
def lowerCamelCase__ ( self ):
_snake_case : Any = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_snake_case : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_snake_case : Union[str, Any] = model(snake_case_ )[0]
_snake_case : int = [1, 6, 3_05_22]
self.assertEqual(output.shape , snake_case_ )
_snake_case : Optional[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 87 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=99 , snake_case_=13 , snake_case_=7 , snake_case_=9 , snake_case_=True , snake_case_=True , snake_case_=False , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_=8 , snake_case_=0.1 , snake_case_=0.002 , snake_case_=1 , snake_case_=0 , snake_case_=0 , snake_case_=None , snake_case_=None , ):
_snake_case : Union[str, Any] = parent
_snake_case : int = batch_size
_snake_case : Any = encoder_seq_length
_snake_case : str = decoder_seq_length
# For common tests
_snake_case : int = self.decoder_seq_length
_snake_case : Optional[Any] = is_training
_snake_case : List[str] = use_attention_mask
_snake_case : List[Any] = use_labels
_snake_case : Optional[int] = vocab_size
_snake_case : List[Any] = hidden_size
_snake_case : str = num_hidden_layers
_snake_case : Any = num_attention_heads
_snake_case : int = d_ff
_snake_case : Any = relative_attention_num_buckets
_snake_case : int = dropout_rate
_snake_case : Union[str, Any] = initializer_factor
_snake_case : List[Any] = eos_token_id
_snake_case : Optional[int] = pad_token_id
_snake_case : Optional[Any] = decoder_start_token_id
_snake_case : Tuple = None
_snake_case : Dict = decoder_layers
def lowerCamelCase__ ( self ):
return TaConfig.from_pretrained("google/umt5-base" )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , ):
if attention_mask is None:
_snake_case : Dict = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_snake_case : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_snake_case : int = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=snake_case_ )
if decoder_head_mask is None:
_snake_case : Dict = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=snake_case_ )
if cross_attn_head_mask is None:
_snake_case : List[Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=snake_case_ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def lowerCamelCase__ ( self ):
_snake_case : List[str] = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_snake_case : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_snake_case : Optional[int] = input_ids.clamp(self.pad_token_id + 1 )
_snake_case : Tuple = decoder_input_ids.clamp(self.pad_token_id + 1 )
_snake_case : Optional[Any] = self.get_config()
_snake_case : Dict = config.num_attention_heads
_snake_case : Optional[Any] = self.prepare_inputs_dict(snake_case_ , snake_case_ , snake_case_ )
return config, input_dict
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase__ ( self ):
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def lowerCamelCase__ ( self ):
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
_snake_case : Optional[int] = UMTaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Tuple = model(
input_ids=snake_case_ , decoder_input_ids=snake_case_ , attention_mask=snake_case_ , decoder_attention_mask=snake_case_ , )
_snake_case : Dict = model(input_ids=snake_case_ , decoder_input_ids=snake_case_ )
_snake_case : int = result.last_hidden_state
_snake_case : Any = result.past_key_values
_snake_case : Tuple = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(snake_case_ ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
_snake_case : Union[str, Any] = UMTaModel(config=snake_case_ ).get_decoder().to(snake_case_ ).eval()
# first forward pass
_snake_case : Optional[int] = model(snake_case_ , use_cache=snake_case_ )
_snake_case : Optional[Any] = model(snake_case_ )
_snake_case : Union[str, Any] = model(snake_case_ , use_cache=snake_case_ )
self.parent.assertTrue(len(snake_case_ ) == len(snake_case_ ) )
self.parent.assertTrue(len(snake_case_ ) == len(snake_case_ ) + 1 )
_snake_case , _snake_case : Any = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_snake_case : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_snake_case : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
_snake_case : str = model(snake_case_ )["last_hidden_state"]
_snake_case : Any = model(snake_case_ , past_key_values=snake_case_ )["last_hidden_state"]
# select random slice
_snake_case : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_snake_case : int = output_from_no_past[:, -1, random_slice_idx].detach()
_snake_case : int = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case_ , snake_case_ , atol=1E-3 ) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , ):
_snake_case : Optional[Any] = UMTaModel(config=snake_case_ ).to(snake_case_ ).half().eval()
_snake_case : Dict = model(**snake_case_ )["last_hidden_state"]
self.parent.assertFalse(torch.isnan(snake_case_ ).any().item() )
@require_torch
class _UpperCAmelCase ( _snake_case , _snake_case , _snake_case , unittest.TestCase):
__lowercase : List[str] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__lowercase : Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__lowercase : str = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__lowercase : List[Any] = True
__lowercase : int = False
__lowercase : Tuple = False
__lowercase : Optional[int] = True
__lowercase : Union[str, Any] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__lowercase : Union[str, Any] = [0.8, 0.9]
def lowerCamelCase__ ( self ):
_snake_case : int = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
_snake_case : Tuple = UMTaModel(config_and_inputs[0] ).to(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
snake_case_ , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=snake_case_ , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
_snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
_snake_case : Union[str, Any] = config_and_inputs[0]
_snake_case : int = UMTaForConditionalGeneration(snake_case_ ).eval()
model.to(snake_case_ )
_snake_case : Union[str, Any] = {
"head_mask": torch.zeros(config.num_layers , config.num_heads , device=snake_case_ ),
"decoder_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case_ ),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case_ ),
}
for attn_name, (name, mask) in zip(snake_case_ , head_masking.items() ):
_snake_case : int = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_snake_case : List[Any] = torch.ones(
config.num_decoder_layers , config.num_heads , device=snake_case_ )
_snake_case : List[str] = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=snake_case_ , return_dict_in_generate=snake_case_ , **snake_case_ , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_snake_case : int = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def lowerCamelCase__ ( self ):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( unittest.TestCase):
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=snake_case_ ).to(snake_case_ )
_snake_case : Union[str, Any] = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=snake_case_ , legacy=snake_case_ )
_snake_case : List[Any] = [
"Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
"No se como puedo <extra_id_0>.",
"This is the reason why we <extra_id_0> them.",
"The <extra_id_0> walks in <extra_id_1>, seats",
"A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
]
_snake_case : Optional[Any] = tokenizer(snake_case_ , return_tensors="pt" , padding=snake_case_ ).input_ids
# fmt: off
_snake_case : List[str] = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(snake_case_ , snake_case_ )
_snake_case : Optional[int] = model.generate(input_ids.to(snake_case_ ) )
_snake_case : str = [
"<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
"<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
]
_snake_case : Dict = tokenizer.batch_decode(snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
_a : int = 5
_a : Dict = 10
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( _snake_case , unittest.TestCase):
__lowercase : str = SpeechaTextTokenizer
__lowercase : int = False
__lowercase : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_snake_case : Optional[Any] = sp.SentencePieceProcessor()
spm_model.Load(snake_case_ )
_snake_case : Any = ["<s>", "<pad>", "</s>", "<unk>"]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(snake_case_ ) )]
_snake_case : Union[str, Any] = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
_snake_case : Dict = Path(self.tmpdirname )
save_json(snake_case_ , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(snake_case_ , save_dir / VOCAB_FILES_NAMES["spm_file"] )
_snake_case : List[Any] = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = "<pad>"
_snake_case : Optional[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(snake_case_ ) , 10_01 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_01 )
def lowerCamelCase__ ( self ):
_snake_case : List[str] = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
_snake_case : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(snake_case_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case_ ) , [2_89, 50, 14, 1_74, 3_86] , )
_snake_case : Optional[int] = 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 : Optional[Any] = tokenizer.convert_tokens_to_ids(snake_case_ )
self.assertListEqual(snake_case_ , [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
_snake_case : List[Any] = 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>", "."] , )
@slow
def lowerCamelCase__ ( self ):
# fmt: off
_snake_case : Tuple = {"input_ids": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="facebook/s2t-small-mustc-en-de-st" , revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad" , )
@require_sentencepiece
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : Tuple = """valhalla/s2t_mustc_multilinguial_medium"""
__lowercase : Union[str, Any] = """C'est trop cool"""
__lowercase : Any = """Esto es genial"""
@classmethod
def lowerCamelCase__ ( cls ):
_snake_case : SpeechaTextTokenizer = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowerCamelCase__ ( self ):
self.assertEqual(self.tokenizer.lang_code_to_id["pt"] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["ru"] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["it"] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["de"] , 11 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.tokenizer.vocab_size , 1_00_00 )
def lowerCamelCase__ ( self ):
self.assertIn(snake_case_ , self.tokenizer.all_special_ids )
_snake_case : Optional[int] = [ES_CODE, 4, 16_01, 47, 76_47, 2]
_snake_case : Tuple = self.tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_ )
_snake_case : 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 ):
_snake_case : str = "fr"
_snake_case : int = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , snake_case_ )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = "fr"
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
_snake_case : List[Any] = "es"
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 87 |
"""simple docstring"""
def a__ ( a : list , a : int , a : int = 0 , a : int = 0 ):
"""simple docstring"""
_snake_case : Optional[int] = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | 1 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ ):
_snake_case , _snake_case : Dict = text, pattern
_snake_case , _snake_case : int = len(snake_case_ ), len(snake_case_ )
def lowerCamelCase__ ( self , snake_case_ ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase__ ( self , snake_case_ ):
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 lowerCamelCase__ ( self ):
# searches pattern in text and returns index positions
_snake_case : List[str] = []
for i in range(self.textLen - self.patLen + 1 ):
_snake_case : Union[str, Any] = self.mismatch_in_text(snake_case_ )
if mismatch_index == -1:
positions.append(snake_case_ )
else:
_snake_case : Tuple = self.match_in_pattern(self.text[mismatch_index] )
_snake_case : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_a : List[Any] = """ABAABA"""
_a : str = """AB"""
_a : List[Any] = BoyerMooreSearch(text, pattern)
_a : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 87 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unittest.TestCase):
@property
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_snake_case : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.dummy_uncond_unet
_snake_case : Optional[int] = KarrasVeScheduler()
_snake_case : Optional[int] = KarrasVePipeline(unet=snake_case_ , scheduler=snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Dict = torch.manual_seed(0 )
_snake_case : Dict = pipe(num_inference_steps=2 , generator=snake_case_ , output_type="numpy" ).images
_snake_case : Union[str, Any] = torch.manual_seed(0 )
_snake_case : List[str] = pipe(num_inference_steps=2 , generator=snake_case_ , output_type="numpy" , return_dict=snake_case_ )[0]
_snake_case : int = image[0, -3:, -3:, -1]
_snake_case : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_snake_case : Union[str, Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = "google/ncsnpp-celebahq-256"
_snake_case : Tuple = UNetaDModel.from_pretrained(snake_case_ )
_snake_case : Tuple = KarrasVeScheduler()
_snake_case : Optional[int] = KarrasVePipeline(unet=snake_case_ , scheduler=snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Union[str, Any] = torch.manual_seed(0 )
_snake_case : List[str] = pipe(num_inference_steps=20 , generator=snake_case_ , output_type="numpy" ).images
_snake_case : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
_snake_case : Dict = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 87 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_a : Dict = input("""Enter image url: """).strip()
print(f'Downloading image from {url} ...')
_a : str = 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 : str = soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
_a : Dict = requests.get(image_url).content
_a : 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}.')
| 87 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_a : Tuple = logging.get_logger(__name__)
def a__ ( a : np.ndarray , a : Union[int, Iterable[int]] , a : bool , a : int ):
"""simple docstring"""
def constraint_to_multiple_of(a : Optional[int] , a : List[Any] , a : Tuple=0 , a : Dict=None ):
_snake_case : List[Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_snake_case : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_snake_case : Union[str, Any] = math.ceil(val / multiple ) * multiple
return x
_snake_case : List[str] = (output_size, output_size) if isinstance(a , a ) else output_size
_snake_case , _snake_case : Optional[int] = get_image_size(a )
_snake_case , _snake_case : Tuple = output_size
# determine new height and width
_snake_case : int = output_height / input_height
_snake_case : Union[str, Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_snake_case : Union[str, Any] = scale_width
else:
# fit height
_snake_case : List[Any] = scale_height
_snake_case : Tuple = constraint_to_multiple_of(scale_height * input_height , multiple=a )
_snake_case : Any = constraint_to_multiple_of(scale_width * input_width , multiple=a )
return (new_height, new_width)
class _UpperCAmelCase ( _snake_case):
__lowercase : Any = ["""pixel_values"""]
def __init__( self , snake_case_ = True , snake_case_ = None , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = False , snake_case_ = 1 , snake_case_ = True , snake_case_ = 1 / 2_55 , snake_case_ = True , snake_case_ = None , snake_case_ = None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : str = size if size is not None else {"height": 3_84, "width": 3_84}
_snake_case : int = get_size_dict(snake_case_ )
_snake_case : Optional[Any] = do_resize
_snake_case : List[Any] = size
_snake_case : Optional[Any] = keep_aspect_ratio
_snake_case : Dict = ensure_multiple_of
_snake_case : Optional[Any] = resample
_snake_case : Optional[int] = do_rescale
_snake_case : List[str] = rescale_factor
_snake_case : Any = do_normalize
_snake_case : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_snake_case : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ = False , snake_case_ = 1 , snake_case_ = PILImageResampling.BICUBIC , snake_case_ = None , **snake_case_ , ):
_snake_case : Tuple = get_size_dict(snake_case_ )
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()}' )
_snake_case : List[str] = get_resize_output_image_size(
snake_case_ , output_size=(size["height"], size["width"]) , keep_aspect_ratio=snake_case_ , multiple=snake_case_ , )
return resize(snake_case_ , size=snake_case_ , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
return rescale(snake_case_ , scale=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
return normalize(snake_case_ , mean=snake_case_ , std=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = ChannelDimension.FIRST , **snake_case_ , ):
_snake_case : Tuple = do_resize if do_resize is not None else self.do_resize
_snake_case : Union[str, Any] = size if size is not None else self.size
_snake_case : str = get_size_dict(snake_case_ )
_snake_case : List[Any] = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_snake_case : List[Any] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_snake_case : str = resample if resample is not None else self.resample
_snake_case : Tuple = do_rescale if do_rescale is not None else self.do_rescale
_snake_case : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
_snake_case : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
_snake_case : int = image_mean if image_mean is not None else self.image_mean
_snake_case : Optional[Any] = image_std if image_std is not None else self.image_std
_snake_case : List[str] = make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
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." )
# All transformations expect numpy arrays.
_snake_case : int = [to_numpy_array(snake_case_ ) for image in images]
if do_resize:
_snake_case : List[Any] = [self.resize(image=snake_case_ , size=snake_case_ , resample=snake_case_ ) for image in images]
if do_rescale:
_snake_case : Dict = [self.rescale(image=snake_case_ , scale=snake_case_ ) for image in images]
if do_normalize:
_snake_case : List[Any] = [self.normalize(image=snake_case_ , mean=snake_case_ , std=snake_case_ ) for image in images]
_snake_case : Union[str, Any] = [to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
_snake_case : Dict = {"pixel_values": images}
return BatchFeature(data=snake_case_ , tensor_type=snake_case_ )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ = None ):
_snake_case : List[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(snake_case_ ):
_snake_case : Optional[int] = target_sizes.numpy()
_snake_case : Any = []
for idx in range(len(snake_case_ ) ):
_snake_case : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=snake_case_ )
_snake_case : int = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(snake_case_ )
else:
_snake_case : Tuple = logits.argmax(dim=1 )
_snake_case : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[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 : List[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 : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 87 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_a : Tuple = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig):
__lowercase : Optional[datasets.Features] = None
__lowercase : str = "utf-8"
__lowercase : Optional[str] = None
__lowercase : Optional[str] = None
__lowercase : bool = True # deprecated
__lowercase : Optional[int] = None # deprecated
__lowercase : int = 1_0 << 2_0 # 10MB
__lowercase : Optional[bool] = None
class _UpperCAmelCase ( datasets.ArrowBasedBuilder):
__lowercase : int = JsonConfig
def lowerCamelCase__ ( self ):
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
_snake_case : List[str] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
if self.config.newlines_in_values is not None:
raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self , snake_case_ ):
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
_snake_case : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case_ , (str, list, tuple) ):
_snake_case : List[Any] = data_files
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = [files]
_snake_case : str = [dl_manager.iter_files(snake_case_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_snake_case : List[str] = []
for split_name, files in data_files.items():
if isinstance(snake_case_ , snake_case_ ):
_snake_case : int = [files]
_snake_case : Optional[int] = [dl_manager.iter_files(snake_case_ ) for file in files]
splits.append(datasets.SplitGenerator(name=snake_case_ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase__ ( self , snake_case_ ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
_snake_case : Optional[int] = self.config.features.arrow_schema.field(snake_case_ ).type
_snake_case : Union[str, Any] = pa_table.append_column(snake_case_ , pa.array([None] * len(snake_case_ ) , type=snake_case_ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
_snake_case : Tuple = table_cast(snake_case_ , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase__ ( self , snake_case_ ):
for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case_ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(snake_case_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
_snake_case : Union[str, Any] = json.load(snake_case_ )
# We keep only the field we are interested in
_snake_case : List[str] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(snake_case_ , (list, tuple) ):
_snake_case : Any = set().union(*[row.keys() for row in dataset] )
_snake_case : str = {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
else:
_snake_case : List[str] = dataset
_snake_case : Tuple = pa.Table.from_pydict(snake_case_ )
yield file_idx, self._cast_table(snake_case_ )
# If the file has one json object per line
else:
with open(snake_case_ , "rb" ) as f:
_snake_case : Union[str, Any] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
_snake_case : Optional[Any] = max(self.config.chunksize // 32 , 16 << 10 )
_snake_case : Tuple = (
self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
)
while True:
_snake_case : int = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(snake_case_ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
_snake_case : Optional[int] = batch.decode(self.config.encoding , errors=snake_case_ ).encode("utf-8" )
try:
while True:
try:
_snake_case : List[Any] = paj.read_json(
io.BytesIO(snake_case_ ) , read_options=paj.ReadOptions(block_size=snake_case_ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(snake_case_ , pa.ArrowInvalid )
and "straddling" not in str(snake_case_ )
or block_size > len(snake_case_ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F'Batch of {len(snake_case_ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
snake_case_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
_snake_case : Optional[Any] = json.load(snake_case_ )
except json.JSONDecodeError:
logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(snake_case_ , snake_case_ ): # list is the only sequence type supported in JSON
try:
_snake_case : Union[str, Any] = set().union(*[row.keys() for row in dataset] )
_snake_case : Union[str, Any] = {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
_snake_case : Union[str, Any] = pa.Table.from_pydict(snake_case_ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}' )
raise ValueError(F'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(snake_case_ )
break
else:
logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}' )
raise ValueError(
F'Not able to read records in the JSON file at {file}. '
F'You should probably indicate the field of the JSON file containing your records. '
F'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(snake_case_ )
batch_idx += 1
| 87 |
"""simple docstring"""
import argparse
import json
import subprocess
def a__ ( a : Optional[Any] , a : Optional[int] ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[Any] = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_snake_case : Dict = subprocess.run(a , shell=a , stdout=subprocess.PIPE )
_snake_case : Tuple = output.stdout.decode("utf-8" )
_snake_case : List[str] = json.loads(a )
_snake_case : Any = 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:
_snake_case : Any = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def a__ ( a : Optional[int] ):
"""simple docstring"""
return values.split("," )
_a : Optional[int] = 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."""
)
_a : List[str] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 87 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_a : int = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
_a : str = [
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def a__ ( ):
"""simple docstring"""
_snake_case : List[Any] = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bootstrap_aggregation=SCREAMING_SNAKE_CASE_ , rouge_keys=["rouge2", "rougeL"] )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_snake_case : List[Any] = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bootstrap_aggregation=SCREAMING_SNAKE_CASE_ , rouge_keys=["rouge2"] )
assert (
pd.DataFrame(no_aggregation["rouge2"] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"] ).fmeasure.mean()
)
def a__ ( ):
"""simple docstring"""
_snake_case : List[str] = "rougeLsum"
_snake_case : Any = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ , rouge_keys=[k] )[k]
_snake_case : Tuple = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ , rouge_keys=[k] )[k]
assert score > score_no_sep
def a__ ( ):
"""simple docstring"""
_snake_case : List[str] = ["rouge1", "rouge2", "rougeL"]
_snake_case : Any = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ , rouge_keys=SCREAMING_SNAKE_CASE_ )
_snake_case : str = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ , rouge_keys=SCREAMING_SNAKE_CASE_ )
assert score_sep == score_no_sep
def a__ ( ):
"""simple docstring"""
_snake_case : Any = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
_snake_case : Tuple = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ ) == calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , newline_sep=SCREAMING_SNAKE_CASE_ )
def a__ ( ):
"""simple docstring"""
_snake_case : Any = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
_snake_case : int = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
_snake_case : List[Any] = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , rouge_keys=["rougeLsum"] , newline_sep=SCREAMING_SNAKE_CASE_ )["rougeLsum"]
_snake_case : Dict = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , rouge_keys=["rougeLsum"] )["rougeLsum"]
assert new_score > prev_score
def a__ ( ):
"""simple docstring"""
_snake_case : int = Path("examples/seq2seq/test_data/wmt_en_ro" )
_snake_case : Dict = calculate_rouge_path(data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_snake_case : Optional[int] = calculate_rouge_path(
data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) , bootstrap_aggregation=SCREAMING_SNAKE_CASE_ )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 700 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_snake_case : List[Any] = Vector()
def lowerCamelCase__ ( self ):
_snake_case : Any = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case_ ) , "(0,0,0,0,0,1)" )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case_ ) , 4 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2] )
_snake_case : List[str] = Vector([1, 2, 3, 4, 5] )
_snake_case : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_snake_case : Any = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Vector([1, 2, 3] )
_snake_case : Any = 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 lowerCamelCase__ ( self ):
_snake_case : str = Vector([1, 2, 3] )
_snake_case : Union[str, Any] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = Vector([1, 2, 3] )
_snake_case : List[Any] = Vector([2, -1, 4] ) # for test of dot product
_snake_case : Union[str, Any] = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowerCamelCase__ ( self ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Vector([1, 2, 3] )
_snake_case : Optional[Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case_ , snake_case_ ) ) , "(3,4,7)" )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Vector([1, 0, 0, 0, 0, 0] )
_snake_case : Optional[int] = x.copy()
self.assertEqual(str(snake_case_ ) , str(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case_ ) , "(0,1,0)" )
def lowerCamelCase__ ( self ):
_snake_case : str = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : str = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = [[-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(snake_case_ , snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self ):
_snake_case : str = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_snake_case : List[str] = 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 lowerCamelCase__ ( self ):
_snake_case : Optional[int] = 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(snake_case_ ) )
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : int = 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 lowerCamelCase__ ( self ):
_snake_case : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_snake_case : Optional[Any] = 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 lowerCamelCase__ ( 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()
| 87 | 0 |
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