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'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def lowerCamelCase ( UpperCAmelCase__ : Any ):
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self : int ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : List[Any]=None , **lowercase_ : Any ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Dict = TFVisionTextDualEncoderModel(lowercase_ )
lowercase_ : int = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : Union[str, Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=None , **lowercase_ : List[str] ):
lowercase_ : str = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : List[str] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : List[str] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str=None , **lowercase_ : Any ):
lowercase_ : Tuple = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : int = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : str = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : Dict = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Optional[int] = after_output[0].numpy()
lowercase_ : int = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Any=None , **lowercase_ : Union[str, Any] ):
lowercase_ : List[str] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Any = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : List[Any] = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : int = to_atuple(vision_model.config.image_size )
lowercase_ : int = to_atuple(vision_model.config.patch_size )
lowercase_ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : int = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Optional[int] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : str = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : str = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Tuple = self.get_pretrained_model_and_inputs()
lowercase_ : List[str] = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : str = TFVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : int = model_a(**lowercase_ )
lowercase_ : str = after_outputs[0].numpy()
lowercase_ : Dict = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Tuple = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
lowercase_ : Optional[int] = 13
lowercase_ : Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : List[str] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowercase_ : Any = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Any , lowercase_ : Union[str, Any] ):
lowercase_ : Any = TFViTModel(lowercase_ , name="""vision_model""" )
lowercase_ : Optional[Any] = TFBertModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[int] = TFViTModelTester(self )
lowercase_ : Tuple = TFBertModelTester(self )
lowercase_ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Union[str, Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ : Tuple = vision_config_and_inputs
(
lowercase_
) : Any = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
lowercase_ : str = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
lowercase_ : Optional[int] = 13
lowercase_ : List[Any] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : Tuple = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowercase_ : str = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : Union[str, Any] ):
lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : int = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : str = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowercase_ : Dict = to_atuple(vision_model.config.image_size )
lowercase_ : Tuple = to_atuple(vision_model.config.patch_size )
lowercase_ : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : List[str] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Dict = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[str] , lowercase_ : Dict ):
lowercase_ : Dict = TFDeiTModel(lowercase_ , name="""vision_model""" )
lowercase_ : Tuple = TFRobertaModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[Any] = TFDeiTModelTester(self )
lowercase_ : int = TFRobertaModelTester(self )
lowercase_ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowercase_ : int = bert_model_tester.prepare_config_and_inputs()
lowercase_ : int = vision_config_and_inputs
(
lowercase_
) : Tuple = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : List[str] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
lowercase_ : List[Any] = 13
lowercase_ : Optional[int] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Any = random_attention_mask([batch_size, 4] )
lowercase_ : Optional[int] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : List[Any] ):
lowercase_ : Dict = TFCLIPVisionModel(lowercase_ , name="""vision_model""" )
lowercase_ : Dict = TFBertModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[int] = TFCLIPVisionModelTester(self )
lowercase_ : Union[str, Any] = TFBertModelTester(self )
lowercase_ : int = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Dict = bert_model_tester.prepare_config_and_inputs()
lowercase_ : int = vision_config_and_inputs
(
lowercase_
) : Tuple = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Tuple = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=lowercase_ )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Any = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : Dict = model(**lowercase_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase_ : List[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , lowercase_ , atol=1E-3 ) )
| 359 | '''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
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 0 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Tuple , lowercase_ : Optional[int] , lowercase_ : Any=13 , lowercase_ : Optional[int]=7 , lowercase_ : List[Any]=True , lowercase_ : List[str]=True , lowercase_ : int=True , lowercase_ : List[Any]=True , lowercase_ : Any=99 , lowercase_ : List[str]=32 , lowercase_ : Union[str, Any]=5 , lowercase_ : Tuple=4 , lowercase_ : int=37 , lowercase_ : Any="gelu" , lowercase_ : int=0.1 , lowercase_ : Optional[int]=0.1 , lowercase_ : Tuple=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[int]=2 , lowercase_ : Any=0.02 , lowercase_ : str=False , lowercase_ : List[Any]=True , lowercase_ : Dict="None" , lowercase_ : Tuple=3 , lowercase_ : Any=4 , lowercase_ : Tuple=None , ):
lowercase_ : int = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[Any] = seq_length
lowercase_ : List[str] = is_training
lowercase_ : List[Any] = use_input_mask
lowercase_ : Optional[int] = use_token_type_ids
lowercase_ : Union[str, Any] = use_labels
lowercase_ : Dict = vocab_size
lowercase_ : List[str] = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Dict = num_attention_heads
lowercase_ : Optional[Any] = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : Any = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Any = max_position_embeddings
lowercase_ : Dict = type_vocab_size
lowercase_ : List[str] = type_sequence_label_size
lowercase_ : List[Any] = initializer_range
lowercase_ : Union[str, Any] = num_labels
lowercase_ : Any = num_choices
lowercase_ : Optional[int] = relative_attention
lowercase_ : Tuple = position_biased_input
lowercase_ : List[str] = pos_att_type
lowercase_ : Any = scope
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : Optional[Any] = None
if self.use_input_mask:
lowercase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
lowercase_ : List[str] = None
if self.use_token_type_ids:
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : str = None
lowercase_ : List[str] = None
lowercase_ : List[Any] = None
if self.use_labels:
lowercase_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : str = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : str ):
return DebertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_config()
lowercase_ : int = 300
return config
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Dict , lowercase_ : Tuple ):
lowercase_ : str = DebertaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : str = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )[0]
lowercase_ : Any = model(lowercase_ , token_type_ids=lowercase_ )[0]
lowercase_ : Any = model(lowercase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase_ : Any = DebertaForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : int ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : Optional[Any] = DebertaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : str ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : List[Any] = DebertaForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Union[str, Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Dict ):
lowercase_ : Any = DebertaForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[Any] = self.prepare_config_and_inputs()
(
lowercase_
) : List[Any] = config_and_inputs
lowercase_ : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Optional[int] = DebertaModelTester(self )
lowercase_ : Any = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Any = DebertaModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@unittest.skip(reason="""Model not available yet""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[int] = DebertaModel.from_pretrained("""microsoft/deberta-base""" )
lowercase_ : Union[str, Any] = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
lowercase_ : int = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase_ : int = model(lowercase_ , attention_mask=lowercase_ )[0]
# compare the actual values for a slice.
lowercase_ : str = torch.tensor(
[[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowercase_ , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 360 | '''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 0 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
UpperCamelCase__ = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
UpperCamelCase__ = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = self.task_name.lower()
class __magic_name__ ( _UpperCAmelCase ):
UpperCamelCase__ = '''train'''
UpperCamelCase__ = '''dev'''
UpperCamelCase__ = '''test'''
class __magic_name__ ( _UpperCAmelCase ):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
def __init__( self : str , lowercase_ : GlueDataTrainingArguments , lowercase_ : PreTrainedTokenizerBase , lowercase_ : Optional[int] = None , lowercase_ : Union[str, Split] = Split.train , lowercase_ : Optional[str] = None , ):
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , lowercase_ , )
lowercase_ : Union[str, Any] = args
lowercase_ : List[Any] = glue_processors[args.task_name]()
lowercase_ : Optional[Any] = glue_output_modes[args.task_name]
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase_ : List[Any] = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
lowercase_ : str = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
lowercase_ : Tuple = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase_ : List[str] = label_list[2], label_list[1]
lowercase_ : Union[str, Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase_ : List[Any] = cached_features_file + """.lock"""
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not args.overwrite_cache:
lowercase_ : Optional[Any] = time.time()
lowercase_ : Optional[int] = torch.load(lowercase_ )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
lowercase_ : Union[str, Any] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowercase_ : Tuple = self.processor.get_test_examples(args.data_dir )
else:
lowercase_ : Optional[Any] = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowercase_ : Dict = examples[:limit_length]
lowercase_ : List[Any] = glue_convert_examples_to_features(
lowercase_ , lowercase_ , max_length=args.max_seq_length , label_list=lowercase_ , output_mode=self.output_mode , )
lowercase_ : int = time.time()
torch.save(self.features , lowercase_ )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : List[Any] ):
return len(self.features )
def __getitem__( self : int , lowercase_ : Optional[Any] ):
return self.features[i]
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.label_list
| 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __magic_name__ ( _UpperCAmelCase):
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : List[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase_ , """tf_padding""" ) )
self.parent.assertTrue(hasattr(lowercase_ , """depth_multiplier""" ) )
class __magic_name__ :
def __init__( self : List[str] , lowercase_ : List[Any] , lowercase_ : int=13 , lowercase_ : List[str]=3 , lowercase_ : Tuple=32 , lowercase_ : List[Any]=0.25 , lowercase_ : int=8 , lowercase_ : Any=8 , lowercase_ : Tuple=6 , lowercase_ : Union[str, Any]=32 , lowercase_ : Tuple=True , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=True , lowercase_ : Optional[int]="relu6" , lowercase_ : Dict=1280 , lowercase_ : Any=0.1 , lowercase_ : Any=0.02 , lowercase_ : List[Any]=True , lowercase_ : List[str]=True , lowercase_ : List[str]=10 , lowercase_ : Optional[Any]=None , ):
lowercase_ : Optional[int] = parent
lowercase_ : Any = batch_size
lowercase_ : List[str] = num_channels
lowercase_ : Union[str, Any] = image_size
lowercase_ : List[Any] = depth_multiplier
lowercase_ : Dict = depth_divisible_by
lowercase_ : Optional[Any] = min_depth
lowercase_ : Dict = expand_ratio
lowercase_ : str = tf_padding
lowercase_ : List[str] = output_stride
lowercase_ : List[str] = first_layer_is_expansion
lowercase_ : List[str] = finegrained_output
lowercase_ : Optional[Any] = hidden_act
lowercase_ : Any = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
lowercase_ : List[str] = classifier_dropout_prob
lowercase_ : List[Any] = use_labels
lowercase_ : int = is_training
lowercase_ : Tuple = num_labels
lowercase_ : Any = initializer_range
lowercase_ : Dict = scope
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : int = None
lowercase_ : List[str] = None
if self.use_labels:
lowercase_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase_ : int = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = MobileNetVaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : List[Any] ):
lowercase_ : Any = self.num_labels
lowercase_ : int = MobileNetVaForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : int ):
lowercase_ : Optional[int] = self.num_labels
lowercase_ : Optional[int] = MobileNetVaForSemanticSegmentation(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : str = model(lowercase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase_ : Dict = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[int] = self.prepare_config_and_inputs()
lowercase_ : Tuple = config_and_inputs
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'''feature-extraction''': MobileNetVaModel,
'''image-classification''': MobileNetVaForImageClassification,
'''image-segmentation''': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Tuple = MobileNetVaModelTester(self )
lowercase_ : Optional[int] = MobileNetVaConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE_ ( self : int ):
pass
@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings""" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
pass
@unittest.skip(reason="""MobileNetV2 does not output attentions""" )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : List[Any] = model_class(lowercase_ )
lowercase_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Union[str, Any] = [*signature.parameters.keys()]
lowercase_ : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
def check_hidden_states_output(lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : int ):
lowercase_ : int = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase_ : Optional[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowercase_ : Optional[int] = outputs.hidden_states
lowercase_ : List[Any] = 16
self.assertEqual(len(lowercase_ ) , lowercase_ )
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : str = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : str = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : List[str] = MobileNetVaModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def lowerCamelCase ( ) -> Tuple:
lowercase_ : str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return (
MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224""" ) if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224""" ).to(lowercase_ )
lowercase_ : Optional[Any] = self.default_image_processor
lowercase_ : Optional[Any] = prepare_img()
lowercase_ : Dict = image_processor(images=lowercase_ , return_tensors="""pt""" ).to(lowercase_ )
# forward pass
with torch.no_grad():
lowercase_ : Optional[int] = model(**lowercase_ )
# verify the logits
lowercase_ : Optional[Any] = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , lowercase_ )
lowercase_ : List[str] = torch.tensor([0.24_45, -1.19_93, 0.19_05] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""" )
lowercase_ : List[str] = model.to(lowercase_ )
lowercase_ : Optional[Any] = MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""" )
lowercase_ : Optional[Any] = prepare_img()
lowercase_ : str = image_processor(images=lowercase_ , return_tensors="""pt""" ).to(lowercase_ )
# forward pass
with torch.no_grad():
lowercase_ : int = model(**lowercase_ )
lowercase_ : List[Any] = outputs.logits
# verify the logits
lowercase_ : List[Any] = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , lowercase_ )
lowercase_ : List[Any] = torch.tensor(
[
[[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
[[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
[[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
] , device=lowercase_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 ) )
| 363 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 0 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = ['''image_processor''', '''tokenizer''']
UpperCamelCase__ = '''FlavaImageProcessor'''
UpperCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : str , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , **lowercase_ : int ):
lowercase_ : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowercase_ , )
lowercase_ : Dict = kwargs.pop("""feature_extractor""" )
lowercase_ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(lowercase_ , lowercase_ )
lowercase_ : List[str] = self.image_processor
def __call__( self : Tuple , lowercase_ : Optional[ImageInput] = None , lowercase_ : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , lowercase_ : bool = True , lowercase_ : Union[bool, str, PaddingStrategy] = False , lowercase_ : Union[bool, str, TruncationStrategy] = False , lowercase_ : Optional[int] = None , lowercase_ : int = 0 , lowercase_ : Optional[int] = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[bool] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = True , lowercase_ : Optional[Union[str, TensorType]] = None , **lowercase_ : Union[str, Any] , ):
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
lowercase_ : Optional[int] = self.tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
if images is not None:
lowercase_ : int = self.image_processor(
lowercase_ , return_image_mask=lowercase_ , return_codebook_pixels=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
if text is not None and images is not None:
encoding.update(lowercase_ )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , *lowercase_ : Dict , **lowercase_ : Union[str, Any] ):
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , *lowercase_ : List[Any] , **lowercase_ : str ):
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[Any] = self.tokenizer.model_input_names
lowercase_ : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowercase_ , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowercase_ , )
return self.image_processor
| 364 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = 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.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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.
lowercase_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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).
lowercase_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> Tuple:
"""simple docstring"""
lowercase_ : Any = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = StableDiffusionLatentUpscalePipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''height''',
'''width''',
'''cross_attention_kwargs''',
'''negative_prompt_embeds''',
'''prompt_embeds''',
}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase__ = frozenset([])
UpperCamelCase__ = True
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[str] = 1
lowercase_ : Union[str, Any] = 4
lowercase_ : Optional[Any] = (16, 16)
lowercase_ : Tuple = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowercase_ )
return image
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
torch.manual_seed(0 )
lowercase_ : int = UNetaDConditionModel(
act_fn="""gelu""" , attention_head_dim=8 , norm_num_groups=lowercase_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"""KDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
) , in_channels=8 , mid_block_type=lowercase_ , only_cross_attention=lowercase_ , out_channels=5 , resnet_time_scale_shift="""scale_shift""" , time_embedding_type="""fourier""" , timestep_post_act="""gelu""" , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") , )
lowercase_ : List[str] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
lowercase_ : int = EulerDiscreteScheduler(prediction_type="""sample""" )
lowercase_ : List[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=1000 , hidden_act="""quick_gelu""" , projection_dim=512 , )
lowercase_ : int = CLIPTextModel(lowercase_ )
lowercase_ : Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase_ : List[str] = {
"""unet""": model.eval(),
"""vae""": vae.eval(),
"""scheduler""": scheduler,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : Optional[Any]=0 ):
if str(lowercase_ ).startswith("""mps""" ):
lowercase_ : Optional[int] = torch.manual_seed(lowercase_ )
else:
lowercase_ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase_ : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": self.dummy_image.cpu(),
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[Any] = """cpu"""
lowercase_ : Any = self.get_dummy_components()
lowercase_ : str = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[int] = self.get_dummy_inputs(lowercase_ )
lowercase_ : List[Any] = pipe(**lowercase_ ).images
lowercase_ : Optional[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
lowercase_ : Any = np.array(
[0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55] )
lowercase_ : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def SCREAMING_SNAKE_CASE_ ( self : str ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : str ):
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = [
"""DDIMScheduler""",
"""DDPMScheduler""",
"""PNDMScheduler""",
"""HeunDiscreteScheduler""",
"""EulerAncestralDiscreteScheduler""",
"""KDPM2DiscreteScheduler""",
"""KDPM2AncestralDiscreteScheduler""",
"""DPMSolverSDEScheduler""",
]
lowercase_ : Union[str, Any] = self.get_dummy_components()
lowercase_ : Dict = self.pipeline_class(**lowercase_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Dict = self.get_dummy_inputs(lowercase_ )
lowercase_ : Optional[int] = 2
lowercase_ : int = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
lowercase_ : Union[str, Any] = getattr(lowercase_ , scheduler_enum.name )
lowercase_ : List[str] = scheduler_cls.from_config(pipe.scheduler.config )
lowercase_ : Union[str, Any] = pipe(**lowercase_ )[0]
outputs.append(lowercase_ )
assert check_same_shape(lowercase_ )
@require_torch_gpu
@slow
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = torch.manual_seed(33 )
lowercase_ : Union[str, Any] = StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" , torch_dtype=torch.floataa )
pipe.to("""cuda""" )
lowercase_ : Any = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
lowercase_ : List[Any] = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""
lowercase_ : int = pipe(lowercase_ , generator=lowercase_ , output_type="""latent""" ).images
lowercase_ : Optional[int] = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="""np""" , ).images[0]
lowercase_ : Any = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy""" )
assert np.abs((expected_image - image).mean() ) < 5E-2
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = torch.manual_seed(33 )
lowercase_ : str = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
lowercase_ : Union[str, Any] = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""
lowercase_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png""" )
lowercase_ : Dict = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="""np""" , ).images[0]
lowercase_ : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy""" )
assert np.abs((expected_image - image).max() ) < 5E-2
| 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
),
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''xlm-roberta'''
def __init__( self : Any , lowercase_ : Dict=30522 , lowercase_ : Optional[Any]=768 , lowercase_ : Any=12 , lowercase_ : Dict=12 , lowercase_ : Optional[int]=3072 , lowercase_ : Optional[Any]="gelu" , lowercase_ : Optional[Any]=0.1 , lowercase_ : Dict=0.1 , lowercase_ : List[Any]=512 , lowercase_ : Union[str, Any]=2 , lowercase_ : Tuple=0.02 , lowercase_ : str=1E-12 , lowercase_ : Union[str, Any]=1 , lowercase_ : Union[str, Any]=0 , lowercase_ : str=2 , lowercase_ : Any="absolute" , lowercase_ : Union[str, Any]=True , lowercase_ : Union[str, Any]=None , **lowercase_ : List[Any] , ):
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
lowercase_ : int = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : str = hidden_act
lowercase_ : List[Any] = intermediate_size
lowercase_ : Tuple = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Any = max_position_embeddings
lowercase_ : Any = type_vocab_size
lowercase_ : Dict = initializer_range
lowercase_ : Any = layer_norm_eps
lowercase_ : Dict = position_embedding_type
lowercase_ : str = use_cache
lowercase_ : Dict = classifier_dropout
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
if self.task == "multiple-choice":
lowercase_ : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 366 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
lowercase_ : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> tuple[int, int]:
lowercase_ : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
lowercase_ : int = x_den * y_den * z_den
lowercase_ : int = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
top //= hcf
bottom //= hcf
return top, bottom
def lowerCamelCase ( UpperCAmelCase__ : int = 35 ) -> int:
lowercase_ : set = set()
lowercase_ : int
lowercase_ : Fraction = Fraction(0 )
lowercase_ : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
lowercase_ : Union[str, Any] = x_num * y_den + x_den * y_num
lowercase_ : int = x_den * y_den
lowercase_ : List[Any] = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Optional[Any] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=2
lowercase_ : Any = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
lowercase_ : Union[str, Any] = x_den * x_den * y_den * y_den
if is_sq(UpperCAmelCase__ ) and is_sq(UpperCAmelCase__ ):
lowercase_ : Optional[Any] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Dict = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Any = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Any = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=-1
lowercase_ : List[str] = x_num * y_num
lowercase_ : Optional[Any] = x_den * y_num + x_num * y_den
lowercase_ : Dict = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : List[str] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=2
lowercase_ : Optional[Any] = x_num * x_num * y_num * y_num
lowercase_ : Optional[Any] = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(UpperCAmelCase__ ) and is_sq(UpperCAmelCase__ ):
lowercase_ : List[str] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Dict = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Union[str, Any] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
for num, den in unique_s:
total += Fraction(UpperCAmelCase__ , UpperCAmelCase__ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"""{solution() = }""")
| 367 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase ( ) -> List[Any]:
lowercase_ : Tuple = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=UpperCAmelCase__ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=UpperCAmelCase__ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=UpperCAmelCase__ )
return parser.parse_args()
def lowerCamelCase ( ) -> Optional[int]:
lowercase_ : Any = parse_args()
# Import training_script as a module.
lowercase_ : int = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowercase_ : List[Any] = script_fpath.stem
lowercase_ : str = importlib.import_module(UpperCAmelCase__ )
# Patch sys.argv
lowercase_ : int = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 0 |
'''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = 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.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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.
lowercase_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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).
lowercase_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 369 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Optional[int]:
lowercase_ : List[Any] = []
for part_id in partition_order:
lowercase_ : Optional[int] = df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(UpperCAmelCase__ ):
expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> Union[str, Any]:
lowercase_ : List[str] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : Any = spark.range(100 ).repartition(1 )
lowercase_ : Optional[int] = Spark(UpperCAmelCase__ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> int:
lowercase_ : List[Any] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : List[str] = spark.range(10 ).repartition(2 )
lowercase_ : int = [1, 0]
lowercase_ : Any = _generate_iterable_examples(UpperCAmelCase__ , UpperCAmelCase__ ) # Reverse the partitions.
lowercase_ : str = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__ , UpperCAmelCase__ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
lowercase_ : Union[str, Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> Union[str, Any]:
lowercase_ : Union[str, Any] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : Dict = spark.range(10 ).repartition(1 )
lowercase_ : List[Any] = SparkExamplesIterable(UpperCAmelCase__ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(UpperCAmelCase__ ):
assert row_id == F'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> Tuple:
lowercase_ : Union[str, Any] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : int = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
lowercase_ : Tuple = lambda UpperCAmelCase__ : x.reverse()
lowercase_ : List[str] = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__ , [2, 1, 0] )
lowercase_ : Union[str, Any] = SparkExamplesIterable(UpperCAmelCase__ ).shuffle_data_sources(UpperCAmelCase__ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(UpperCAmelCase__ ):
lowercase_ : Any = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> List[Any]:
lowercase_ : Dict = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : int = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
lowercase_ : Union[str, Any] = SparkExamplesIterable(UpperCAmelCase__ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase_ : str = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__ , [0, 2] )
for i, (row_id, row_dict) in enumerate(UpperCAmelCase__ ):
lowercase_ : Any = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
lowercase_ : Optional[Any] = SparkExamplesIterable(UpperCAmelCase__ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase_ : Any = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__ , [1, 3] )
for i, (row_id, row_dict) in enumerate(UpperCAmelCase__ ):
lowercase_ : Tuple = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase ( ) -> Union[str, Any]:
lowercase_ : Union[str, Any] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowercase_ : Dict = spark.range(100 ).repartition(1 )
lowercase_ : str = Spark(UpperCAmelCase__ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 371 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> list[int]:
if length <= 0 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(UpperCAmelCase__ )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 350 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 0 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 351 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 0 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_lowercase : Any = "<<<<<<< This should probably be modified because it mentions: "
_lowercase : Any = "=======\n>>>>>>>\n"
_lowercase : Dict = [
"TextEncoderConfig",
"ByteTextEncoder",
"SubwordTextEncoder",
"encoder_config",
"maybe_build_from_corpus",
"manual_dir",
]
_lowercase : List[str] = [
# (pattern, replacement)
# Order is important here for some replacements
(r"tfds\.core", r"datasets"),
(r"tf\.io\.gfile\.GFile", r"open"),
(r"tf\.([\w\d]+)", r"datasets.Value('\1')"),
(r"tfds\.features\.Text\(\)", r"datasets.Value('string')"),
(r"tfds\.features\.Text\(", r"datasets.Value('string'),"),
(r"features\s*=\s*tfds.features.FeaturesDict\(", r"features=datasets.Features("),
(r"tfds\.features\.FeaturesDict\(", r"dict("),
(r"The TensorFlow Datasets Authors", r"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
(r"tfds\.", r"datasets."),
(r"dl_manager\.manual_dir", r"self.config.data_dir"),
(r"self\.builder_config", r"self.config"),
]
def lowerCamelCase ( UpperCAmelCase__ : Namespace ) -> Dict:
return ConvertCommand(args.tfds_path , args.datasets_directory )
class __magic_name__ ( _UpperCAmelCase):
@staticmethod
def SCREAMING_SNAKE_CASE_ ( lowercase_ : ArgumentParser ):
lowercase_ : List[Any] = parser.add_parser(
"""convert""" , help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.""" , )
train_parser.add_argument(
"""--tfds_path""" , type=lowercase_ , required=lowercase_ , help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.""" , )
train_parser.add_argument(
"""--datasets_directory""" , type=lowercase_ , required=lowercase_ , help="""Path to the HuggingFace Datasets folder.""" )
train_parser.set_defaults(func=lowercase_ )
def __init__( self : Tuple , lowercase_ : str , lowercase_ : str , *lowercase_ : Tuple ):
lowercase_ : Optional[int] = get_logger("""datasets-cli/converting""" )
lowercase_ : List[Any] = tfds_path
lowercase_ : Any = datasets_directory
def SCREAMING_SNAKE_CASE_ ( self : int ):
if os.path.isdir(self._tfds_path ):
lowercase_ : Dict = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
lowercase_ : List[str] = os.path.dirname(self._tfds_path )
else:
raise ValueError("""--tfds_path is neither a directory nor a file. Please check path.""" )
lowercase_ : Any = os.path.abspath(self._datasets_directory )
self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}''' )
lowercase_ : Union[str, Any] = []
lowercase_ : List[str] = []
lowercase_ : Union[str, Any] = {}
if os.path.isdir(self._tfds_path ):
lowercase_ : Dict = os.listdir(lowercase_ )
else:
lowercase_ : List[Any] = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f'''Looking at file {f_name}''' )
lowercase_ : Dict = os.path.join(lowercase_ , lowercase_ )
lowercase_ : List[Any] = os.path.join(lowercase_ , lowercase_ )
if not os.path.isfile(lowercase_ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("""Skipping file""" )
continue
with open(lowercase_ , encoding="""utf-8""" ) as f:
lowercase_ : str = f.readlines()
lowercase_ : Any = []
lowercase_ : List[Any] = False
lowercase_ : Any = False
lowercase_ : str = []
for line in lines:
lowercase_ : Dict = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
lowercase_ : int = """import datasets\n"""
elif "import tensorflow" in out_line:
# order is important here
lowercase_ : Dict = """"""
continue
elif "from absl import logging" in out_line:
lowercase_ : int = """from datasets import logging\n"""
elif "getLogger" in out_line:
lowercase_ : Dict = out_line.replace("""getLogger""" , """get_logger""" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
lowercase_ : Optional[int] = True
lowercase_ : List[Any] = list(filter(lambda lowercase_ : e in out_line , lowercase_ ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowercase_ ) + """\n""" )
out_lines.append(lowercase_ )
out_lines.append(lowercase_ )
continue
else:
for pattern, replacement in TO_CONVERT:
lowercase_ : Union[str, Any] = re.sub(lowercase_ , lowercase_ , lowercase_ )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
lowercase_ : Dict = re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)""" , lowercase_ )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
lowercase_ : Tuple = """from . import """ + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f'''Error converting {out_line.strip()}''' )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
lowercase_ : Optional[int] = True
out_lines.append(lowercase_ )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
lowercase_ : int = f_name.replace(""".py""" , """""" )
lowercase_ : int = os.path.join(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , lowercase_ )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
self._logger.info(f'''Adding directory {output_dir}''' )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(lowercase_ )
if needs_manual_update:
with_manual_update.append(lowercase_ )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as f:
f.writelines(lowercase_ )
self._logger.info(f'''Converted in {output_file}''' )
for utils_file in utils_files:
try:
lowercase_ : str = os.path.basename(lowercase_ )
lowercase_ : List[str] = imports_to_builder_map[f_name.replace(""".py""" , """""" )]
self._logger.info(f'''Moving {dest_folder} to {utils_file}''' )
shutil.copy(lowercase_ , lowercase_ )
except KeyError:
self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.''' )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.''' )
| 352 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : int = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : str ) -> YolosConfig:
lowercase_ : Any = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowercase_ : Dict = 192
lowercase_ : List[Any] = 768
lowercase_ : Dict = 12
lowercase_ : List[str] = 3
lowercase_ : List[str] = [800, 1333]
lowercase_ : Tuple = False
elif yolos_name == "yolos_s_dWr":
lowercase_ : str = 330
lowercase_ : Tuple = 14
lowercase_ : Optional[int] = 6
lowercase_ : List[Any] = 1320
elif "yolos_s" in yolos_name:
lowercase_ : Any = 384
lowercase_ : List[Any] = 1536
lowercase_ : Union[str, Any] = 12
lowercase_ : int = 6
elif "yolos_b" in yolos_name:
lowercase_ : List[Any] = [800, 1344]
lowercase_ : int = 91
lowercase_ : str = """huggingface/label-files"""
lowercase_ : List[str] = """coco-detection-id2label.json"""
lowercase_ : List[str] = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
lowercase_ : List[Any] = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
lowercase_ : Optional[Any] = idalabel
lowercase_ : List[Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( UpperCAmelCase__ : dict , UpperCAmelCase__ : YolosConfig , UpperCAmelCase__ : bool = False ) -> Any:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ : Optional[int] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
lowercase_ : str = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ : Optional[Any] = in_proj_weight[: config.hidden_size, :]
lowercase_ : Union[str, Any] = in_proj_bias[: config.hidden_size]
lowercase_ : Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ : Tuple = in_proj_weight[-config.hidden_size :, :]
lowercase_ : List[str] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
if "backbone" in name:
lowercase_ : Union[str, Any] = name.replace("""backbone""" , """vit""" )
if "cls_token" in name:
lowercase_ : int = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "det_token" in name:
lowercase_ : Optional[int] = name.replace("""det_token""" , """embeddings.detection_tokens""" )
if "mid_pos_embed" in name:
lowercase_ : List[str] = name.replace("""mid_pos_embed""" , """encoder.mid_position_embeddings""" )
if "pos_embed" in name:
lowercase_ : int = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
lowercase_ : Union[str, Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "blocks" in name:
lowercase_ : int = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
lowercase_ : Union[str, Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowercase_ : Optional[int] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase_ : Union[str, Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase_ : Any = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase_ : List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase_ : Any = name.replace("""mlp.fc2""" , """output.dense""" )
if "class_embed" in name:
lowercase_ : int = name.replace("""class_embed""" , """class_labels_classifier""" )
if "bbox_embed" in name:
lowercase_ : Optional[Any] = name.replace("""bbox_embed""" , """bbox_predictor""" )
if "vit.norm" in name:
lowercase_ : Optional[int] = name.replace("""vit.norm""" , """vit.layernorm""" )
return name
def lowerCamelCase ( UpperCAmelCase__ : dict , UpperCAmelCase__ : YolosForObjectDetection ) -> dict:
for key in orig_state_dict.copy().keys():
lowercase_ : Optional[int] = orig_state_dict.pop(UpperCAmelCase__ )
if "qkv" in key:
lowercase_ : str = key.split(""".""" )
lowercase_ : Union[str, Any] = int(key_split[2] )
lowercase_ : Union[str, Any] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowercase_ : Union[str, Any] = val[:dim, :]
lowercase_ : Optional[Any] = val[
dim : dim * 2, :
]
lowercase_ : Any = val[-dim:, :]
else:
lowercase_ : List[str] = val[:dim]
lowercase_ : Any = val[dim : dim * 2]
lowercase_ : Optional[Any] = val[-dim:]
else:
lowercase_ : Union[str, Any] = val
return orig_state_dict
def lowerCamelCase ( ) -> torch.Tensor:
lowercase_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ : List[str] = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : bool = False ) -> Optional[Any]:
lowercase_ : int = get_yolos_config(UpperCAmelCase__ )
# load original state_dict
lowercase_ : List[str] = torch.load(UpperCAmelCase__ , map_location="""cpu""" )["""model"""]
# load 🤗 model
lowercase_ : str = YolosForObjectDetection(UpperCAmelCase__ )
model.eval()
lowercase_ : Dict = convert_state_dict(UpperCAmelCase__ , UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
# Check outputs on an image, prepared by YolosImageProcessor
lowercase_ : Union[str, Any] = 800 if yolos_name != """yolos_ti""" else 512
lowercase_ : Optional[int] = YolosImageProcessor(format="""coco_detection""" , size=UpperCAmelCase__ )
lowercase_ : List[str] = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase_ : List[Any] = model(**UpperCAmelCase__ )
lowercase_ : str = outputs.logits, outputs.pred_boxes
lowercase_ : Dict = None, None
if yolos_name == "yolos_ti":
lowercase_ : Dict = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
lowercase_ : Optional[Any] = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
elif yolos_name == "yolos_s_200_pre":
lowercase_ : Any = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
lowercase_ : List[Any] = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
elif yolos_name == "yolos_s_300_pre":
lowercase_ : List[str] = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
lowercase_ : Optional[int] = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
elif yolos_name == "yolos_s_dWr":
lowercase_ : List[str] = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
lowercase_ : str = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
elif yolos_name == "yolos_base":
lowercase_ : Tuple = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
lowercase_ : str = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
else:
raise ValueError(F'''Unknown yolos_name: {yolos_name}''' )
assert torch.allclose(logits[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
lowercase_ : Any = {
"""yolos_ti""": """yolos-tiny""",
"""yolos_s_200_pre""": """yolos-small""",
"""yolos_s_300_pre""": """yolos-small-300""",
"""yolos_s_dWr""": """yolos-small-dwr""",
"""yolos_base""": """yolos-base""",
}
print("""Pushing to the hub...""" )
lowercase_ : List[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(UpperCAmelCase__ , organization="""hustvl""" )
model.push_to_hub(UpperCAmelCase__ , organization="""hustvl""" )
if __name__ == "__main__":
_lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--yolos_name",
default="yolos_s_200_pre",
type=str,
help=(
"Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
),
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowercase : Tuple = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 353 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : int = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
lowercase_ : int = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase_ : Optional[Any] = model(lowercase_ )["""last_hidden_state"""]
lowercase_ : Tuple = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , lowercase_ )
# compare the actual values for a slice.
lowercase_ : Optional[int] = tf.convert_to_tensor(
[[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 354 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
import torch
def lowerCamelCase ( ) -> List[str]:
if torch.cuda.is_available():
lowercase_ : Any = torch.cuda.device_count()
else:
lowercase_ : Any = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 355 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
_lowercase : Dict = sys.version_info >= (3, 10)
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : List[Any]=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=UpperCAmelCase__ )
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = field(default='''toto''', metadata={'''help''': '''help message'''})
@dataclass
class __magic_name__ :
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''titi'''
UpperCamelCase__ = '''toto'''
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''titi'''
UpperCamelCase__ = '''toto'''
UpperCamelCase__ = 42
@dataclass
class __magic_name__ :
UpperCamelCase__ = '''toto'''
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[Any] = BasicEnum(self.foo )
@dataclass
class __magic_name__ :
UpperCamelCase__ = '''toto'''
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''help message'''})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class __magic_name__ :
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class __magic_name__ :
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = BasicEnum(self.required_enum )
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default='''toto''', metadata={'''help''': '''help message'''})
UpperCamelCase__ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])
if is_python_no_less_than_3_10:
@dataclass
class __magic_name__ :
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''help message'''})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : argparse.ArgumentParser , lowercase_ : argparse.ArgumentParser ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
lowercase_ : str = {k: v for k, v in vars(lowercase_ ).items() if k != """container"""}
lowercase_ : int = {k: v for k, v in vars(lowercase_ ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , lowercase_ ) and yy.get("""choices""" , lowercase_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](lowercase_ ) , yy["""type"""](lowercase_ ) )
del xx["type"], yy["type"]
self.assertEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Dict = HfArgumentParser(lowercase_ )
lowercase_ : Tuple = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=lowercase_ , required=lowercase_ )
expected.add_argument("""--bar""" , type=lowercase_ , required=lowercase_ )
expected.add_argument("""--baz""" , type=lowercase_ , required=lowercase_ )
expected.add_argument("""--flag""" , type=lowercase_ , default=lowercase_ , const=lowercase_ , nargs="""?""" )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : int = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
(lowercase_ ) : Union[str, Any] = parser.parse_args_into_dataclasses(lowercase_ , look_for_args_file=lowercase_ )
self.assertFalse(example.flag )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = HfArgumentParser(lowercase_ )
lowercase_ : List[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=lowercase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=lowercase_ , help="""help message""" )
self.argparsersEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=lowercase_ , default=lowercase_ , const=lowercase_ , nargs="""?""" )
expected.add_argument("""--baz""" , type=lowercase_ , default=lowercase_ , const=lowercase_ , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=lowercase_ , dest="""baz""" )
expected.add_argument("""--opt""" , type=lowercase_ , default=lowercase_ )
lowercase_ : int = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase_ )
for dataclass_type in dataclass_types:
lowercase_ : Optional[Any] = HfArgumentParser(lowercase_ )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = parser.parse_args([] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , baz=lowercase_ , opt=lowercase_ ) )
lowercase_ : List[Any] = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , baz=lowercase_ , opt=lowercase_ ) )
lowercase_ : Optional[int] = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , baz=lowercase_ , opt=lowercase_ ) )
lowercase_ : Tuple = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , baz=lowercase_ , opt=lowercase_ ) )
lowercase_ : Any = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , baz=lowercase_ , opt=lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = HfArgumentParser(lowercase_ )
lowercase_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
lowercase_ : int = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
lowercase_ : str = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
lowercase_ : Tuple = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
lowercase_ : Union[str, Any] = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
lowercase_ : Union[str, Any] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
@dataclass
class __magic_name__ :
UpperCamelCase__ = '''toto'''
lowercase_ : Optional[Any] = HfArgumentParser(lowercase_ )
lowercase_ : Any = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : List[Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
lowercase_ : Optional[Any] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
lowercase_ : Optional[int] = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = HfArgumentParser(lowercase_ )
lowercase_ : List[str] = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=lowercase_ )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=lowercase_ )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=lowercase_ )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=lowercase_ )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : Dict = parser.parse_args([] )
self.assertEqual(
lowercase_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
lowercase_ : List[str] = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(lowercase_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=lowercase_ , type=lowercase_ )
expected.add_argument("""--bar""" , default=lowercase_ , type=lowercase_ , help="""help message""" )
expected.add_argument("""--baz""" , default=lowercase_ , type=lowercase_ )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=lowercase_ )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=lowercase_ )
lowercase_ : Dict = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase_ )
for dataclass_type in dataclass_types:
lowercase_ : Any = HfArgumentParser(lowercase_ )
self.argparsersEqual(lowercase_ , lowercase_ )
lowercase_ : Dict = parser.parse_args([] )
self.assertEqual(lowercase_ , Namespace(foo=lowercase_ , bar=lowercase_ , baz=lowercase_ , ces=[] , des=[] ) )
lowercase_ : int = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(lowercase_ , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = HfArgumentParser(lowercase_ )
lowercase_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=lowercase_ , required=lowercase_ )
expected.add_argument("""--required_str""" , type=lowercase_ , required=lowercase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=lowercase_ , )
self.argparsersEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Any = HfArgumentParser(lowercase_ )
lowercase_ : Tuple = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=lowercase_ , required=lowercase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=lowercase_ , )
expected.add_argument("""--opt""" , type=lowercase_ , default=lowercase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=lowercase_ , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=lowercase_ )
self.argparsersEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Any = HfArgumentParser(lowercase_ )
lowercase_ : List[str] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
lowercase_ : str = parser.parse_dict(lowercase_ )[0]
lowercase_ : Any = BasicExample(**lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : str = HfArgumentParser(lowercase_ )
lowercase_ : Any = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(lowercase_ , parser.parse_dict , lowercase_ , allow_extra_keys=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[int] = HfArgumentParser(lowercase_ )
lowercase_ : Optional[Any] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : List[str] = os.path.join(lowercase_ , """temp_json""" )
os.mkdir(lowercase_ )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
lowercase_ : Union[str, Any] = BasicExample(**lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = HfArgumentParser(lowercase_ )
lowercase_ : int = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Any = os.path.join(lowercase_ , """temp_yaml""" )
os.mkdir(lowercase_ )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(lowercase_ , lowercase_ )
lowercase_ : Any = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
lowercase_ : Any = BasicExample(**lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = HfArgumentParser(lowercase_ )
self.assertIsNotNone(lowercase_ )
| 356 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : int = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowercase : Any = 256047
_lowercase : List[str] = 256145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = NllbTokenizer
UpperCamelCase__ = NllbTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
UpperCamelCase__ = {}
def SCREAMING_SNAKE_CASE_ ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ : Union[str, Any] = NllbTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = NllbTokenizer(lowercase_ , keep_accents=lowercase_ )
lowercase_ : Optional[int] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowercase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowercase_ : str = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
lowercase_ : Tuple = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowercase_ : List[Any] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Union[str, Any] = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Optional[int] = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Tuple = tempfile.mkdtemp()
lowercase_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ )
lowercase_ : Dict = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
lowercase_ : List[Any] = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase_ : Dict = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
lowercase_ : List[Any] = tempfile.mkdtemp()
lowercase_ : Tuple = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase_ : Any = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
lowercase_ : int = tempfile.mkdtemp()
lowercase_ : Any = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowercase_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : Any = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : str ):
if not self.test_seqaseq:
return
lowercase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
lowercase_ : Any = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
lowercase_ : Any = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
lowercase_ : List[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowercase_ , tgt_texts=lowercase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
lowercase_ : int = tokenizer.prepare_seqaseq_batch(
lowercase_ , tgt_texts=lowercase_ , max_length=3 , return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
lowercase_ : List[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowercase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("""decoder_input_ids""" , lowercase_ )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : Dict = [AddedToken("""<special>""" , lstrip=lowercase_ )]
lowercase_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ )
lowercase_ : Tuple = tokenizer_r.encode("""Hey this is a <special> token""" )
lowercase_ : List[Any] = tokenizer_r.encode("""<special>""" , add_special_tokens=lowercase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
lowercase_ : int = self.rust_tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
lowercase_ : str = self.tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ )
lowercase_ : Tuple = tokenizer_p.encode("""Hey this is a <special> token""" )
lowercase_ : int = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(lowercase_ , lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = '''facebook/nllb-200-distilled-600M'''
UpperCamelCase__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
UpperCamelCase__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
UpperCamelCase__ = [
25_6047,
1_6297,
13_4408,
8165,
24_8066,
1_4734,
950,
1135,
10_5721,
3573,
83,
2_7352,
108,
4_9486,
2,
]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] ):
lowercase_ : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" )
lowercase_ : Any = 1
return cls
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] , 256057 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.assertIn(lowercase_ , self.tokenizer.all_special_ids )
# fmt: off
lowercase_ : List[str] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
lowercase_ : Optional[Any] = self.tokenizer.decode(lowercase_ , skip_special_tokens=lowercase_ )
lowercase_ : List[str] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.assertNotIn(self.tokenizer.eos_token , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Tuple = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , lowercase_ )
lowercase_ : List[str] = 10
lowercase_ : Dict = self.tokenizer(lowercase_ , max_length=lowercase_ , truncation=lowercase_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , lowercase_ )
self.assertEqual(len(lowercase_ ) , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [256203, 3] )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[Any] = tempfile.mkdtemp()
lowercase_ : Any = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase_ )
lowercase_ : Tuple = NllbTokenizer.from_pretrained(lowercase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase_ )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Tuple = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
lowercase_ : Tuple = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
lowercase_ : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
self.assertEqual(lowercase_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[Any] = self.tokenizer(self.src_text , padding=lowercase_ , truncation=lowercase_ , max_length=3 , return_tensors="""pt""" )
lowercase_ : List[Any] = self.tokenizer(
text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=10 , return_tensors="""pt""" )
lowercase_ : Optional[Any] = targets["""input_ids"""]
lowercase_ : int = shift_tokens_right(
lowercase_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(lowercase_ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[256047, 70, 7356, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 256057,
} , )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Any = True
lowercase_ : Optional[int] = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
lowercase_ : List[str] = False
lowercase_ : Optional[Any] = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 357 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( UpperCAmelCase__ : NDArray[floataa] , UpperCAmelCase__ : NDArray[floataa] , UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , ) -> list[float]:
lowercase_ : Optional[Any] = coefficient_matrix.shape
lowercase_ : Optional[int] = constant_matrix.shape
if rowsa != colsa:
lowercase_ : Optional[int] = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(UpperCAmelCase__ )
if colsa != 1:
lowercase_ : Any = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(UpperCAmelCase__ )
if rowsa != rowsa:
lowercase_ : Tuple = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != rowsa:
lowercase_ : Dict = (
"""Number of initial values must be equal to number of rows in coefficient """
F'''matrix but received {len(UpperCAmelCase__ )} and {rowsa}'''
)
raise ValueError(UpperCAmelCase__ )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
lowercase_ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
lowercase_ : str = table.shape
strictly_diagonally_dominant(UpperCAmelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCAmelCase__ ):
lowercase_ : List[Any] = []
for row in range(UpperCAmelCase__ ):
lowercase_ : Optional[Any] = 0
for col in range(UpperCAmelCase__ ):
if col == row:
lowercase_ : Tuple = table[row][col]
elif col == cols - 1:
lowercase_ : Optional[int] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
lowercase_ : Dict = (temp + val) / denom
new_val.append(UpperCAmelCase__ )
lowercase_ : List[str] = new_val
return [float(UpperCAmelCase__ ) for i in new_val]
def lowerCamelCase ( UpperCAmelCase__ : NDArray[floataa] ) -> bool:
lowercase_ : Union[str, Any] = table.shape
lowercase_ : Optional[int] = True
for i in range(0 , UpperCAmelCase__ ):
lowercase_ : List[str] = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
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
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################
_lowercase : List[Any] = 16
_lowercase : List[str] = 32
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int = 16 ):
lowercase_ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase_ : int = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
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():
lowercase_ : Optional[int] = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , 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
lowercase_ : Any = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase__ : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase_ : Optional[int] = 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":
lowercase_ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase_ : Union[str, Any] = 8
else:
lowercase_ : List[str] = None
return tokenizer.pad(
UpperCAmelCase__ , padding="""longest""" , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase_ : List[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : Tuple = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
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
_lowercase : Optional[int] = mocked_dataloaders # noqa: F811
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCAmelCase__ ) == "1":
lowercase_ : List[str] = 2
# Initialize accelerator
lowercase_ : Tuple = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ : List[Any] = config["""lr"""]
lowercase_ : Optional[Any] = int(config["""num_epochs"""] )
lowercase_ : Optional[int] = int(config["""seed"""] )
lowercase_ : Any = int(config["""batch_size"""] )
lowercase_ : Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=UpperCAmelCase__ )
def inner_training_loop(UpperCAmelCase__ : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ : Any = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCAmelCase__ )
# 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).
lowercase_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
lowercase_ : Union[str, Any] = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
lowercase_ : int = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate scheduler
lowercase_ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) , )
# 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.
lowercase_ : Optional[int] = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase_ : str = model(**UpperCAmelCase__ )
lowercase_ : Tuple = outputs.loss
accelerator.backward(UpperCAmelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ : Tuple = model(**UpperCAmelCase__ )
lowercase_ : int = outputs.logits.argmax(dim=-1 )
lowercase_ : List[str] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
lowercase_ : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def lowerCamelCase ( ):
lowercase_ : List[Any] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , 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.""" )
lowercase_ : int = parser.parse_args()
lowercase_ : Dict = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 359 | '''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
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 0 |
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[Any]=False ) -> str:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : Optional[Any] = len(set_a.intersection(UpperCAmelCase__ ) )
if alternative_union:
lowercase_ : Dict = len(UpperCAmelCase__ ) + len(UpperCAmelCase__ )
else:
lowercase_ : List[Any] = len(set_a.union(UpperCAmelCase__ ) )
return intersection / union
if isinstance(UpperCAmelCase__ , (list, tuple) ) and isinstance(UpperCAmelCase__ , (list, tuple) ):
lowercase_ : List[str] = [element for element in set_a if element in set_b]
if alternative_union:
lowercase_ : Optional[Any] = len(UpperCAmelCase__ ) + len(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) / union
else:
lowercase_ : str = set_a + [element for element in set_b if element not in set_a]
return len(UpperCAmelCase__ ) / len(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) / len(UpperCAmelCase__ )
return None
if __name__ == "__main__":
_lowercase : Union[str, Any] = {"a", "b", "c", "d", "e"}
_lowercase : Dict = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 360 | '''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''decision_transformer'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Optional[Any] , lowercase_ : Dict=17 , lowercase_ : Tuple=4 , lowercase_ : Optional[int]=128 , lowercase_ : Optional[Any]=4096 , lowercase_ : Tuple=True , lowercase_ : Dict=1 , lowercase_ : Tuple=1024 , lowercase_ : Tuple=3 , lowercase_ : Optional[int]=1 , lowercase_ : Optional[int]=None , lowercase_ : List[str]="relu" , lowercase_ : List[str]=0.1 , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : List[str]=1E-5 , lowercase_ : Any=0.02 , lowercase_ : List[Any]=True , lowercase_ : Optional[int]=True , lowercase_ : Dict=50256 , lowercase_ : str=50256 , lowercase_ : Tuple=False , lowercase_ : str=False , **lowercase_ : str , ):
lowercase_ : Tuple = state_dim
lowercase_ : Optional[int] = act_dim
lowercase_ : str = hidden_size
lowercase_ : Dict = max_ep_len
lowercase_ : Any = action_tanh
lowercase_ : Union[str, Any] = vocab_size
lowercase_ : List[str] = n_positions
lowercase_ : Optional[int] = n_layer
lowercase_ : Tuple = n_head
lowercase_ : Any = n_inner
lowercase_ : Optional[Any] = activation_function
lowercase_ : List[str] = resid_pdrop
lowercase_ : str = embd_pdrop
lowercase_ : Dict = attn_pdrop
lowercase_ : List[Any] = layer_norm_epsilon
lowercase_ : Dict = initializer_range
lowercase_ : Tuple = scale_attn_weights
lowercase_ : Optional[Any] = use_cache
lowercase_ : Optional[int] = scale_attn_by_inverse_layer_idx
lowercase_ : Any = reorder_and_upcast_attn
lowercase_ : Optional[Any] = bos_token_id
lowercase_ : Optional[Any] = eos_token_id
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = 1
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = None
UpperCamelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return self.__class__(**{k: copy.deepcopy(lowercase_ ) for k, v in self.__dict__.items()} )
| 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : Dict = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''trajectory_transformer'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : int , lowercase_ : Tuple=100 , lowercase_ : Optional[int]=5 , lowercase_ : Optional[int]=1 , lowercase_ : int=1 , lowercase_ : Any=249 , lowercase_ : List[str]=6 , lowercase_ : List[Any]=17 , lowercase_ : Tuple=25 , lowercase_ : Any=4 , lowercase_ : int=4 , lowercase_ : Optional[Any]=128 , lowercase_ : Tuple=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[str]=0.1 , lowercase_ : Union[str, Any]=0.00_06 , lowercase_ : List[Any]=512 , lowercase_ : Tuple=0.02 , lowercase_ : List[str]=1E-12 , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[Any]=True , lowercase_ : Tuple=1 , lowercase_ : Optional[int]=50256 , lowercase_ : Union[str, Any]=50256 , **lowercase_ : int , ):
lowercase_ : str = vocab_size
lowercase_ : Union[str, Any] = action_weight
lowercase_ : int = reward_weight
lowercase_ : List[Any] = value_weight
lowercase_ : Dict = max_position_embeddings
lowercase_ : List[str] = block_size
lowercase_ : Optional[Any] = action_dim
lowercase_ : Any = observation_dim
lowercase_ : Union[str, Any] = transition_dim
lowercase_ : int = learning_rate
lowercase_ : Tuple = n_layer
lowercase_ : Optional[int] = n_head
lowercase_ : Any = n_embd
lowercase_ : str = embd_pdrop
lowercase_ : int = attn_pdrop
lowercase_ : int = resid_pdrop
lowercase_ : Any = initializer_range
lowercase_ : List[Any] = layer_norm_eps
lowercase_ : int = kaiming_initializer_range
lowercase_ : List[str] = use_cache
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 363 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : List[str] , lowercase_ : Optional[int]=0.01 , lowercase_ : Any=1000 ):
lowercase_ : List[Any] = p_stop
lowercase_ : List[Any] = max_length
def __iter__( self : Any ):
lowercase_ : List[str] = 0
lowercase_ : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
lowercase_ : Optional[Any] = random.random() < self.p_stop
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Tuple , lowercase_ : List[str]=False , lowercase_ : List[Any]=True ):
lowercase_ : List[str] = [
BatchSamplerShard(lowercase_ , 2 , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
for i in range(2 )
]
lowercase_ : Dict = [list(lowercase_ ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowercase_ ) for shard in batch_sampler_shards] , [len(lowercase_ ) for e in expected] )
self.assertListEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# Check the shards when the dataset is a round multiple of total batch size.
lowercase_ : Optional[Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Any = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowercase_ : Union[str, Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : int = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowercase_ : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowercase_ : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : int = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# Check the shards when the dataset is a round multiple of batch size.
lowercase_ : Optional[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowercase_ : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowercase_ : str = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : str = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : str = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : int = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Tuple = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : List[str] = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[str] = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
# Check the shards when the dataset is a round multiple of total batch size.
lowercase_ : List[str] = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Tuple = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowercase_ : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowercase_ : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Dict = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : Tuple = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
# Check the shards when the dataset is a round multiple of batch size.
lowercase_ : Tuple = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowercase_ : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : Optional[Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : str = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[str] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
lowercase_ : str = [BatchSamplerShard(lowercase_ , 2 , lowercase_ , even_batches=lowercase_ ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple=False , lowercase_ : Dict=2 , lowercase_ : Optional[int]=False ):
random.seed(lowercase_ )
lowercase_ : List[str] = list(lowercase_ )
lowercase_ : Optional[Any] = [
IterableDatasetShard(
lowercase_ , batch_size=lowercase_ , drop_last=lowercase_ , num_processes=lowercase_ , process_index=lowercase_ , split_batches=lowercase_ , )
for i in range(lowercase_ )
]
lowercase_ : List[str] = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(lowercase_ )
iterable_dataset_lists.append(list(lowercase_ ) )
lowercase_ : Dict = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
lowercase_ : str = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
self.assertTrue(len(lowercase_ ) % shard_batch_size == 0 )
lowercase_ : int = []
for idx in range(0 , len(lowercase_ ) , lowercase_ ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowercase_ ) < len(lowercase_ ):
reference += reference
self.assertListEqual(lowercase_ , reference[: len(lowercase_ )] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Tuple = 42
lowercase_ : Dict = RandomIterableDataset()
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
# Edge case with a very small dataset
lowercase_ : Tuple = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[Any] = BatchSampler(range(16 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = SkipBatchSampler(lowercase_ , 2 )
self.assertListEqual(list(lowercase_ ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : int = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
lowercase_ : int = skip_first_batches(lowercase_ , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
Accelerator()
lowercase_ : Tuple = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 364 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = 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.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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.
lowercase_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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).
lowercase_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Union[str, Any]=13 , lowercase_ : List[str]=7 , lowercase_ : Any=True , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[int]=False , lowercase_ : List[str]=True , lowercase_ : int=99 , lowercase_ : Any=32 , lowercase_ : List[str]=5 , lowercase_ : List[str]=4 , lowercase_ : Optional[int]=64 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : int=0.1 , lowercase_ : Union[str, Any]=512 , lowercase_ : List[Any]=16 , lowercase_ : List[Any]=2 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : Any=3 , lowercase_ : List[Any]=4 , lowercase_ : List[str]=None , lowercase_ : str=2 , lowercase_ : Tuple=2 , lowercase_ : Optional[int]=2 , lowercase_ : Tuple=2 , lowercase_ : Optional[Any]=4 , lowercase_ : str=1 , ):
lowercase_ : Union[str, Any] = parent
lowercase_ : Optional[Any] = batch_size
lowercase_ : List[str] = seq_length
lowercase_ : Dict = is_training
lowercase_ : Union[str, Any] = use_input_mask
lowercase_ : Tuple = use_token_type_ids
lowercase_ : Optional[int] = use_labels
lowercase_ : List[Any] = vocab_size
lowercase_ : Dict = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : List[Any] = hidden_act
lowercase_ : int = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Optional[int] = max_position_embeddings
lowercase_ : str = type_vocab_size
lowercase_ : Optional[Any] = type_sequence_label_size
lowercase_ : Optional[int] = initializer_range
lowercase_ : Optional[int] = num_labels
lowercase_ : int = num_choices
lowercase_ : List[str] = scope
lowercase_ : Optional[Any] = q_groups
lowercase_ : Union[str, Any] = k_groups
lowercase_ : Any = v_groups
lowercase_ : Optional[int] = post_attention_groups
lowercase_ : Union[str, Any] = intermediate_groups
lowercase_ : Optional[Any] = output_groups
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : str = None
if self.use_input_mask:
lowercase_ : int = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : Any = None
lowercase_ : Optional[int] = None
lowercase_ : Tuple = None
if self.use_labels:
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : int = SqueezeBertModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(lowercase_ , lowercase_ )
lowercase_ : List[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Optional[int] ):
lowercase_ : Optional[Any] = SqueezeBertForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Dict , lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Dict = SqueezeBertForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Union[str, Any] = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : int ):
lowercase_ : Optional[Any] = self.num_labels
lowercase_ : Optional[Any] = SqueezeBertForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[str] = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : Union[str, Any] ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : int = SqueezeBertForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Dict = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : int ):
lowercase_ : Any = self.num_choices
lowercase_ : Any = SqueezeBertForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Optional[Any] = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[str] = self.prepare_config_and_inputs()
(lowercase_) : Union[str, Any] = config_and_inputs
lowercase_ : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCamelCase__ = (
{
'''feature-extraction''': SqueezeBertModel,
'''fill-mask''': SqueezeBertForMaskedLM,
'''question-answering''': SqueezeBertForQuestionAnswering,
'''text-classification''': SqueezeBertForSequenceClassification,
'''token-classification''': SqueezeBertForTokenClassification,
'''zero-shot''': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = SqueezeBertModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(self , config_class=lowercase_ , dim=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : int = SqueezeBertModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : List[str] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
lowercase_ : str = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
lowercase_ : List[Any] = model(lowercase_ )[0]
lowercase_ : Tuple = torch.Size((1, 3) )
self.assertEqual(output.shape , lowercase_ )
lowercase_ : Dict = torch.tensor([[0.64_01, -0.03_49, -0.60_41]] )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1E-4 ) )
| 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Tuple = {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''roberta'''
def __init__( self : Dict , lowercase_ : Any=50265 , lowercase_ : Optional[Any]=768 , lowercase_ : List[str]=12 , lowercase_ : Any=12 , lowercase_ : Optional[int]=3072 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : int=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : List[str]=2 , lowercase_ : Any=0.02 , lowercase_ : Union[str, Any]=1E-12 , lowercase_ : Optional[Any]=1 , lowercase_ : Optional[int]=0 , lowercase_ : Tuple=2 , lowercase_ : Any="absolute" , lowercase_ : int=True , lowercase_ : Union[str, Any]=None , **lowercase_ : Tuple , ):
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
lowercase_ : Optional[Any] = vocab_size
lowercase_ : Optional[Any] = hidden_size
lowercase_ : List[Any] = num_hidden_layers
lowercase_ : Any = num_attention_heads
lowercase_ : Union[str, Any] = hidden_act
lowercase_ : int = intermediate_size
lowercase_ : List[str] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : Optional[Any] = max_position_embeddings
lowercase_ : Dict = type_vocab_size
lowercase_ : str = initializer_range
lowercase_ : int = layer_norm_eps
lowercase_ : Union[str, Any] = position_embedding_type
lowercase_ : Optional[int] = use_cache
lowercase_ : Optional[int] = classifier_dropout
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
if self.task == "multiple-choice":
lowercase_ : Tuple = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 366 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 0 |
'''simple docstring'''
_lowercase : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
_lowercase : int = {value: key for key, value in encode_dict.items()}
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
lowercase_ : int = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
if set(UpperCAmelCase__ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
lowercase_ : str = """"""
for word in coded.split():
while len(UpperCAmelCase__ ) != 0:
decoded += decode_dict[word[:5]]
lowercase_ : List[Any] = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 367 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : list[tuple[float, float]] ):
lowercase_ : int = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowercase_ : Dict = len(lowercase_ ) - 1
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase_ : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , lowercase_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(lowercase_ ) , 5 ) == 1
return output_values
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase_ : List[str] = self.basis_function(lowercase_ )
lowercase_ : Tuple = 0.0
lowercase_ : Dict = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
lowercase_ : list[float] = [] # x coordinates of points to plot
lowercase_ : list[float] = [] # y coordinates of points to plot
lowercase_ : Tuple = 0.0
while t <= 1:
lowercase_ : Any = self.bezier_curve_function(lowercase_ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowercase_ : Any = [i[0] for i in self.list_of_points]
lowercase_ : List[str] = [i[1] for i in self.list_of_points]
plt.plot(
lowercase_ , lowercase_ , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(lowercase_ , lowercase_ , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 0 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 369 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 0 |
def lowerCamelCase ( UpperCAmelCase__ : int = 1000 ) -> int:
lowercase_ : List[Any] = -1
lowercase_ : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase_ : Optional[int] = (n * n - 2 * a * n) // (2 * n - 2 * a)
lowercase_ : List[Any] = n - a - b
if c * c == (a * a + b * b):
lowercase_ : int = a * b * c
if candidate >= product:
lowercase_ : Dict = candidate
return product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 371 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 350 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_lowercase : Any = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Dict ) -> List[List[ImageInput]]:
if isinstance(UpperCAmelCase__ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(UpperCAmelCase__ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(UpperCAmelCase__ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = ['''pixel_values''']
def __init__( self : Optional[Any] , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : bool = True , lowercase_ : Union[int, float] = 1 / 255 , lowercase_ : bool = True , lowercase_ : bool = True , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , **lowercase_ : List[str] , ):
super().__init__(**lowercase_ )
lowercase_ : Optional[int] = size if size is not None else {"""shortest_edge""": 256}
lowercase_ : Optional[int] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase_ : List[Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase_ : List[str] = get_size_dict(lowercase_ , param_name="""crop_size""" )
lowercase_ : str = do_resize
lowercase_ : Dict = size
lowercase_ : Union[str, Any] = do_center_crop
lowercase_ : int = crop_size
lowercase_ : int = resample
lowercase_ : int = do_rescale
lowercase_ : str = rescale_factor
lowercase_ : Tuple = offset
lowercase_ : Any = do_normalize
lowercase_ : List[str] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase_ : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : List[str] , ):
lowercase_ : Union[str, Any] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" in size:
lowercase_ : str = get_resize_output_image_size(lowercase_ , size["""shortest_edge"""] , default_to_square=lowercase_ )
elif "height" in size and "width" in size:
lowercase_ : List[Any] = (size["""height"""], size["""width"""])
else:
raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Optional[Any] , ):
lowercase_ : Optional[Any] = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(lowercase_ , size=(size["""height"""], size["""width"""]) , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : np.ndarray , lowercase_ : Union[int, float] , lowercase_ : bool = True , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Any , ):
lowercase_ : List[str] = image.astype(np.floataa )
if offset:
lowercase_ : Optional[int] = image - (scale / 2)
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : np.ndarray , lowercase_ : Union[float, List[float]] , lowercase_ : Union[float, List[float]] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : List[str] , ):
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
lowercase_ : Dict = to_numpy_array(lowercase_ )
if do_resize:
lowercase_ : Union[str, Any] = self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ )
if do_center_crop:
lowercase_ : Union[str, Any] = self.center_crop(lowercase_ , size=lowercase_ )
if do_rescale:
lowercase_ : Union[str, Any] = self.rescale(image=lowercase_ , scale=lowercase_ , offset=lowercase_ )
if do_normalize:
lowercase_ : Tuple = self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ )
lowercase_ : str = to_channel_dimension_format(lowercase_ , lowercase_ )
return image
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : ChannelDimension = ChannelDimension.FIRST , **lowercase_ : Tuple , ):
lowercase_ : int = do_resize if do_resize is not None else self.do_resize
lowercase_ : Tuple = resample if resample is not None else self.resample
lowercase_ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
lowercase_ : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase_ : Tuple = offset if offset is not None else self.offset
lowercase_ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
lowercase_ : List[str] = image_std if image_std is not None else self.image_std
lowercase_ : Dict = size if size is not None else self.size
lowercase_ : Any = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase_ : List[Any] = crop_size if crop_size is not None else self.crop_size
lowercase_ : Optional[Any] = get_size_dict(lowercase_ , param_name="""crop_size""" )
if not valid_images(lowercase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase_ : Union[str, Any] = make_batched(lowercase_ )
lowercase_ : Any = [
[
self._preprocess_image(
image=lowercase_ , do_resize=lowercase_ , size=lowercase_ , resample=lowercase_ , do_center_crop=lowercase_ , crop_size=lowercase_ , do_rescale=lowercase_ , rescale_factor=lowercase_ , offset=lowercase_ , do_normalize=lowercase_ , image_mean=lowercase_ , image_std=lowercase_ , data_format=lowercase_ , )
for img in video
]
for video in videos
]
lowercase_ : List[str] = {"""pixel_values""": videos}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 351 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 0 |
'''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
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 352 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> str:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0""" )
if not iterations >= 1:
raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" )
lowercase_ : Dict = """"""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(UpperCAmelCase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : Any , lowercase_ : List[str]=13 , lowercase_ : str=30 , lowercase_ : List[str]=2 , lowercase_ : str=3 , lowercase_ : Dict=True , lowercase_ : int=True , lowercase_ : List[str]=32 , lowercase_ : List[str]=5 , lowercase_ : Dict=4 , lowercase_ : Optional[int]=37 , lowercase_ : Dict="gelu" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : Dict=10 , lowercase_ : Optional[int]=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Union[str, Any]=0.6 , lowercase_ : Dict=None , ):
lowercase_ : Any = parent
lowercase_ : Tuple = batch_size
lowercase_ : List[Any] = image_size
lowercase_ : Dict = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Dict = is_training
lowercase_ : Any = use_labels
lowercase_ : Optional[int] = hidden_size
lowercase_ : List[str] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Dict = intermediate_size
lowercase_ : Dict = hidden_act
lowercase_ : int = hidden_dropout_prob
lowercase_ : Dict = attention_probs_dropout_prob
lowercase_ : Union[str, Any] = type_sequence_label_size
lowercase_ : Union[str, Any] = initializer_range
lowercase_ : Tuple = mask_ratio
lowercase_ : List[Any] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowercase_ : str = (image_size // patch_size) ** 2
lowercase_ : Tuple = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : List[Any] = None
if self.use_labels:
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Any = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = ViTMAEModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Any = ViTMAEForPreTraining(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Any = model(lowercase_ )
lowercase_ : Union[str, Any] = (self.image_size // self.patch_size) ** 2
lowercase_ : Union[str, Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowercase_ : Dict = 1
lowercase_ : Union[str, Any] = ViTMAEForPreTraining(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase_ : Union[str, Any] = model(lowercase_ )
lowercase_ : Any = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : str = self.prepare_config_and_inputs()
lowercase_ : List[str] = config_and_inputs
lowercase_ : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCamelCase__ = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : List[Any] = ViTMAEModelTester(self )
lowercase_ : List[str] = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Optional[int] = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase_ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_ , nn.Linear ) )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Tuple = model_class(lowercase_ )
lowercase_ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : List[Any] = [*signature.parameters.keys()]
lowercase_ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : str , lowercase_ : Dict ):
# make masks reproducible
np.random.seed(2 )
lowercase_ : Optional[int] = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowercase_ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowercase_ : Optional[int] = torch.from_numpy(lowercase_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowercase_ : List[Any] = pt_noise
super().check_pt_tf_models(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : List[Any] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowercase_ : str = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowercase_ : Dict = outputs[0].cpu().numpy()
lowercase_ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = model_class.from_pretrained(lowercase_ )
model.to(lowercase_ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowercase_ : Optional[int] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
# Make sure we don't have nans
lowercase_ : Optional[int] = after_outputs[0].cpu().numpy()
lowercase_ : Any = 0
lowercase_ : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Any = ViTMAEModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def lowerCamelCase ( ) -> List[str]:
lowercase_ : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowercase_ : Union[str, Any] = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(lowercase_ )
lowercase_ : Dict = self.default_image_processor
lowercase_ : int = prepare_img()
lowercase_ : List[str] = image_processor(images=lowercase_ , return_tensors="""pt""" ).to(lowercase_ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowercase_ : Union[str, Any] = ViTMAEConfig()
lowercase_ : List[Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowercase_ : List[str] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowercase_ : Union[str, Any] = model(**lowercase_ , noise=torch.from_numpy(lowercase_ ).to(device=lowercase_ ) )
# verify the logits
lowercase_ : int = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , lowercase_ )
lowercase_ : int = torch.tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowercase_ ) , atol=1E-4 ) )
| 354 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''blenderbot-small'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Optional[int] , lowercase_ : Any=50265 , lowercase_ : Tuple=512 , lowercase_ : Tuple=8 , lowercase_ : Optional[int]=2048 , lowercase_ : Optional[int]=16 , lowercase_ : Dict=8 , lowercase_ : str=2048 , lowercase_ : List[Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : int=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Tuple=True , lowercase_ : List[Any]="gelu" , lowercase_ : Optional[int]=512 , lowercase_ : Tuple=0.1 , lowercase_ : List[str]=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Any=1 , lowercase_ : Any=False , lowercase_ : List[Any]=0 , lowercase_ : Tuple=1 , lowercase_ : int=2 , lowercase_ : Tuple=2 , **lowercase_ : List[Any] , ):
lowercase_ : Optional[int] = vocab_size
lowercase_ : Union[str, Any] = max_position_embeddings
lowercase_ : List[Any] = d_model
lowercase_ : Optional[int] = encoder_ffn_dim
lowercase_ : List[Any] = encoder_layers
lowercase_ : List[str] = encoder_attention_heads
lowercase_ : List[Any] = decoder_ffn_dim
lowercase_ : Optional[Any] = decoder_layers
lowercase_ : List[str] = decoder_attention_heads
lowercase_ : int = dropout
lowercase_ : Optional[int] = attention_dropout
lowercase_ : int = activation_dropout
lowercase_ : int = activation_function
lowercase_ : Any = init_std
lowercase_ : str = encoder_layerdrop
lowercase_ : List[str] = decoder_layerdrop
lowercase_ : Optional[int] = use_cache
lowercase_ : Any = encoder_layers
lowercase_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : List[Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase_ : Any = {0: """batch"""}
lowercase_ : List[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
lowercase_ : List[str] = {0: """batch""", 1: """decoder_sequence"""}
lowercase_ : Optional[int] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase_ : List[str] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase_ : Optional[Any] = self.num_layers
for i in range(lowercase_ ):
lowercase_ : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase_ : Any = {0: """batch""", 2: """past_sequence + sequence"""}
else:
lowercase_ : str = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : List[str] = super().outputs
else:
lowercase_ : str = super(lowercase_ , self ).outputs
if self.use_past:
lowercase_ : int = self.num_layers
for i in range(lowercase_ ):
lowercase_ : str = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase_ : str = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
lowercase_ : Dict = seq_length if not self.use_past else 1
lowercase_ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase_ : Any = {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase_ : Optional[int] = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase_ : Optional[int] = common_inputs["""input_ids"""].shape
lowercase_ : str = common_inputs["""decoder_input_ids"""].shape[1]
lowercase_ : Optional[int] = self.num_attention_heads
lowercase_ : Optional[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase_ : Dict = decoder_seq_length + 3
lowercase_ : Tuple = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase_ : Optional[int] = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
lowercase_ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase_ : Dict = self.num_layers
lowercase_ : Any = min(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = max(lowercase_ , lowercase_ ) - min_num_layers
lowercase_ : int = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
lowercase_ : Union[str, Any] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase_ : str = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowercase_ : List[str] = seqlen + 2
lowercase_ : List[str] = self.num_layers
lowercase_ : Optional[Any] = self.num_attention_heads
lowercase_ : Optional[Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase_ : Optional[Any] = common_inputs["""attention_mask"""].dtype
lowercase_ : List[Any] = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
lowercase_ : List[Any] = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ : Tuple = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase_ : Dict = tokenizer.num_special_tokens_to_add(lowercase_ )
lowercase_ : Optional[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
lowercase_ : List[str] = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase_ : str = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : Tuple = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
elif self.task == "causal-lm":
lowercase_ : List[Any] = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
lowercase_ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : str ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : Tuple = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
lowercase_ : List[Any] = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
| 355 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] ) -> str:
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , UpperCAmelCase__ )
lowercase_ : List[str] = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowercase_ : List[Any] = dataset_size < in_memory_max_size
else:
lowercase_ : Optional[int] = False
lowercase_ : Any = is_small_dataset(UpperCAmelCase__ )
assert result == expected
| 356 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 357 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 358 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Dict=0 ):
lowercase_ : Optional[Any] = np.random.RandomState(lowercase_ )
lowercase_ : Optional[int] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : int = self.get_dummy_inputs()
lowercase_ : Dict = pipe(**lowercase_ ).images
lowercase_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : List[str] = np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowercase_ : Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : List[Any] = self.get_dummy_inputs()
lowercase_ : Optional[Any] = pipe(**lowercase_ ).images
lowercase_ : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : Dict = np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowercase_ : Optional[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Tuple = self.get_dummy_inputs()
lowercase_ : Optional[int] = pipe(**lowercase_ ).images
lowercase_ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : Any = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[str] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowercase_ : Optional[int] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[int] = self.get_dummy_inputs()
lowercase_ : str = pipe(**lowercase_ ).images
lowercase_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : Any = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowercase_ : Union[str, Any] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Tuple = self.get_dummy_inputs()
lowercase_ : Optional[Any] = pipe(**lowercase_ ).images
lowercase_ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : List[Any] = np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowercase_ : Dict = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[Any] = self.get_dummy_inputs()
lowercase_ : Dict = pipe(**lowercase_ ).images
lowercase_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowercase_ : Union[str, Any] = np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Dict = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Tuple = self.get_dummy_inputs()
lowercase_ : Optional[int] = 3 * [inputs["""prompt"""]]
# forward
lowercase_ : Optional[int] = pipe(**lowercase_ )
lowercase_ : List[Any] = output.images[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = self.get_dummy_inputs()
lowercase_ : str = 3 * [inputs.pop("""prompt""" )]
lowercase_ : Optional[Any] = pipe.tokenizer(
lowercase_ , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=lowercase_ , return_tensors="""np""" , )
lowercase_ : Optional[int] = text_inputs["""input_ids"""]
lowercase_ : Any = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
lowercase_ : int = prompt_embeds
# forward
lowercase_ : Union[str, Any] = pipe(**lowercase_ )
lowercase_ : List[Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Dict = self.get_dummy_inputs()
lowercase_ : Dict = 3 * ["""this is a negative prompt"""]
lowercase_ : Optional[int] = negative_prompt
lowercase_ : str = 3 * [inputs["""prompt"""]]
# forward
lowercase_ : int = pipe(**lowercase_ )
lowercase_ : Union[str, Any] = output.images[0, -3:, -3:, -1]
lowercase_ : Optional[int] = self.get_dummy_inputs()
lowercase_ : Any = 3 * [inputs.pop("""prompt""" )]
lowercase_ : Any = []
for p in [prompt, negative_prompt]:
lowercase_ : str = pipe.tokenizer(
lowercase_ , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=lowercase_ , return_tensors="""np""" , )
lowercase_ : Optional[Any] = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
lowercase_ : Optional[Any] = embeds
# forward
lowercase_ : Union[str, Any] = pipe(**lowercase_ )
lowercase_ : Optional[int] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Dict = ort.SessionOptions()
lowercase_ : List[str] = False
return options
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# using the PNDM scheduler by default
lowercase_ : Tuple = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=lowercase_ , feature_extractor=lowercase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[Any] = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
lowercase_ : Optional[int] = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
lowercase_ : List[Any] = output.images
lowercase_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : Tuple = np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Any = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
lowercase_ : int = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : List[Any] = """open neural network exchange"""
lowercase_ : Any = np.random.RandomState(0 )
lowercase_ : Dict = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowercase_ , output_type="""np""" )
lowercase_ : int = output.images
lowercase_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : List[Any] = np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[str] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
lowercase_ : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Union[str, Any] = """open neural network exchange"""
lowercase_ : Dict = np.random.RandomState(0 )
lowercase_ : Any = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowercase_ , output_type="""np""" )
lowercase_ : Union[str, Any] = output.images
lowercase_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : Tuple = np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Tuple = 0
def test_callback_fn(lowercase_ : int , lowercase_ : int , lowercase_ : np.ndarray ) -> None:
lowercase_ : Optional[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
lowercase_ : Tuple = latents[0, -3:, -3:, -1]
lowercase_ : str = np.array(
[-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
lowercase_ : Optional[Any] = latents[0, -3:, -3:, -1]
lowercase_ : str = np.array(
[-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
lowercase_ : Dict = False
lowercase_ : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=lowercase_ , feature_extractor=lowercase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Tuple = """Andromeda galaxy in a bottle"""
lowercase_ : List[Any] = np.random.RandomState(0 )
pipe(
prompt=lowercase_ , num_inference_steps=5 , guidance_scale=7.5 , generator=lowercase_ , callback=lowercase_ , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Any = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=lowercase_ , feature_extractor=lowercase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(lowercase_ , lowercase_ )
assert pipe.safety_checker is None
lowercase_ : str = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowercase_ )
lowercase_ : List[str] = OnnxStableDiffusionPipeline.from_pretrained(lowercase_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowercase_ : Optional[int] = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 359 | '''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
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 0 |
def lowerCamelCase ( UpperCAmelCase__ : list ) -> list:
if len(UpperCAmelCase__ ) < 2:
return collection
def circle_sort_util(UpperCAmelCase__ : list , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> bool:
lowercase_ : str = False
if low == high:
return swapped
lowercase_ : str = low
lowercase_ : Optional[Any] = high
while left < right:
if collection[left] > collection[right]:
lowercase_ : str = (
collection[right],
collection[left],
)
lowercase_ : Optional[int] = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowercase_ : List[Any] = (
collection[right + 1],
collection[left],
)
lowercase_ : Optional[Any] = True
lowercase_ : Dict = low + int((high - low) / 2 )
lowercase_ : List[str] = circle_sort_util(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = circle_sort_util(UpperCAmelCase__ , mid + 1 , UpperCAmelCase__ )
return swapped or left_swap or right_swap
lowercase_ : List[Any] = True
while is_not_sorted is True:
lowercase_ : Tuple = circle_sort_util(UpperCAmelCase__ , 0 , len(UpperCAmelCase__ ) - 1 )
return collection
if __name__ == "__main__":
_lowercase : Dict = input("Enter numbers separated by a comma:\n").strip()
_lowercase : int = [int(item) for item in user_input.split(",")]
print(circle_sort(unsorted))
| 360 | '''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase : Optional[Any] = "▁"
_lowercase : Dict = {"vocab_file": "spiece.model"}
_lowercase : int = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
_lowercase : Optional[int] = {
"google/pegasus-xsum": 512,
}
_lowercase : Dict = logging.get_logger(__name__)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Tuple , lowercase_ : int , lowercase_ : int="<pad>" , lowercase_ : Tuple="</s>" , lowercase_ : Optional[int]="<unk>" , lowercase_ : str="<mask_2>" , lowercase_ : str="<mask_1>" , lowercase_ : str=None , lowercase_ : Optional[int]=103 , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : Optional[int] , ):
lowercase_ : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(lowercase_ )}, but is'''
f''' {type(lowercase_ )}''' )
lowercase_ : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
lowercase_ : str = additional_special_tokens_extended
else:
lowercase_ : int = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
lowercase_ : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , pad_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
lowercase_ : Any = mask_token_sent
lowercase_ : Any = vocab_file
lowercase_ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
# add special tokens to encoder dict
lowercase_ : Dict[int, str] = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase_ : Dict[str, int] = {v: k for k, v in self.encoder.items()}
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return len(self.sp_model ) + self.offset
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : int = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Any ):
lowercase_ : str = self.__dict__.copy()
lowercase_ : int = None
return state
def __setstate__( self : Optional[Any] , lowercase_ : int ):
lowercase_ : int = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase_ : Dict = {}
lowercase_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : str ):
return self.sp_model.encode(lowercase_ , out_type=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : str ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase_ : str = self.sp_model.piece_to_id(lowercase_ )
return sp_id + self.offset
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase_ : List[str] = self.sp_model.IdToPiece(index - self.offset )
return token
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[str] ):
lowercase_ : int = []
lowercase_ : int = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowercase_ ) + token
lowercase_ : List[str] = []
else:
current_sub_tokens.append(lowercase_ )
out_string += self.sp_model.decode(lowercase_ )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any]=False ):
return 1
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] ):
lowercase_ : Union[str, Any] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Any=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ : Optional[Any] = os.path.join(
lowercase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , """wb""" ) as fi:
lowercase_ : Dict = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> str:
lowercase_ : List[Any] = filter(lambda UpperCAmelCase__ : p.requires_grad , model.parameters() )
lowercase_ : Optional[Any] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_lowercase : Union[str, Any] = logging.getLogger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
if metric == "rouge2":
lowercase_ : List[str] = """{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
lowercase_ : List[str] = """{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
lowercase_ : int = """{val_avg_em:.4f}-{step_count}"""
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
""" function.""" )
lowercase_ : Dict = ModelCheckpoint(
dirpath=UpperCAmelCase__ , filename=UpperCAmelCase__ , monitor=F'''val_{metric}''' , mode="""max""" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCamelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
return EarlyStopping(
monitor=F'''val_{metric}''' , mode="""min""" if """loss""" in metric else """max""" , patience=UpperCAmelCase__ , verbose=UpperCAmelCase__ , )
class __magic_name__ ( pl.Callback ):
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowercase_ )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : pl.Trainer , lowercase_ : pl.LightningModule , lowercase_ : str , lowercase_ : Any=True ):
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
lowercase_ : Union[str, Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
lowercase_ : Optional[int] = Path(pl_module.hparams.output_dir )
if type_path == "test":
lowercase_ : Optional[Any] = od / """test_results.txt"""
lowercase_ : str = od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
lowercase_ : Any = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
lowercase_ : Tuple = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=lowercase_ )
generations_file.parent.mkdir(exist_ok=lowercase_ )
with open(lowercase_ , """a+""" ) as writer:
for key in sorted(lowercase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
lowercase_ : List[str] = metrics[key]
if isinstance(lowercase_ , torch.Tensor ):
lowercase_ : List[str] = val.item()
lowercase_ : int = f'''{key}: {val:.6f}\n'''
writer.write(lowercase_ )
if not save_generations:
return
if "preds" in metrics:
lowercase_ : int = """\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(lowercase_ )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Optional[Any] ):
try:
lowercase_ : List[Any] = pl_module.model.model.num_parameters()
except AttributeError:
lowercase_ : Union[str, Any] = pl_module.model.num_parameters()
lowercase_ : Union[str, Any] = count_trainable_parameters(lowercase_ )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : pl.Trainer , lowercase_ : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowercase_ , lowercase_ , """test""" )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : pl.Trainer , lowercase_ : Optional[int] ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
lowercase_ : int = [True] * (num + 1)
lowercase_ : int = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , UpperCAmelCase__ ):
lowercase_ : List[Any] = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Any = int(input("Enter a positive integer: ").strip())
print(prime_sieve_eratosthenes(user_num))
| 363 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 0 |
from collections import defaultdict
from math import gcd
def lowerCamelCase ( UpperCAmelCase__ : int = 1500000 ) -> int:
lowercase_ : defaultdict = defaultdict(UpperCAmelCase__ )
lowercase_ : Any = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , UpperCAmelCase__ , 2 ):
if gcd(UpperCAmelCase__ , UpperCAmelCase__ ) > 1:
continue
lowercase_ : Tuple = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(UpperCAmelCase__ , limit + 1 , UpperCAmelCase__ ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 364 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = 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.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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.
lowercase_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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).
lowercase_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowercase : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
_lowercase : Tuple = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = 42
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : Iterable[int] ):
lowercase_ : Node | None = None
for i in sorted(lowercase_ , reverse=lowercase_ ):
lowercase_ : Optional[int] = Node(lowercase_ , self.head )
def __iter__( self : Optional[int] ):
lowercase_ : Tuple = self.head
while node:
yield node.data
lowercase_ : str = node.next_node
def __len__( self : str ):
return sum(1 for _ in self )
def __str__( self : List[str] ):
return " -> ".join([str(lowercase_ ) for node in self] )
def lowerCamelCase ( UpperCAmelCase__ : SortedLinkedList , UpperCAmelCase__ : SortedLinkedList ) -> SortedLinkedList:
"""simple docstring"""
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : List[str] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Optional[Any] = {
"facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''levit'''
def __init__( self : Optional[Any] , lowercase_ : List[Any]=224 , lowercase_ : Optional[Any]=3 , lowercase_ : Any=3 , lowercase_ : Optional[Any]=2 , lowercase_ : Dict=1 , lowercase_ : int=16 , lowercase_ : Union[str, Any]=[128, 256, 384] , lowercase_ : Dict=[4, 8, 12] , lowercase_ : Tuple=[4, 4, 4] , lowercase_ : str=[16, 16, 16] , lowercase_ : Optional[Any]=0 , lowercase_ : str=[2, 2, 2] , lowercase_ : Tuple=[2, 2, 2] , lowercase_ : Optional[int]=0.02 , **lowercase_ : List[str] , ):
super().__init__(**lowercase_ )
lowercase_ : List[Any] = image_size
lowercase_ : Dict = num_channels
lowercase_ : Any = kernel_size
lowercase_ : Any = stride
lowercase_ : int = padding
lowercase_ : List[str] = hidden_sizes
lowercase_ : str = num_attention_heads
lowercase_ : Dict = depths
lowercase_ : str = key_dim
lowercase_ : str = drop_path_rate
lowercase_ : List[Any] = patch_size
lowercase_ : str = attention_ratio
lowercase_ : List[Any] = mlp_ratio
lowercase_ : str = initializer_range
lowercase_ : List[str] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = version.parse('''1.11''')
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
return 1E-4
| 366 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase ( ) -> Dict:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(UpperCAmelCase__ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCamelCase ( ) -> int:
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCamelCase ( ) -> Union[str, Any]:
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(UpperCAmelCase__ ):
http_head("""https://huggingface.co""" )
| 367 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowercase_ : List[str] = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
lowercase_ : Union[str, Any] = str(bin(UpperCAmelCase__ ) )[2:]
lowercase_ : Tuple = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int("""1""" in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 0 |
'''simple docstring'''
from collections import deque
class __magic_name__ :
def __init__( self : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
lowercase_ : Optional[Any] = process_name # process name
lowercase_ : Any = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
lowercase_ : List[Any] = arrival_time
lowercase_ : Any = burst_time # remaining burst time
lowercase_ : Tuple = 0 # total time of the process wait in ready queue
lowercase_ : Any = 0 # time from arrival time to completion time
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : int , lowercase_ : list[int] , lowercase_ : deque[Process] , lowercase_ : int , ):
# total number of mlfq's queues
lowercase_ : Tuple = number_of_queues
# time slice of queues that round robin algorithm applied
lowercase_ : List[str] = time_slices
# unfinished process is in this ready_queue
lowercase_ : Dict = queue
# current time
lowercase_ : Optional[Any] = current_time
# finished process is in this sequence queue
lowercase_ : deque[Process] = deque()
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : str = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : list[Process] ):
lowercase_ : Tuple = []
for i in range(len(lowercase_ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : list[Process] ):
lowercase_ : Union[str, Any] = []
for i in range(len(lowercase_ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : list[Process] ):
lowercase_ : str = []
for i in range(len(lowercase_ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : deque[Process] ):
return [q.burst_time for q in queue]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Process ):
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : deque[Process] ):
lowercase_ : deque[Process] = deque() # sequence deque of finished process
while len(lowercase_ ) != 0:
lowercase_ : Union[str, Any] = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(lowercase_ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
lowercase_ : int = 0
# set the process's turnaround time because it is finished
lowercase_ : Optional[Any] = self.current_time - cp.arrival_time
# set the completion time
lowercase_ : Optional[Any] = self.current_time
# add the process to queue that has finished queue
finished.append(lowercase_ )
self.finish_queue.extend(lowercase_ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : deque[Process] , lowercase_ : int ):
lowercase_ : deque[Process] = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(lowercase_ ) ):
lowercase_ : List[str] = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(lowercase_ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
lowercase_ : List[Any] = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(lowercase_ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
lowercase_ : str = 0
# set the finish time
lowercase_ : List[str] = self.current_time
# update the process' turnaround time because it is finished
lowercase_ : Dict = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(lowercase_ )
self.finish_queue.extend(lowercase_ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def SCREAMING_SNAKE_CASE_ ( self : int ):
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
lowercase_ : Dict = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
_lowercase : Optional[int] = Process("P1", 0, 53)
_lowercase : Any = Process("P2", 0, 17)
_lowercase : Optional[Any] = Process("P3", 0, 68)
_lowercase : Optional[Any] = Process("P4", 0, 24)
_lowercase : Optional[Any] = 3
_lowercase : List[str] = [17, 25]
_lowercase : int = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
_lowercase : Dict = Process("P1", 0, 53)
_lowercase : List[str] = Process("P2", 0, 17)
_lowercase : Any = Process("P3", 0, 68)
_lowercase : List[Any] = Process("P4", 0, 24)
_lowercase : str = 3
_lowercase : List[str] = [17, 25]
_lowercase : Optional[Any] = deque([Pa, Pa, Pa, Pa])
_lowercase : Dict = MLFQ(number_of_queues, time_slices, queue, 0)
_lowercase : Union[str, Any] = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"""waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"""completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"""turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print sequence of finished processes
print(
f"""sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"""
)
| 369 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
_lowercase : List[str] = None
_lowercase : int = logging.get_logger(__name__)
_lowercase : List[str] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_lowercase : Dict = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
},
"tokenizer_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
},
}
_lowercase : Optional[Any] = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
_lowercase : Tuple = "▁"
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = AlbertTokenizer
def __init__( self : Any , lowercase_ : int=None , lowercase_ : str=None , lowercase_ : Optional[int]=True , lowercase_ : Dict=True , lowercase_ : Union[str, Any]=False , lowercase_ : Optional[Any]="[CLS]" , lowercase_ : Any="[SEP]" , lowercase_ : List[str]="<unk>" , lowercase_ : Dict="[SEP]" , lowercase_ : Union[str, Any]="<pad>" , lowercase_ : List[str]="[CLS]" , lowercase_ : str="[MASK]" , **lowercase_ : Optional[Any] , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowercase_ : Optional[int] = (
AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ , normalized=lowercase_ )
if isinstance(lowercase_ , lowercase_ )
else mask_token
)
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , remove_space=lowercase_ , keep_accents=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , **lowercase_ , )
lowercase_ : Union[str, Any] = do_lower_case
lowercase_ : List[str] = remove_space
lowercase_ : str = keep_accents
lowercase_ : str = vocab_file
lowercase_ : int = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
lowercase_ : List[str] = [self.sep_token_id]
lowercase_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
lowercase_ : Tuple = [self.sep_token_id]
lowercase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ : Any = os.path.join(
lowercase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 0 |
import numpy as np
import qiskit
def lowerCamelCase ( UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : int | None = None ) -> str:
lowercase_ : Tuple = np.random.default_rng(seed=UpperCAmelCase__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
lowercase_ : Dict = 6 * key_len
# Measurement basis for Alice's qubits.
lowercase_ : int = rng.integers(2 , size=UpperCAmelCase__ )
# The set of states Alice will prepare.
lowercase_ : Dict = rng.integers(2 , size=UpperCAmelCase__ )
# Measurement basis for Bob's qubits.
lowercase_ : Tuple = rng.integers(2 , size=UpperCAmelCase__ )
# Quantum Circuit to simulate BB84
lowercase_ : int = qiskit.QuantumCircuit(UpperCAmelCase__ , name="""BB84""" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(UpperCAmelCase__ ):
if alice_state[index] == 1:
bbaa_circ.x(UpperCAmelCase__ )
if alice_basis[index] == 1:
bbaa_circ.h(UpperCAmelCase__ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(UpperCAmelCase__ ):
if bob_basis[index] == 1:
bbaa_circ.h(UpperCAmelCase__ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
lowercase_ : int = qiskit.Aer.get_backend("""aer_simulator""" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
lowercase_ : List[str] = qiskit.execute(UpperCAmelCase__ , UpperCAmelCase__ , shots=1 , seed_simulator=UpperCAmelCase__ )
# Returns the result of measurement.
lowercase_ : Any = job.result().get_counts(UpperCAmelCase__ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
lowercase_ : Union[str, Any] = """""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
lowercase_ : Union[str, Any] = gen_key[:key_len] if len(UpperCAmelCase__ ) >= key_len else gen_key.ljust(UpperCAmelCase__ , """0""" )
return key
if __name__ == "__main__":
print(f"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod()
| 371 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[str] = """laion/clap-htsat-unfused"""
lowercase_ : Dict = tempfile.mkdtemp()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , **lowercase_ : Any ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , **lowercase_ : str ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = self.get_tokenizer()
lowercase_ : Optional[Any] = self.get_feature_extractor()
lowercase_ : str = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
processor.save_pretrained(self.tmpdirname )
lowercase_ : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowercase_ : Tuple = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase_ : Optional[int] = self.get_feature_extractor(do_normalize=lowercase_ , padding_value=1.0 )
lowercase_ : Optional[int] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowercase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Dict = self.get_feature_extractor()
lowercase_ : List[str] = self.get_tokenizer()
lowercase_ : List[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[int] = floats_list((3, 1000) )
lowercase_ : int = feature_extractor(lowercase_ , return_tensors="""np""" )
lowercase_ : List[Any] = processor(audios=lowercase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_feature_extractor()
lowercase_ : Optional[int] = self.get_tokenizer()
lowercase_ : List[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[Any] = """This is a test string"""
lowercase_ : str = processor(text=lowercase_ )
lowercase_ : int = tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.get_feature_extractor()
lowercase_ : Dict = self.get_tokenizer()
lowercase_ : Optional[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase_ : List[str] = processor.batch_decode(lowercase_ )
lowercase_ : Tuple = tokenizer.batch_decode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = self.get_feature_extractor()
lowercase_ : int = self.get_tokenizer()
lowercase_ : Optional[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 350 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = ['''image_processor''', '''tokenizer''']
UpperCamelCase__ = '''BlipImageProcessor'''
UpperCamelCase__ = '''AutoTokenizer'''
def __init__( self : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] ):
super().__init__(lowercase_ , lowercase_ )
# add QFormer tokenizer
lowercase_ : Optional[int] = qformer_tokenizer
def __call__( self : List[Any] , lowercase_ : ImageInput = None , lowercase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase_ : bool = True , lowercase_ : Union[bool, str, PaddingStrategy] = False , lowercase_ : Union[bool, str, TruncationStrategy] = None , lowercase_ : Optional[int] = None , lowercase_ : int = 0 , lowercase_ : Optional[int] = None , lowercase_ : Optional[bool] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = True , lowercase_ : Optional[Union[str, TensorType]] = None , **lowercase_ : Dict , ):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
lowercase_ : List[str] = BatchFeature()
if text is not None:
lowercase_ : Optional[Any] = self.tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
encoding.update(lowercase_ )
lowercase_ : Tuple = self.qformer_tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
lowercase_ : List[Any] = qformer_text_encoding.pop("""input_ids""" )
lowercase_ : List[Any] = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
lowercase_ : str = self.image_processor(lowercase_ , return_tensors=lowercase_ )
encoding.update(lowercase_ )
return encoding
def SCREAMING_SNAKE_CASE_ ( self : List[str] , *lowercase_ : int , **lowercase_ : Any ):
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any] ):
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Tuple = self.tokenizer.model_input_names
lowercase_ : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , **lowercase_ : Optional[int] ):
if os.path.isfile(lowercase_ ):
raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
lowercase_ : List[Any] = os.path.join(lowercase_ , """qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(lowercase_ )
return super().save_pretrained(lowercase_ , **lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , lowercase_ : Any , **lowercase_ : str ):
lowercase_ : Tuple = AutoTokenizer.from_pretrained(lowercase_ , subfolder="""qformer_tokenizer""" )
lowercase_ : List[Any] = cls._get_arguments_from_pretrained(lowercase_ , **lowercase_ )
args.append(lowercase_ )
return cls(*lowercase_ )
| 351 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 0 |
'''simple docstring'''
from math import factorial
_lowercase : List[str] = {str(d): factorial(d) for d in range(10)}
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCAmelCase__ ) )
def lowerCamelCase ( ) -> int:
lowercase_ : Tuple = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , UpperCAmelCase__ ) if sum_of_digit_factorial(UpperCAmelCase__ ) == i )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 352 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : int = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''longformer'''
def __init__( self : Dict , lowercase_ : Union[List[int], int] = 512 , lowercase_ : int = 2 , lowercase_ : int = 1 , lowercase_ : int = 0 , lowercase_ : int = 2 , lowercase_ : int = 30522 , lowercase_ : int = 768 , lowercase_ : int = 12 , lowercase_ : int = 12 , lowercase_ : int = 3072 , lowercase_ : str = "gelu" , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : int = 512 , lowercase_ : int = 2 , lowercase_ : float = 0.02 , lowercase_ : float = 1E-12 , lowercase_ : bool = False , **lowercase_ : List[str] , ):
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
lowercase_ : List[Any] = attention_window
lowercase_ : Dict = sep_token_id
lowercase_ : str = bos_token_id
lowercase_ : str = eos_token_id
lowercase_ : Dict = vocab_size
lowercase_ : Optional[int] = hidden_size
lowercase_ : List[Any] = num_hidden_layers
lowercase_ : Dict = num_attention_heads
lowercase_ : Any = hidden_act
lowercase_ : int = intermediate_size
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : str = attention_probs_dropout_prob
lowercase_ : Optional[int] = max_position_embeddings
lowercase_ : Dict = type_vocab_size
lowercase_ : Tuple = initializer_range
lowercase_ : Tuple = layer_norm_eps
lowercase_ : Tuple = onnx_export
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : "PretrainedConfig" , lowercase_ : str = "default" , lowercase_ : "List[PatchingSpec]" = None ):
super().__init__(lowercase_ , lowercase_ , lowercase_ )
lowercase_ : Dict = True
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
if self.task == "multiple-choice":
lowercase_ : int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = super().outputs
if self.task == "default":
lowercase_ : Optional[int] = {0: """batch"""}
return outputs
@property
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return 1E-4
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : "PreTrainedTokenizerBase" , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : str = super().generate_dummy_inputs(
preprocessor=lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowercase_ : int = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
lowercase_ : List[Any] = 1
return inputs
| 353 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 0 |
'''simple docstring'''
class __magic_name__ :
def __init__( self : Optional[Any] ):
lowercase_ : int = """"""
lowercase_ : str = """"""
lowercase_ : Dict = []
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : int , lowercase_ : int ):
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
lowercase_ : int = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
lowercase_ : Union[str, Any] = self.__min_dist_top_down_dp(lowercase_ , n - 1 )
lowercase_ : List[Any] = self.__min_dist_top_down_dp(m - 1 , lowercase_ )
lowercase_ : List[str] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
lowercase_ : Optional[Any] = 1 + min(lowercase_ , lowercase_ , lowercase_ )
return self.dp[m][n]
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : str ):
lowercase_ : Any = worda
lowercase_ : Union[str, Any] = worda
lowercase_ : Optional[int] = [[-1 for _ in range(len(lowercase_ ) )] for _ in range(len(lowercase_ ) )]
return self.__min_dist_top_down_dp(len(lowercase_ ) - 1 , len(lowercase_ ) - 1 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : str ):
lowercase_ : Any = worda
lowercase_ : int = worda
lowercase_ : str = len(lowercase_ )
lowercase_ : int = len(lowercase_ )
lowercase_ : Tuple = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
lowercase_ : Tuple = j
elif j == 0: # second string is empty
lowercase_ : str = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
lowercase_ : Any = self.dp[i - 1][j - 1]
else:
lowercase_ : str = self.dp[i][j - 1]
lowercase_ : Union[str, Any] = self.dp[i - 1][j]
lowercase_ : List[Any] = self.dp[i - 1][j - 1]
lowercase_ : int = 1 + min(lowercase_ , lowercase_ , lowercase_ )
return self.dp[m][n]
if __name__ == "__main__":
_lowercase : int = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
_lowercase : List[str] = input("Enter the first string: ").strip()
_lowercase : Union[str, Any] = input("Enter the second string: ").strip()
print()
print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 354 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = CycleDiffusionPipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''negative_prompt''',
'''height''',
'''width''',
'''negative_prompt_embeds''',
}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''})
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE_ ( self : int ):
torch.manual_seed(0 )
lowercase_ : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase_ : Any = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
lowercase_ : Optional[int] = 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 , )
torch.manual_seed(0 )
lowercase_ : List[str] = 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=1000 , )
lowercase_ : int = CLIPTextModel(lowercase_ )
lowercase_ : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase_ : Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : int=0 ):
lowercase_ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowercase_ : int = image / 2 + 0.5
if str(lowercase_ ).startswith("""mps""" ):
lowercase_ : Dict = torch.manual_seed(lowercase_ )
else:
lowercase_ : List[str] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase_ : Optional[int] = {
"""prompt""": """An astronaut riding an elephant""",
"""source_prompt""": """An astronaut riding a horse""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""eta""": 0.1,
"""strength""": 0.8,
"""guidance_scale""": 3,
"""source_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase_ : Any = self.get_dummy_components()
lowercase_ : List[Any] = CycleDiffusionPipeline(**lowercase_ )
lowercase_ : Any = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Dict = self.get_dummy_inputs(lowercase_ )
lowercase_ : Any = pipe(**lowercase_ )
lowercase_ : Dict = output.images
lowercase_ : Dict = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase_ : List[Any] = np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , """half""" ):
lowercase_ : List[str] = module.half()
lowercase_ : str = CycleDiffusionPipeline(**lowercase_ )
lowercase_ : Optional[Any] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : str = self.get_dummy_inputs(lowercase_ )
lowercase_ : List[Any] = pipe(**lowercase_ )
lowercase_ : int = output.images
lowercase_ : Union[str, Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase_ : List[Any] = np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : str ):
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : str ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
lowercase_ : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
lowercase_ : int = init_image.resize((512, 512) )
lowercase_ : Optional[int] = """CompVis/stable-diffusion-v1-4"""
lowercase_ : Any = DDIMScheduler.from_pretrained(lowercase_ , subfolder="""scheduler""" )
lowercase_ : List[Any] = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowercase_ : Tuple = """A black colored car"""
lowercase_ : Optional[Any] = """A blue colored car"""
lowercase_ : List[Any] = torch.manual_seed(0 )
lowercase_ : Union[str, Any] = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type="""np""" , )
lowercase_ : List[str] = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
lowercase_ : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
lowercase_ : str = init_image.resize((512, 512) )
lowercase_ : Any = """CompVis/stable-diffusion-v1-4"""
lowercase_ : int = DDIMScheduler.from_pretrained(lowercase_ , subfolder="""scheduler""" )
lowercase_ : Any = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowercase_ : List[Any] = """A black colored car"""
lowercase_ : List[Any] = """A blue colored car"""
lowercase_ : Any = torch.manual_seed(0 )
lowercase_ : Optional[Any] = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type="""np""" , )
lowercase_ : List[str] = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 355 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowercase : List[Any] = "\nimport os\n"
_lowercase : List[str] = "\ndef foo():\n import os\n return False\n"
_lowercase : List[str] = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
_lowercase : Any = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
_lowercase : str = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
_lowercase : List[Any] = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
_lowercase : Optional[int] = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
_lowercase : Tuple = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
_lowercase : List[Any] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
_lowercase : Optional[int] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
_lowercase : Any = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""" , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple ) -> Union[str, Any]:
lowercase_ : Tuple = os.path.join(UpperCAmelCase__ , """test_file.py""" )
with open(UpperCAmelCase__ , """w""" ) as _tmp_file:
_tmp_file.write(UpperCAmelCase__ )
lowercase_ : Optional[Any] = get_imports(UpperCAmelCase__ )
assert parsed_imports == ["os"]
| 356 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = DDIMPipeline
UpperCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''latents''',
'''callback''',
'''callback_steps''',
}
UpperCamelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
torch.manual_seed(0 )
lowercase_ : 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""") , )
lowercase_ : Optional[Any] = DDIMScheduler()
lowercase_ : Tuple = {"""unet""": unet, """scheduler""": scheduler}
return components
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Optional[int] , lowercase_ : List[Any]=0 ):
if str(lowercase_ ).startswith("""mps""" ):
lowercase_ : List[str] = torch.manual_seed(lowercase_ )
else:
lowercase_ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase_ : Dict = {
"""batch_size""": 1,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = """cpu"""
lowercase_ : Union[str, Any] = self.get_dummy_components()
lowercase_ : str = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : List[str] = self.get_dummy_inputs(lowercase_ )
lowercase_ : str = pipe(**lowercase_ ).images
lowercase_ : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
lowercase_ : str = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowercase_ : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[Any] = """google/ddpm-cifar10-32"""
lowercase_ : Any = UNetaDModel.from_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = DDIMScheduler()
lowercase_ : str = DDIMPipeline(unet=lowercase_ , scheduler=lowercase_ )
ddim.to(lowercase_ )
ddim.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Tuple = torch.manual_seed(0 )
lowercase_ : Dict = ddim(generator=lowercase_ , eta=0.0 , output_type="""numpy""" ).images
lowercase_ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase_ : str = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = """google/ddpm-ema-bedroom-256"""
lowercase_ : Any = UNetaDModel.from_pretrained(lowercase_ )
lowercase_ : int = DDIMScheduler.from_pretrained(lowercase_ )
lowercase_ : Any = DDIMPipeline(unet=lowercase_ , scheduler=lowercase_ )
ddpm.to(lowercase_ )
ddpm.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[Any] = torch.manual_seed(0 )
lowercase_ : Tuple = ddpm(generator=lowercase_ , output_type="""numpy""" ).images
lowercase_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase_ : Tuple = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 357 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 0 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 358 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
while second != 0:
lowercase_ : str = first & second
first ^= second
lowercase_ : str = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Tuple = int(input("Enter the first number: ").strip())
_lowercase : Any = int(input("Enter the second number: ").strip())
print(f"""{add(first, second) = }""")
| 359 | '''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
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 0 |
import heapq
def lowerCamelCase ( UpperCAmelCase__ : dict ) -> set[int]:
lowercase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(UpperCAmelCase__ , [-1 * len(UpperCAmelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
lowercase_ : List[Any] = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
lowercase_ : Tuple = heapq.heappop(UpperCAmelCase__ )[1][0]
chosen_vertices.add(UpperCAmelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
lowercase_ : Union[str, Any] = elem[1][1].index(UpperCAmelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(UpperCAmelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 360 | '''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_lowercase : Any = True
except (ImportError, ModuleNotFoundError):
_lowercase : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
re.sub("""<n>""" , """""" , UpperCAmelCase__ ) # 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(UpperCAmelCase__ ) )
| 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 0 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
_lowercase : Any = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
_lowercase : Tuple = "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
_lowercase : Union[str, Any] = "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_ ( self : int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""] , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : str , lowercase_ : Any , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=1 , lowercase_ : Any="binary" , lowercase_ : Optional[Any]=None , lowercase_ : Any="warn" , ):
lowercase_ : Union[str, Any] = recall_score(
lowercase_ , lowercase_ , labels=lowercase_ , pos_label=lowercase_ , average=lowercase_ , sample_weight=lowercase_ , zero_division=lowercase_ , )
return {"recall": float(lowercase_ ) if score.size == 1 else score}
| 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
return x if y == 0 else greatest_common_divisor(UpperCAmelCase__ , x % y )
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
return (x * y) // greatest_common_divisor(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int = 20 ) -> int:
lowercase_ : Dict = 1
for i in range(1 , n + 1 ):
lowercase_ : Optional[int] = lcm(UpperCAmelCase__ , UpperCAmelCase__ )
return g
if __name__ == "__main__":
print(f"""{solution() = }""")
| 363 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
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
_lowercase : str = 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>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default='''cifar10''', metadata={'''help''': '''Name of a dataset from the datasets package'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''The column name of the images in the files.'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A folder containing the training data.'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A folder containing the validation data.'''})
UpperCamelCase__ = field(
default=0.15, metadata={'''help''': '''Percent to split off of train for validation.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
}, )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : int = {}
if self.train_dir is not None:
lowercase_ : int = self.train_dir
if self.validation_dir is not None:
lowercase_ : Optional[int] = self.validation_dir
lowercase_ : Optional[Any] = data_files if data_files else None
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''})
UpperCamelCase__ = field(
default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Name or path of preprocessor config.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
}, )
UpperCamelCase__ = field(
default=0.75, metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''})
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = field(
default=1e-3, metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''})
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Optional[Any]:
lowercase_ : List[Any] = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowerCamelCase ( ) -> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase_ : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ : Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , UpperCAmelCase__ , UpperCAmelCase__ )
# 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()
lowercase_ : str = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase__ )
transformers.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase_ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
lowercase_ : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase_ : List[str] = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCAmelCase__ ) and data_args.train_val_split > 0.0:
lowercase_ : int = ds["""train"""].train_test_split(data_args.train_val_split )
lowercase_ : Union[str, Any] = split["""train"""]
lowercase_ : Tuple = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ : Any = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowercase_ : Any = ViTMAEConfig.from_pretrained(model_args.config_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
lowercase_ : List[str] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
else:
lowercase_ : Dict = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowercase_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
lowercase_ : str = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
else:
lowercase_ : List[str] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase_ : str = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
lowercase_ : Optional[int] = ViTMAEForPreTraining(UpperCAmelCase__ )
if training_args.do_train:
lowercase_ : Union[str, Any] = ds["""train"""].column_names
else:
lowercase_ : str = ds["""validation"""].column_names
if data_args.image_column_name is not None:
lowercase_ : Optional[int] = data_args.image_column_name
elif "image" in column_names:
lowercase_ : Tuple = """image"""
elif "img" in column_names:
lowercase_ : int = """img"""
else:
lowercase_ : List[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowercase_ : Union[str, Any] = image_processor.size["""shortest_edge"""]
else:
lowercase_ : str = (image_processor.size["""height"""], image_processor.size["""width"""])
lowercase_ : List[str] = Compose(
[
Lambda(lambda UpperCAmelCase__ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(UpperCAmelCase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(UpperCAmelCase__ : List[str] ):
lowercase_ : int = [transforms(UpperCAmelCase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
lowercase_ : Optional[Any] = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(UpperCAmelCase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
lowercase_ : Union[str, Any] = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(UpperCAmelCase__ )
# Compute absolute learning rate
lowercase_ : Union[str, Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase_ : Union[str, Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
lowercase_ : int = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
lowercase_ : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
lowercase_ : Optional[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase_ : List[Any] = last_checkpoint
lowercase_ : str = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
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:
lowercase_ : int = trainer.evaluate()
trainer.log_metrics("""eval""" , UpperCAmelCase__ )
trainer.save_metrics("""eval""" , UpperCAmelCase__ )
# Write model card and (optionally) push to hub
lowercase_ : Union[str, Any] = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCAmelCase__ )
else:
trainer.create_model_card(**UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 364 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = 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.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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.
lowercase_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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).
lowercase_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase ( ) -> Generator[int, None, None]:
"""simple docstring"""
lowercase_ : dict[int, int] = {}
lowercase_ : int = 2
while True:
lowercase_ : Tuple = factor_map.pop(UpperCAmelCase__ , UpperCAmelCase__ )
if factor:
lowercase_ : Dict = factor + prime
while x in factor_map:
x += factor
lowercase_ : str = factor
else:
lowercase_ : Union[str, Any] = prime
yield prime
prime += 1
def lowerCamelCase ( UpperCAmelCase__ : float = 1e10 ) -> int:
"""simple docstring"""
lowercase_ : int = sieve()
lowercase_ : int = 1
while True:
lowercase_ : Union[str, Any] = next(UpperCAmelCase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(UpperCAmelCase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 0 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
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
# 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>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowercase : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
}, )
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Train language if it is different from the evaluation language.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}, )
UpperCamelCase__ = field(
default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}, )
def lowerCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase_ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase_ : Optional[int] = 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_xnli""" , UpperCAmelCase__ )
# 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()
lowercase_ : Optional[int] = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase__ )
datasets.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase_ : Dict = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowercase_ : int = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowercase_ : int = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowercase_ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
lowercase_ : str = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowercase_ : int = eval_dataset.features["""label"""].names
if training_args.do_predict:
lowercase_ : List[str] = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowercase_ : List[Any] = predict_dataset.features["""label"""].names
# Labels
lowercase_ : Dict = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ : Dict = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel={str(UpperCAmelCase__ ): label for i, label in enumerate(UpperCAmelCase__ )} , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase_ : Optional[int] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase_ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , 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 , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowercase_ : Dict = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowercase_ : Any = False
def preprocess_function(UpperCAmelCase__ : Tuple ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=UpperCAmelCase__ , max_length=data_args.max_seq_length , truncation=UpperCAmelCase__ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase_ : Optional[int] = min(len(UpperCAmelCase__ ) , data_args.max_train_samples )
lowercase_ : Optional[Any] = train_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
lowercase_ : Tuple = train_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(UpperCAmelCase__ ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase_ : str = min(len(UpperCAmelCase__ ) , data_args.max_eval_samples )
lowercase_ : Tuple = eval_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
lowercase_ : Dict = eval_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowercase_ : str = min(len(UpperCAmelCase__ ) , data_args.max_predict_samples )
lowercase_ : Optional[int] = predict_dataset.select(range(UpperCAmelCase__ ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
lowercase_ : Dict = predict_dataset.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
lowercase_ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(UpperCAmelCase__ : EvalPrediction ):
lowercase_ : int = p.predictions[0] if isinstance(p.predictions , UpperCAmelCase__ ) else p.predictions
lowercase_ : Union[str, Any] = np.argmax(UpperCAmelCase__ , axis=1 )
return metric.compute(predictions=UpperCAmelCase__ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowercase_ : List[str] = default_data_collator
elif training_args.fpaa:
lowercase_ : Tuple = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 )
else:
lowercase_ : Optional[int] = None
# Initialize our Trainer
lowercase_ : int = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
lowercase_ : int = None
if training_args.resume_from_checkpoint is not None:
lowercase_ : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase_ : List[str] = last_checkpoint
lowercase_ : Tuple = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
lowercase_ : Tuple = train_result.metrics
lowercase_ : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase__ )
)
lowercase_ : str = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , UpperCAmelCase__ )
trainer.save_metrics("""train""" , UpperCAmelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase_ : List[Any] = trainer.evaluate(eval_dataset=UpperCAmelCase__ )
lowercase_ : Tuple = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase__ )
lowercase_ : List[str] = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.log_metrics("""eval""" , UpperCAmelCase__ )
trainer.save_metrics("""eval""" , UpperCAmelCase__ )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase_ : str = trainer.predict(UpperCAmelCase__ , metric_key_prefix="""predict""" )
lowercase_ : Optional[int] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCAmelCase__ )
)
lowercase_ : Optional[Any] = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.log_metrics("""predict""" , UpperCAmelCase__ )
trainer.save_metrics("""predict""" , UpperCAmelCase__ )
lowercase_ : List[str] = np.argmax(UpperCAmelCase__ , axis=1 )
lowercase_ : List[Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(UpperCAmelCase__ ):
lowercase_ : List[Any] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 366 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[Any]=1024 , UpperCAmelCase__ : Optional[int]=1024 , UpperCAmelCase__ : List[str]=False , **UpperCAmelCase__ : Dict ) -> Optional[int]:
lowercase_ : str = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = SeqaSeqDataset(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , type_path="""train""" , **UpperCAmelCase__ )
lowercase_ : Optional[int] = tok.pad_token_id
def get_lens(UpperCAmelCase__ : Union[str, Any] ):
lowercase_ : Optional[int] = tqdm(
DataLoader(UpperCAmelCase__ , batch_size=512 , num_workers=8 , shuffle=UpperCAmelCase__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
lowercase_ : Union[str, Any] = []
for batch in dl:
lowercase_ : List[str] = batch["""input_ids"""].ne(UpperCAmelCase__ ).sum(1 ).tolist()
lowercase_ : Any = batch["""labels"""].ne(UpperCAmelCase__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
max_lens.append(max(UpperCAmelCase__ , UpperCAmelCase__ ) )
else:
max_lens.extend(UpperCAmelCase__ )
return max_lens
lowercase_ : Optional[Any] = get_lens(UpperCAmelCase__ )
lowercase_ : Tuple = SeqaSeqDataset(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , type_path="""val""" , **UpperCAmelCase__ )
lowercase_ : Optional[int] = get_lens(UpperCAmelCase__ )
pickle_save(UpperCAmelCase__ , train_ds.len_file )
pickle_save(UpperCAmelCase__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 367 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowercase : Any = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowercase : Optional[Any] = "\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {'f1': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results['f1'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results['f1'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n >>> print(round(results['f1'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'f1': array([0.8, 0. , 0. ])}\n"
_lowercase : List[Any] = "\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class __magic_name__ ( datasets.Metric):
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] , )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : str=None , lowercase_ : Any=1 , lowercase_ : str="binary" , lowercase_ : Union[str, Any]=None ):
lowercase_ : Optional[int] = fa_score(
lowercase_ , lowercase_ , labels=lowercase_ , pos_label=lowercase_ , average=lowercase_ , sample_weight=lowercase_ )
return {"f1": float(lowercase_ ) if score.size == 1 else score}
| 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = '''PIL.Image.Image'''
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 369 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
"facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''encodec'''
def __init__( self : Optional[int] , lowercase_ : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase_ : Tuple=24000 , lowercase_ : str=1 , lowercase_ : Optional[Any]=False , lowercase_ : Optional[int]=None , lowercase_ : Union[str, Any]=None , lowercase_ : str=128 , lowercase_ : Tuple=32 , lowercase_ : Dict=1 , lowercase_ : Optional[Any]=[8, 5, 4, 2] , lowercase_ : Optional[int]="weight_norm" , lowercase_ : Tuple=7 , lowercase_ : Union[str, Any]=7 , lowercase_ : Dict=3 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[Any]=True , lowercase_ : List[Any]="reflect" , lowercase_ : str=2 , lowercase_ : Any=2 , lowercase_ : Tuple=1.0 , lowercase_ : Dict=1024 , lowercase_ : List[Any]=None , lowercase_ : Dict=True , **lowercase_ : str , ):
lowercase_ : Union[str, Any] = target_bandwidths
lowercase_ : Optional[int] = sampling_rate
lowercase_ : Union[str, Any] = audio_channels
lowercase_ : str = normalize
lowercase_ : Dict = chunk_length_s
lowercase_ : Optional[int] = overlap
lowercase_ : Any = hidden_size
lowercase_ : List[Any] = num_filters
lowercase_ : Tuple = num_residual_layers
lowercase_ : List[Any] = upsampling_ratios
lowercase_ : List[Any] = norm_type
lowercase_ : List[str] = kernel_size
lowercase_ : Tuple = last_kernel_size
lowercase_ : Optional[Any] = residual_kernel_size
lowercase_ : Any = dilation_growth_rate
lowercase_ : Optional[int] = use_causal_conv
lowercase_ : Optional[int] = pad_mode
lowercase_ : str = compress
lowercase_ : Any = num_lstm_layers
lowercase_ : List[str] = trim_right_ratio
lowercase_ : Optional[int] = codebook_size
lowercase_ : Optional[int] = codebook_dim if codebook_dim is not None else hidden_size
lowercase_ : List[Any] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''' )
super().__init__(**lowercase_ )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Union[str, Any] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 0 |
_lowercase : int = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 371 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A(__a: Optional[Any] , __a: int , __a: List[str] , __a: int=5 ):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>" ) == 1
lowerCAmelCase_ = torch.tensor(tokenizer.encode(__a , add_special_tokens=__a ) ).unsqueeze(0 ) # Batch size 1
lowerCAmelCase_ = model(__a )[0] # The last hidden-state is the first element of the output tuple
lowerCAmelCase_ = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
lowerCAmelCase_ = logits[0, masked_index, :]
lowerCAmelCase_ = logits.softmax(dim=0 )
lowerCAmelCase_ , lowerCAmelCase_ = prob.topk(k=__a , dim=0 )
lowerCAmelCase_ = " ".join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__a ) )] )
lowerCAmelCase_ = tokenizer.mask_token
lowerCAmelCase_ = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" " ) ):
lowerCAmelCase_ = predicted_token_bpe.replace("\u2581" , " " )
if " {0}".format(__a ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(" {0}".format(__a ) , __a ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(__a , __a ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
lowerCamelCase__ = CamembertTokenizer.from_pretrained('''camembert-base''')
lowerCamelCase__ = CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()
lowerCamelCase__ = '''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 22 |
def A(__a: Tuple ):
lowerCAmelCase_ = len(__a )
while cur > 1:
# Find the maximum number in arr
lowerCAmelCase_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCAmelCase_ = arr[mi::-1] + arr[mi + 1 : len(__a )]
# Reverse whole list
lowerCAmelCase_ = arr[cur - 1 :: -1] + arr[cur : len(__a )]
cur -= 1
return arr
if __name__ == "__main__":
lowerCamelCase__ = input('''Enter numbers separated by a comma:\n''').strip()
lowerCamelCase__ = [int(item) for item in user_input.split(''',''')]
print(pancake_sort(unsorted))
| 22 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ (__lowercase , unittest.TestCase ):
lowerCamelCase__ = CanineTokenizer
lowerCamelCase__ = False
def __a ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase_ = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __a ( self ) -> int:
return CanineTokenizer.from_pretrained("google/canine-s" )
def __a ( self , **_a ) -> CanineTokenizer:
lowerCAmelCase_ = self.tokenizer_class.from_pretrained(self.tmpdirname , **_a )
lowerCAmelCase_ = 1024
return tokenizer
@require_torch
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.canine_tokenizer
lowerCAmelCase_ = ["Life is like a box of chocolates.", "You never know what you're gonna get."]
# fmt: off
lowerCAmelCase_ = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
# fmt: on
lowerCAmelCase_ = tokenizer(_a , padding=_a , return_tensors="pt" )
self.assertIsInstance(_a , _a )
lowerCAmelCase_ = list(batch.input_ids.numpy()[0] )
self.assertListEqual(_a , _a )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def __a ( self ) -> str:
lowerCAmelCase_ = self.canine_tokenizer
lowerCAmelCase_ = ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
lowerCAmelCase_ = tokenizer(_a , padding=_a , return_tensors="pt" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("input_ids" , _a )
self.assertIn("attention_mask" , _a )
self.assertIn("token_type_ids" , _a )
@require_torch
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.canine_tokenizer
lowerCAmelCase_ = [
"What's the weater?",
"It's about 25 degrees.",
]
lowerCAmelCase_ = tokenizer(
text_target=_a , max_length=32 , padding="max_length" , truncation=_a , return_tensors="pt" )
self.assertEqual(32 , targets["input_ids"].shape[1] )
def __a ( self ) -> Dict:
# safety check on max_len default value so we are sure the test works
lowerCAmelCase_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
lowerCAmelCase_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ = tempfile.mkdtemp()
lowerCAmelCase_ = " He is very happy, UNwant\u00E9d,running"
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
tokenizer.save_pretrained(_a )
lowerCAmelCase_ = tokenizer.__class__.from_pretrained(_a )
lowerCAmelCase_ = after_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
shutil.rmtree(_a )
lowerCAmelCase_ = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ = tempfile.mkdtemp()
lowerCAmelCase_ = " He is very happy, UNwant\u00E9d,running"
lowerCAmelCase_ = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
lowerCAmelCase_ = chr(0xe007 )
additional_special_tokens.append(_a )
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
tokenizer.save_pretrained(_a )
lowerCAmelCase_ = tokenizer.__class__.from_pretrained(_a )
lowerCAmelCase_ = after_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
self.assertIn(_a , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
lowerCAmelCase_ = tokenizer.__class__.from_pretrained(_a , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(_a )
def __a ( self ) -> int:
lowerCAmelCase_ = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase_ , lowerCAmelCase_ = self.get_clean_sequence(_a )
# a special token for Canine can be defined as follows:
lowerCAmelCase_ = 0xe005
lowerCAmelCase_ = chr(_a )
tokenizer.add_special_tokens({"cls_token": special_token} )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
self.assertEqual(len(_a ) , 1 )
lowerCAmelCase_ = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=_a )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
self.assertEqual(_a , input_encoded + special_token_id )
lowerCAmelCase_ = tokenizer.decode(_a , skip_special_tokens=_a )
self.assertTrue(special_token not in decoded )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase_ = chr(0xe005 )
lowerCAmelCase_ = chr(0xe006 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=_a )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]} )
lowerCAmelCase_ = tokenizer.tokenize(_a )
lowerCAmelCase_ = tokenizer.tokenize(_a )
self.assertEqual(len(_a ) , 1 )
self.assertEqual(len(_a ) , 1 )
self.assertEqual(token_a[0] , _a )
self.assertEqual(token_a[0] , _a )
@require_tokenizers
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# a special token for Canine can be defined as follows:
lowerCAmelCase_ = 0xe006
lowerCAmelCase_ = chr(_a )
lowerCAmelCase_ = AddedToken(_a , lstrip=_a )
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(_a )
tokenizer.from_pretrained(_a )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_a )
with open(os.path.join(_a , "special_tokens_map.json" ) , encoding="utf-8" ) as json_file:
lowerCAmelCase_ = json.load(_a )
with open(os.path.join(_a , "tokenizer_config.json" ) , encoding="utf-8" ) as json_file:
lowerCAmelCase_ = json.load(_a )
# a special token for Canine can be defined as follows:
lowerCAmelCase_ = 0xe006
lowerCAmelCase_ = chr(_a )
lowerCAmelCase_ = [new_token_a]
lowerCAmelCase_ = [new_token_a]
with open(os.path.join(_a , "special_tokens_map.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(_a , _a )
with open(os.path.join(_a , "tokenizer_config.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(_a , _a )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCAmelCase_ = tokenizer_class.from_pretrained(_a , extra_ids=0 )
self.assertIn(_a , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
lowerCAmelCase_ = 0xe007
lowerCAmelCase_ = chr(_a )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCAmelCase_ = [AddedToken(_a , lstrip=_a )]
lowerCAmelCase_ = tokenizer_class.from_pretrained(
_a , additional_special_tokens=_a , extra_ids=0 )
self.assertIn(_a , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase_ = "hello world"
if self.space_between_special_tokens:
lowerCAmelCase_ = "[CLS] hello world [SEP]"
else:
lowerCAmelCase_ = input
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer.decode(_a , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(_a , [output, output.lower()] )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase_ = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
lowerCAmelCase_ = "a"
lowerCAmelCase_ = ord(_a )
for attr in attributes_list:
setattr(_a , attr + "_id" , _a )
self.assertEqual(getattr(_a , _a ) , _a )
self.assertEqual(getattr(_a , attr + "_id" ) , _a )
setattr(_a , attr + "_id" , _a )
self.assertEqual(getattr(_a , _a ) , _a )
self.assertEqual(getattr(_a , attr + "_id" ) , _a )
setattr(_a , "additional_special_tokens_ids" , [] )
self.assertListEqual(getattr(_a , "additional_special_tokens" ) , [] )
self.assertListEqual(getattr(_a , "additional_special_tokens_ids" ) , [] )
lowerCAmelCase_ = 0xe006
lowerCAmelCase_ = chr(_a )
setattr(_a , "additional_special_tokens_ids" , [additional_special_token_id] )
self.assertListEqual(getattr(_a , "additional_special_tokens" ) , [additional_special_token] )
self.assertListEqual(getattr(_a , "additional_special_tokens_ids" ) , [additional_special_token_id] )
def __a ( self ) -> Any:
pass
def __a ( self ) -> Optional[Any]:
pass
def __a ( self ) -> List[Any]:
pass
def __a ( self ) -> Tuple:
pass
def __a ( self ) -> int:
pass
def __a ( self ) -> Any:
pass
def __a ( self ) -> Any:
pass
def __a ( self ) -> List[str]:
pass
| 22 |
import string
from math import logaa
def A(__a: str , __a: str ):
lowerCAmelCase_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
lowerCAmelCase_ = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def A(__a: str , __a: str ):
lowerCAmelCase_ = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
lowerCAmelCase_ = corpus_without_punctuation.split("\n" )
lowerCAmelCase_ = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__a ))
def A(__a: int , __a: int , __a: List[Any]=False ):
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def A(__a: int , __a: int ):
return round(tf * idf , 3 )
| 22 | 1 |
from __future__ import annotations
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''MIT'''
lowerCamelCase__ = '''1.0.0'''
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''[email protected]'''
lowerCamelCase__ = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class __magic_name__ (__lowercase ):
def __init__( self , _a ) -> None:
super().__init__()
lowerCAmelCase_ = []
lowerCAmelCase_ = domain
def __a ( self , _a , _a ) -> None:
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
lowerCAmelCase_ = parse.urljoin(self.domain , _a )
self.urls.append(_a )
def A(__a: str ):
return ".".join(get_sub_domain_name(__a ).split("." )[-2:] )
def A(__a: str ):
return parse.urlparse(__a ).netloc
def A(__a: str = "https://github.com" ):
lowerCAmelCase_ = get_domain_name(__a )
# Initialize the parser
lowerCAmelCase_ = Parser(__a )
try:
# Open URL
lowerCAmelCase_ = requests.get(__a )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
lowerCAmelCase_ = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
lowerCAmelCase_ = requests.get(__a )
# Get the valid email.
lowerCAmelCase_ = re.findall("[a-zA-Z0-9]+@" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(__a )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(__a )
if __name__ == "__main__":
lowerCamelCase__ = emails_from_url('''https://github.com''')
print(F'''{len(emails)} emails found:''')
print('''\n'''.join(sorted(emails)))
| 22 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCamelCase__ = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def A(__a: str , __a: List[Any] ):
warnings.warn(__a , __a )
requires_backends(__a , "sklearn" )
return (preds == labels).mean()
def A(__a: Any , __a: Any ):
warnings.warn(__a , __a )
requires_backends(__a , "sklearn" )
lowerCAmelCase_ = simple_accuracy(__a , __a )
lowerCAmelCase_ = fa_score(y_true=__a , y_pred=__a )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def A(__a: List[str] , __a: Optional[int] ):
warnings.warn(__a , __a )
requires_backends(__a , "sklearn" )
lowerCAmelCase_ = pearsonr(__a , __a )[0]
lowerCAmelCase_ = spearmanr(__a , __a )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def A(__a: Union[str, Any] , __a: Any , __a: str ):
warnings.warn(__a , __a )
requires_backends(__a , "sklearn" )
assert len(__a ) == len(__a ), F"Predictions and labels have mismatched lengths {len(__a )} and {len(__a )}"
if task_name == "cola":
return {"mcc": matthews_corrcoef(__a , __a )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__a , __a )}
elif task_name == "mrpc":
return acc_and_fa(__a , __a )
elif task_name == "sts-b":
return pearson_and_spearman(__a , __a )
elif task_name == "qqp":
return acc_and_fa(__a , __a )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__a , __a )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__a , __a )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__a , __a )}
elif task_name == "rte":
return {"acc": simple_accuracy(__a , __a )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__a , __a )}
elif task_name == "hans":
return {"acc": simple_accuracy(__a , __a )}
else:
raise KeyError(__a )
def A(__a: int , __a: Optional[Any] , __a: Optional[Any] ):
warnings.warn(__a , __a )
requires_backends(__a , "sklearn" )
if len(__a ) != len(__a ):
raise ValueError(F"Predictions and labels have mismatched lengths {len(__a )} and {len(__a )}" )
if task_name == "xnli":
return {"acc": simple_accuracy(__a , __a )}
else:
raise KeyError(__a )
| 22 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __magic_name__ (__lowercase , unittest.TestCase ):
lowerCamelCase__ = LDMTextToImagePipeline
lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS - {
'''negative_prompt''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
'''prompt_embeds''',
}
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase__ = False
def __a ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
lowerCAmelCase_ = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=_a , set_alpha_to_one=_a , )
torch.manual_seed(0 )
lowerCAmelCase_ = 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 , )
torch.manual_seed(0 )
lowerCAmelCase_ = 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=1000 , )
lowerCAmelCase_ = CLIPTextModel(_a )
lowerCAmelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ = {
"unet": unet,
"scheduler": scheduler,
"vqvae": vae,
"bert": text_encoder,
"tokenizer": tokenizer,
}
return components
def __a ( self , _a , _a=0 ) -> Union[str, Any]:
if str(_a ).startswith("mps" ):
lowerCAmelCase_ = torch.manual_seed(_a )
else:
lowerCAmelCase_ = torch.Generator(device=_a ).manual_seed(_a )
lowerCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = LDMTextToImagePipeline(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
lowerCAmelCase_ = self.get_dummy_inputs(_a )
lowerCAmelCase_ = pipe(**_a ).images
lowerCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
lowerCAmelCase_ = np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class __magic_name__ (unittest.TestCase ):
def __a ( self ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self , _a , _a=torch.floataa , _a=0 ) -> int:
lowerCAmelCase_ = torch.manual_seed(_a )
lowerCAmelCase_ = np.random.RandomState(_a ).standard_normal((1, 4, 32, 32) )
lowerCAmelCase_ = torch.from_numpy(_a ).to(device=_a , dtype=_a )
lowerCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __a ( self ) -> str:
lowerCAmelCase_ = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256" ).to(_a )
pipe.set_progress_bar_config(disable=_a )
lowerCAmelCase_ = self.get_inputs(_a )
lowerCAmelCase_ = pipe(**_a ).images
lowerCAmelCase_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
lowerCAmelCase_ = np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] )
lowerCAmelCase_ = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class __magic_name__ (unittest.TestCase ):
def __a ( self ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self , _a , _a=torch.floataa , _a=0 ) -> List[str]:
lowerCAmelCase_ = torch.manual_seed(_a )
lowerCAmelCase_ = np.random.RandomState(_a ).standard_normal((1, 4, 32, 32) )
lowerCAmelCase_ = torch.from_numpy(_a ).to(device=_a , dtype=_a )
lowerCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256" ).to(_a )
pipe.set_progress_bar_config(disable=_a )
lowerCAmelCase_ = self.get_inputs(_a )
lowerCAmelCase_ = pipe(**_a ).images[0]
lowerCAmelCase_ = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy" )
lowerCAmelCase_ = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 22 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ (__lowercase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViTImageProcessor'''
lowerCamelCase__ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self , _a=None , _a=None , **_a ) -> Tuple:
lowerCAmelCase_ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , _a , )
lowerCAmelCase_ = kwargs.pop("feature_extractor" )
lowerCAmelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(_a , _a )
def __call__( self , _a=None , _a=None , _a=None , _a=None , **_a ) -> Dict:
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
lowerCAmelCase_ = self.tokenizer(_a , return_tensors=_a , **_a )
if visual_prompt is not None:
lowerCAmelCase_ = self.image_processor(_a , return_tensors=_a , **_a )
if images is not None:
lowerCAmelCase_ = self.image_processor(_a , return_tensors=_a , **_a )
if visual_prompt is not None and images is not None:
lowerCAmelCase_ = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
lowerCAmelCase_ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
lowerCAmelCase_ = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def __a ( self , *_a , **_a ) -> List[str]:
return self.tokenizer.batch_decode(*_a , **_a )
def __a ( self , *_a , **_a ) -> Optional[int]:
return self.tokenizer.decode(*_a , **_a )
@property
def __a ( self ) -> List[str]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , _a , )
return self.image_processor_class
@property
def __a ( self ) -> Optional[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , _a , )
return self.image_processor
| 22 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class __magic_name__ (__lowercase ):
def __init__( self , _a ) -> Optional[Any]:
lowerCAmelCase_ = data
def __iter__( self ) -> int:
for element in self.data:
yield element
def A(__a: Union[str, Any]=True ):
lowerCAmelCase_ = Accelerator(even_batches=__a )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def A(__a: Accelerator , __a: int , __a: int , __a: bool = False ):
if iterable:
lowerCAmelCase_ = DummyIterableDataset(torch.as_tensor(range(__a ) ) )
else:
lowerCAmelCase_ = TensorDataset(torch.as_tensor(range(__a ) ) )
lowerCAmelCase_ = DataLoader(__a , batch_size=__a )
lowerCAmelCase_ = accelerator.prepare(__a )
return dl
def A(__a: Accelerator , __a: int , __a: int , __a: List[int] , __a: List[int] , ):
lowerCAmelCase_ = create_dataloader(accelerator=__a , dataset_size=__a , batch_size=__a )
lowerCAmelCase_ = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def A():
lowerCAmelCase_ = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
__a , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
__a , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def A():
lowerCAmelCase_ = create_accelerator(even_batches=__a )
verify_dataloader_batch_sizes(
__a , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
__a , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def A():
lowerCAmelCase_ = create_accelerator(even_batches=__a )
lowerCAmelCase_ = torch.nn.Linear(1 , 1 )
lowerCAmelCase_ = accelerator.prepare(__a )
lowerCAmelCase_ = create_dataloader(__a , dataset_size=3 , batch_size=1 )
lowerCAmelCase_ = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(__a ):
lowerCAmelCase_ = ddp_model(batch[0].float() )
lowerCAmelCase_ = output.sum()
loss.backward()
batch_idxs.append(__a )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def A(__a: List[str] ):
with warnings.catch_warnings(record=__a ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , __a )
assert "only supported for multi-GPU" in str(w[-1].message )
def A():
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = create_accelerator(even_batches=__a )
lowerCAmelCase_ = torch.nn.Linear(1 , 1 )
lowerCAmelCase_ = accelerator.prepare(__a )
lowerCAmelCase_ = create_dataloader(__a , dataset_size=3 , batch_size=1 )
lowerCAmelCase_ = create_dataloader(__a , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__a ):
lowerCAmelCase_ = train_dl.batch_sampler.even_batches
lowerCAmelCase_ = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def A():
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = create_accelerator(even_batches=__a )
lowerCAmelCase_ = torch.nn.Linear(1 , 1 )
lowerCAmelCase_ = accelerator.prepare(__a )
create_dataloader(__a , dataset_size=3 , batch_size=1 , iterable=__a )
lowerCAmelCase_ = create_dataloader(__a , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("ignore" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__a ):
lowerCAmelCase_ = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def A():
lowerCAmelCase_ = create_accelerator()
lowerCAmelCase_ = torch.nn.Linear(1 , 1 )
lowerCAmelCase_ = accelerator.prepare(__a )
create_dataloader(__a , dataset_size=3 , batch_size=1 , iterable=__a )
with warnings.catch_warnings(record=__a ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__a ):
pass
assert issubclass(w[-1].category , __a )
assert "only supported for map-style datasets" in str(w[-1].message )
def A():
lowerCAmelCase_ = create_accelerator()
accelerator.print("Test that even_batches variable ensures uniform batches across processes" )
test_default_ensures_even_batch_sizes()
accelerator.print("Run tests with even_batches disabled" )
test_can_disable_even_batches()
accelerator.print("Test joining uneven inputs" )
test_can_join_uneven_inputs()
accelerator.print("Test overriding even_batches when joining uneven inputs" )
test_join_can_override_even_batches()
accelerator.print("Test overriding even_batches for mixed dataloader types" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("Test join with non DDP distributed raises warning" )
lowerCAmelCase_ = accelerator.state.distributed_type
lowerCAmelCase_ = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(__a )
lowerCAmelCase_ = original_state
if __name__ == "__main__":
main()
| 22 |
import datasets
lowerCamelCase__ = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
'''
lowerCamelCase__ = '''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''
lowerCamelCase__ = '''
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
\'accuracy\': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric("xnli")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
'''
def A(__a: Dict , __a: Union[str, Any] ):
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ (datasets.Metric ):
def __a ( self ) -> Tuple:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def __a ( self , _a , _a ) -> List[str]:
return {"accuracy": simple_accuracy(_a , _a )}
| 22 | 1 |
import string
from math import logaa
def A(__a: str , __a: str ):
lowerCAmelCase_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
lowerCAmelCase_ = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def A(__a: str , __a: str ):
lowerCAmelCase_ = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
lowerCAmelCase_ = corpus_without_punctuation.split("\n" )
lowerCAmelCase_ = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__a ))
def A(__a: int , __a: int , __a: List[Any]=False ):
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def A(__a: int , __a: int ):
return round(tf * idf , 3 )
| 22 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowerCamelCase__ = '''bert-base-cased'''
lowerCamelCase__ = '''google/pegasus-xsum'''
lowerCamelCase__ = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__ = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__ = '''patrickvonplaten/t5-tiny-random'''
lowerCamelCase__ = '''sshleifer/bart-tiny-random'''
lowerCamelCase__ = '''sshleifer/tiny-mbart'''
lowerCamelCase__ = '''sshleifer/tiny-marian-en-de'''
def A(__a: Path , __a: list ):
lowerCAmelCase_ = "\n".join(__a )
Path(__a ).open("w" ).writelines(__a )
def A(__a: str ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__a , F"{split}.source" ) , __a )
_dump_articles(os.path.join(__a , F"{split}.target" ) , __a )
return tmp_dir
class __magic_name__ (__lowercase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def __a ( self , _a ) -> Dict:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in ARTICLES )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in SUMMARIES )
lowerCAmelCase_ = 4
lowerCAmelCase_ = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
lowerCAmelCase_ , lowerCAmelCase_ = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=_a , max_target_length=_a , src_lang=_a , tgt_lang=_a , )
lowerCAmelCase_ = DataLoader(_a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(_a , _a )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
lowerCAmelCase_ = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def __a ( self , _a ) -> str:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in ARTICLES )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in SUMMARIES )
lowerCAmelCase_ = 4
lowerCAmelCase_ = LegacySeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=20 , max_target_length=_a , )
lowerCAmelCase_ = DataLoader(_a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
lowerCAmelCase_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
lowerCAmelCase_ = tmp_dir.joinpath("train.source" ).open().readlines()
lowerCAmelCase_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(_a , _a , 128 , _a )
lowerCAmelCase_ = {x.name for x in tmp_dir.iterdir()}
lowerCAmelCase_ = {x.name for x in save_dir.iterdir()}
lowerCAmelCase_ = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(_a ) < len(_a )
assert len(_a ) == 1
assert len(packed_examples[0] ) == sum(len(_a ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def __a ( self ) -> Any:
if not FAIRSEQ_AVAILABLE:
return
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset(max_len=64 )
lowerCAmelCase_ = 64
lowerCAmelCase_ = ds.make_dynamic_sampler(_a , required_batch_size_multiple=_a )
lowerCAmelCase_ = [len(_a ) for x in batch_sampler]
assert len(set(_a ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(_a ) == len(_a ) # no dropped or added examples
lowerCAmelCase_ = DataLoader(_a , batch_sampler=_a , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase_ = []
lowerCAmelCase_ = []
for batch in data_loader:
lowerCAmelCase_ = batch["input_ids"].shape
lowerCAmelCase_ = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
lowerCAmelCase_ = np.product(batch["input_ids"].shape )
num_src_per_batch.append(_a )
if num_src_tokens > (max_tokens * 1.1):
failures.append(_a )
assert num_src_per_batch[0] == max(_a )
if failures:
raise AssertionError(f"too many tokens in {len(_a )} batches" )
def __a ( self ) -> List[str]:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset(max_len=512 )
lowerCAmelCase_ = 2
lowerCAmelCase_ = ds.make_sortish_sampler(_a , shuffle=_a )
lowerCAmelCase_ = DataLoader(_a , batch_size=_a , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase_ = DataLoader(_a , batch_size=_a , collate_fn=ds.collate_fn , num_workers=2 , sampler=_a )
lowerCAmelCase_ = tokenizer.pad_token_id
def count_pad_tokens(_a , _a="input_ids" ):
return [batch[k].eq(_a ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(_a , k="labels" ) ) < sum(count_pad_tokens(_a , k="labels" ) )
assert sum(count_pad_tokens(_a ) ) < sum(count_pad_tokens(_a ) )
assert len(_a ) == len(_a )
def __a ( self , _a=1000 , _a=128 ) -> str:
if os.getenv("USE_REAL_DATA" , _a ):
lowerCAmelCase_ = "examples/seq2seq/wmt_en_ro"
lowerCAmelCase_ = max_len * 2 * 64
if not Path(_a ).joinpath("train.len" ).exists():
save_len_file(_a , _a )
else:
lowerCAmelCase_ = "examples/seq2seq/test_data/wmt_en_ro"
lowerCAmelCase_ = max_len * 4
save_len_file(_a , _a )
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=_a , max_target_length=_a , n_obs=_a , )
return ds, max_tokens, tokenizer
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset()
lowerCAmelCase_ = set(DistributedSortishSampler(_a , 256 , num_replicas=2 , rank=0 , add_extra_examples=_a ) )
lowerCAmelCase_ = set(DistributedSortishSampler(_a , 256 , num_replicas=2 , rank=1 , add_extra_examples=_a ) )
assert idsa.intersection(_a ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def __a ( self , _a ) -> List[str]:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a , use_fast=_a )
if tok_name == MBART_TINY:
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
lowerCAmelCase_ = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
lowerCAmelCase_ = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(_a ) == 1 if tok_name == BART_TINY else len(_a ) == 0
| 22 | 1 |
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a ) -> Tuple:
super().__init__()
lowerCAmelCase_ = class_size
lowerCAmelCase_ = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
lowerCAmelCase_ = nn.Linear(_a , _a )
def __a ( self , _a ) -> Tuple:
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
lowerCAmelCase_ = self.mlp(_a )
return logits
| 22 |
def A(__a: Optional[Any] ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = sum(__a )
lowerCAmelCase_ = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
lowerCAmelCase_ = True
for i in range(1 , s + 1 ):
lowerCAmelCase_ = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
lowerCAmelCase_ = dp[i][j - 1]
if arr[i - 1] <= j:
lowerCAmelCase_ = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
lowerCAmelCase_ = s - 2 * j
break
return diff
| 22 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCamelCase__ = TypeVar('''T''')
class __magic_name__ (Generic[T] ):
def __init__( self , _a , _a ) -> None:
lowerCAmelCase_ = None
lowerCAmelCase_ = len(_a )
lowerCAmelCase_ = [any_type for _ in range(self.N )] + arr
lowerCAmelCase_ = fnc
self.build()
def __a ( self ) -> None:
for p in range(self.N - 1 , 0 , -1 ):
lowerCAmelCase_ = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __a ( self , _a , _a ) -> None:
p += self.N
lowerCAmelCase_ = v
while p > 1:
lowerCAmelCase_ = p // 2
lowerCAmelCase_ = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __a ( self , _a , _a ) -> T | None: # noqa: E741
lowerCAmelCase_ , lowerCAmelCase_ = l + self.N, r + self.N
lowerCAmelCase_ = None
while l <= r:
if l % 2 == 1:
lowerCAmelCase_ = self.st[l] if res is None else self.fn(_a , self.st[l] )
if r % 2 == 0:
lowerCAmelCase_ = self.st[r] if res is None else self.fn(_a , self.st[r] )
lowerCAmelCase_ , lowerCAmelCase_ = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
lowerCamelCase__ = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
lowerCamelCase__ = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
lowerCamelCase__ = SegmentTree(test_array, min)
lowerCamelCase__ = SegmentTree(test_array, max)
lowerCamelCase__ = SegmentTree(test_array, lambda a, b: a + b)
def A():
for i in range(len(__a ) ):
for j in range(__a , len(__a ) ):
lowerCAmelCase_ = reduce(__a , test_array[i : j + 1] )
lowerCAmelCase_ = reduce(__a , test_array[i : j + 1] )
lowerCAmelCase_ = reduce(lambda __a , __a : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__a , __a )
assert max_range == max_segment_tree.query(__a , __a )
assert sum_range == sum_segment_tree.query(__a , __a )
test_all_segments()
for index, value in test_updates.items():
lowerCamelCase__ = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 22 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A(__a: Any , __a: Union[str, Any] , __a: List[str] ):
lowerCAmelCase_ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
lowerCAmelCase_ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
lowerCAmelCase_ = F"{src_lang}-{tgt_lang}"
lowerCAmelCase_ = F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
os.makedirs(__a , exist_ok=__a )
lowerCAmelCase_ = os.path.join(__a , "README.md" )
print(F"Generating {path}" )
with open(__a , "w" , encoding="utf-8" ) as f:
f.write(__a )
# make sure we are under the root of the project
lowerCamelCase__ = Path(__file__).resolve().parent.parent.parent
lowerCamelCase__ = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = model_name.split('''-''')
lowerCamelCase__ = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 22 | 1 |
import requests
lowerCamelCase__ = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def A(__a: str ):
# fetching a list of articles in json format
lowerCAmelCase_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F"{i}.) {article['title']}" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
| 22 |
import re
from filelock import FileLock
try:
import nltk
lowerCamelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCamelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def A(__a: str ):
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 ) )
| 22 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EncodecConfig''',
],
'''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EncodecModel''',
'''EncodecPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 | 1 |
from __future__ import annotations
def A(__a: float , __a: float , __a: float ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def A(__a: float , __a: float , __a: float , ):
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def A(__a: float , __a: float , __a: float , ):
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
__a , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
import logging
from transformers import PretrainedConfig
lowerCamelCase__ = logging.getLogger(__name__)
lowerCamelCase__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class __magic_name__ (__lowercase ):
lowerCamelCase__ = '''bertabs'''
def __init__( self , _a=30522 , _a=512 , _a=6 , _a=512 , _a=8 , _a=512 , _a=0.2 , _a=6 , _a=768 , _a=8 , _a=2048 , _a=0.2 , **_a , ) -> List[Any]:
super().__init__(**_a )
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = max_pos
lowerCAmelCase_ = enc_layers
lowerCAmelCase_ = enc_hidden_size
lowerCAmelCase_ = enc_heads
lowerCAmelCase_ = enc_ff_size
lowerCAmelCase_ = enc_dropout
lowerCAmelCase_ = dec_layers
lowerCAmelCase_ = dec_hidden_size
lowerCAmelCase_ = dec_heads
lowerCAmelCase_ = dec_ff_size
lowerCAmelCase_ = dec_dropout
| 22 | 1 |
lowerCamelCase__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 22 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def A(__a: Tuple , __a: Union[str, Any] ):
lowerCAmelCase_ = checkpoint
lowerCAmelCase_ = {}
lowerCAmelCase_ = vae_state_dict["encoder.conv_in.weight"]
lowerCAmelCase_ = vae_state_dict["encoder.conv_in.bias"]
lowerCAmelCase_ = vae_state_dict["encoder.conv_out.weight"]
lowerCAmelCase_ = vae_state_dict["encoder.conv_out.bias"]
lowerCAmelCase_ = vae_state_dict["encoder.norm_out.weight"]
lowerCAmelCase_ = vae_state_dict["encoder.norm_out.bias"]
lowerCAmelCase_ = vae_state_dict["decoder.conv_in.weight"]
lowerCAmelCase_ = vae_state_dict["decoder.conv_in.bias"]
lowerCAmelCase_ = vae_state_dict["decoder.conv_out.weight"]
lowerCAmelCase_ = vae_state_dict["decoder.conv_out.bias"]
lowerCAmelCase_ = vae_state_dict["decoder.norm_out.weight"]
lowerCAmelCase_ = vae_state_dict["decoder.norm_out.bias"]
lowerCAmelCase_ = vae_state_dict["quant_conv.weight"]
lowerCAmelCase_ = vae_state_dict["quant_conv.bias"]
lowerCAmelCase_ = vae_state_dict["post_quant_conv.weight"]
lowerCAmelCase_ = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
lowerCAmelCase_ = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "encoder.down" in layer} )
lowerCAmelCase_ = {
layer_id: [key for key in vae_state_dict if F"down.{layer_id}" in key] for layer_id in range(__a )
}
# Retrieves the keys for the decoder up blocks only
lowerCAmelCase_ = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "decoder.up" in layer} )
lowerCAmelCase_ = {
layer_id: [key for key in vae_state_dict if F"up.{layer_id}" in key] for layer_id in range(__a )
}
for i in range(__a ):
lowerCAmelCase_ = [key for key in down_blocks[i] if F"down.{i}" in key and F"down.{i}.downsample" not in key]
if F"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
lowerCAmelCase_ = vae_state_dict.pop(
F"encoder.down.{i}.downsample.conv.weight" )
lowerCAmelCase_ = vae_state_dict.pop(
F"encoder.down.{i}.downsample.conv.bias" )
lowerCAmelCase_ = renew_vae_resnet_paths(__a )
lowerCAmelCase_ = {"old": F"down.{i}.block", "new": F"down_blocks.{i}.resnets"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
lowerCAmelCase_ = [key for key in vae_state_dict if "encoder.mid.block" in key]
lowerCAmelCase_ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
lowerCAmelCase_ = [key for key in mid_resnets if F"encoder.mid.block_{i}" in key]
lowerCAmelCase_ = renew_vae_resnet_paths(__a )
lowerCAmelCase_ = {"old": F"mid.block_{i}", "new": F"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
lowerCAmelCase_ = [key for key in vae_state_dict if "encoder.mid.attn" in key]
lowerCAmelCase_ = renew_vae_attention_paths(__a )
lowerCAmelCase_ = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
conv_attn_to_linear(__a )
for i in range(__a ):
lowerCAmelCase_ = num_up_blocks - 1 - i
lowerCAmelCase_ = [
key for key in up_blocks[block_id] if F"up.{block_id}" in key and F"up.{block_id}.upsample" not in key
]
if F"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
lowerCAmelCase_ = vae_state_dict[
F"decoder.up.{block_id}.upsample.conv.weight"
]
lowerCAmelCase_ = vae_state_dict[
F"decoder.up.{block_id}.upsample.conv.bias"
]
lowerCAmelCase_ = renew_vae_resnet_paths(__a )
lowerCAmelCase_ = {"old": F"up.{block_id}.block", "new": F"up_blocks.{i}.resnets"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
lowerCAmelCase_ = [key for key in vae_state_dict if "decoder.mid.block" in key]
lowerCAmelCase_ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
lowerCAmelCase_ = [key for key in mid_resnets if F"decoder.mid.block_{i}" in key]
lowerCAmelCase_ = renew_vae_resnet_paths(__a )
lowerCAmelCase_ = {"old": F"mid.block_{i}", "new": F"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
lowerCAmelCase_ = [key for key in vae_state_dict if "decoder.mid.attn" in key]
lowerCAmelCase_ = renew_vae_attention_paths(__a )
lowerCAmelCase_ = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
conv_attn_to_linear(__a )
return new_checkpoint
def A(__a: str , __a: str , ):
# Only support V1
lowerCAmelCase_ = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" )
lowerCAmelCase_ = io.BytesIO(r.content )
lowerCAmelCase_ = OmegaConf.load(__a )
lowerCAmelCase_ = 512
lowerCAmelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors" ):
from safetensors import safe_open
lowerCAmelCase_ = {}
with safe_open(__a , framework="pt" , device="cpu" ) as f:
for key in f.keys():
lowerCAmelCase_ = f.get_tensor(__a )
else:
lowerCAmelCase_ = torch.load(__a , map_location=__a )["state_dict"]
# Convert the VAE model.
lowerCAmelCase_ = create_vae_diffusers_config(__a , image_size=__a )
lowerCAmelCase_ = custom_convert_ldm_vae_checkpoint(__a , __a )
lowerCAmelCase_ = AutoencoderKL(**__a )
vae.load_state_dict(__a )
vae.save_pretrained(__a )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
lowerCamelCase__ = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 22 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCamelCase__ = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
lowerCamelCase__ = json.load(f)
@require_torch
class __magic_name__ (unittest.TestCase ):
def __a ( self , _a ) -> Tuple:
return FSMTTokenizer.from_pretrained(_a )
def __a ( self , _a ) -> Any:
lowerCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(_a ).to(_a )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 2_6.0],
["ru-en", 2_2.0],
["en-de", 2_2.0],
["de-en", 2_9.0],
] )
@slow
def __a ( self , _a , _a ) -> Optional[int]:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase_ = f"facebook/wmt19-{pair}"
lowerCAmelCase_ = self.get_tokenizer(_a )
lowerCAmelCase_ = self.get_model(_a )
lowerCAmelCase_ = bleu_data[pair]["src"]
lowerCAmelCase_ = bleu_data[pair]["tgt"]
lowerCAmelCase_ = tokenizer(_a , return_tensors="pt" , truncation=_a , padding="longest" ).to(_a )
lowerCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase_ = tokenizer.batch_decode(
_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a )
lowerCAmelCase_ = calculate_bleu(_a , _a )
print(_a )
self.assertGreaterEqual(scores["bleu"] , _a )
| 22 |
def A():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A(__a: list[list[int]] ):
assert all(row == sorted(__a , reverse=__a ) for row in grid )
assert all(list(__a ) == sorted(__a , reverse=__a ) for col in zip(*__a ) )
def A(__a: list[int] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(__a ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCAmelCase_ = (left + right) // 2
lowerCAmelCase_ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCAmelCase_ = mid + 1
else:
lowerCAmelCase_ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__a )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(grid[0] )
for i in range(len(__a ) ):
lowerCAmelCase_ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__a ) * len(grid[0] )) - total
def A(__a: list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
for row in grid:
for i, number in enumerate(__a ):
if number < 0:
total += len(__a ) - i
break
return total
def A():
from timeit import timeit
print("Running benchmarks" )
lowerCAmelCase_ = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCAmelCase_ = timeit(F"{func}(grid=grid)" , setup=__a , number=500 )
print(F"{func}() took {time:0.4f} seconds" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 22 | 1 |
# Imports
import numpy as np
class __magic_name__ :
def __init__( self , _a=None , _a=None , _a=None , _a=None , _a=None ) -> Tuple:
self.set_matricies(red=_a , green=_a , blue=_a , red_edge=_a , nir=_a )
def __a ( self , _a=None , _a=None , _a=None , _a=None , _a=None ) -> Union[str, Any]:
if red is not None:
lowerCAmelCase_ = red
if green is not None:
lowerCAmelCase_ = green
if blue is not None:
lowerCAmelCase_ = blue
if red_edge is not None:
lowerCAmelCase_ = red_edge
if nir is not None:
lowerCAmelCase_ = nir
return True
def __a ( self , _a="" , _a=None , _a=None , _a=None , _a=None , _a=None ) -> Tuple:
self.set_matricies(red=_a , green=_a , blue=_a , red_edge=_a , nir=_a )
lowerCAmelCase_ = {
"ARVI2": self.arvaa,
"CCCI": self.ccci,
"CVI": self.cvi,
"GLI": self.gli,
"NDVI": self.ndvi,
"BNDVI": self.bndvi,
"redEdgeNDVI": self.red_edge_ndvi,
"GNDVI": self.gndvi,
"GBNDVI": self.gbndvi,
"GRNDVI": self.grndvi,
"RBNDVI": self.rbndvi,
"PNDVI": self.pndvi,
"ATSAVI": self.atsavi,
"BWDRVI": self.bwdrvi,
"CIgreen": self.ci_green,
"CIrededge": self.ci_rededge,
"CI": self.ci,
"CTVI": self.ctvi,
"GDVI": self.gdvi,
"EVI": self.evi,
"GEMI": self.gemi,
"GOSAVI": self.gosavi,
"GSAVI": self.gsavi,
"Hue": self.hue,
"IVI": self.ivi,
"IPVI": self.ipvi,
"I": self.i,
"RVI": self.rvi,
"MRVI": self.mrvi,
"MSAVI": self.m_savi,
"NormG": self.norm_g,
"NormNIR": self.norm_nir,
"NormR": self.norm_r,
"NGRDI": self.ngrdi,
"RI": self.ri,
"S": self.s,
"IF": self._if,
"DVI": self.dvi,
"TVI": self.tvi,
"NDRE": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("Index not in the list!" )
return False
def __a ( self ) -> List[str]:
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def __a ( self ) -> int:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def __a ( self ) -> Tuple:
return self.nir * (self.red / (self.green**2))
def __a ( self ) -> Any:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def __a ( self ) -> Any:
return (self.nir - self.red) / (self.nir + self.red)
def __a ( self ) -> Optional[Any]:
return (self.nir - self.blue) / (self.nir + self.blue)
def __a ( self ) -> List[Any]:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def __a ( self ) -> int:
return (self.nir - self.green) / (self.nir + self.green)
def __a ( self ) -> Union[str, Any]:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def __a ( self ) -> Dict:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def __a ( self ) -> str:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def __a ( self ) -> Any:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def __a ( self , _a=0.0_8 , _a=1.2_2 , _a=0.0_3 ) -> int:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def __a ( self ) -> Dict:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def __a ( self ) -> Tuple:
return (self.nir / self.green) - 1
def __a ( self ) -> int:
return (self.nir / self.redEdge) - 1
def __a ( self ) -> Tuple:
return (self.red - self.blue) / self.red
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def __a ( self ) -> Dict:
return self.nir - self.green
def __a ( self ) -> Any:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def __a ( self , _a=0.1_6 ) -> Dict:
return (self.nir - self.green) / (self.nir + self.green + y)
def __a ( self , _a=0.5 ) -> str:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def __a ( self ) -> Any:
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def __a ( self , _a=None , _a=None ) -> List[Any]:
return (self.nir - b) / (a * self.red)
def __a ( self ) -> Union[str, Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def __a ( self ) -> List[Any]:
return (self.red + self.green + self.blue) / 3_0.5
def __a ( self ) -> List[Any]:
return self.nir / self.red
def __a ( self ) -> Optional[Any]:
return (self.rvi() - 1) / (self.rvi() + 1)
def __a ( self ) -> Union[str, Any]:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def __a ( self ) -> Union[str, Any]:
return self.green / (self.nir + self.red + self.green)
def __a ( self ) -> List[str]:
return self.nir / (self.nir + self.red + self.green)
def __a ( self ) -> Optional[int]:
return self.red / (self.nir + self.red + self.green)
def __a ( self ) -> Any:
return (self.green - self.red) / (self.green + self.red)
def __a ( self ) -> Any:
return (self.red - self.green) / (self.red + self.green)
def __a ( self ) -> Tuple:
lowerCAmelCase_ = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowerCAmelCase_ = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def __a ( self ) -> Any:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def __a ( self ) -> Dict:
return self.nir / self.red
def __a ( self ) -> List[str]:
return (self.ndvi() + 0.5) ** (1 / 2)
def __a ( self ) -> Any:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 22 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def A(__a: Dict ):
lowerCAmelCase_ = r"\w+[.]\d+"
lowerCAmelCase_ = re.findall(__a , __a )
for pat in pats:
lowerCAmelCase_ = key.replace(__a , "_".join(pat.split("." ) ) )
return key
def A(__a: str , __a: Tuple , __a: List[Any] ):
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowerCAmelCase_ = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowerCAmelCase_ = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowerCAmelCase_ = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowerCAmelCase_ = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
lowerCAmelCase_ = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowerCAmelCase_ = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowerCAmelCase_ = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A(__a: Dict , __a: Any , __a: List[Any]=42 ):
# Step 1: Convert pytorch tensor to numpy
lowerCAmelCase_ = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowerCAmelCase_ = flax_model.init_weights(PRNGKey(__a ) )
lowerCAmelCase_ = flatten_dict(__a )
lowerCAmelCase_ = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCAmelCase_ = rename_key(__a )
lowerCAmelCase_ = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
lowerCAmelCase_ , lowerCAmelCase_ = rename_key_and_reshape_tensor(__a , __a , __a )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# also add unexpected weight so that warning is thrown
lowerCAmelCase_ = jnp.asarray(__a )
return unflatten_dict(__a )
| 22 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def A(__a: int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A():
lowerCAmelCase_ = 2
while True:
if is_prime(__a ):
yield num
num += 1
def A(__a: int = 200_0000 ):
return sum(takewhile(lambda __a : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 | 1 |
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a ) + 1
lowerCAmelCase_ = len(__a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowerCAmelCase_ = [[0 for i in range(__a )] for j in range(__a )]
# since string of zero length match pattern of zero length
lowerCAmelCase_ = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __a ):
lowerCAmelCase_ = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __a ):
lowerCAmelCase_ = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __a ):
for j in range(1 , __a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowerCAmelCase_ = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowerCAmelCase_ = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowerCAmelCase_ = dp[i - 1][j]
else:
lowerCAmelCase_ = 0
else:
lowerCAmelCase_ = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
lowerCamelCase__ = '''aab'''
lowerCamelCase__ = '''c*a*b'''
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 22 |
import math
def A(__a: int ):
return math.sqrt(__a ) * math.sqrt(__a ) == num
def A(__a: int ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = n
while left <= right:
lowerCAmelCase_ = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
lowerCAmelCase_ = mid - 1
else:
lowerCAmelCase_ = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
def A(__a: str ):
if n_term == "":
return []
lowerCAmelCase_ = []
for temp in range(int(__a ) ):
series.append(F"1/{temp + 1}" if series else "1" )
return series
if __name__ == "__main__":
lowerCamelCase__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lowerCamelCase__ = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def A(__a: Dict , __a: List[str]=None ):
require_version(deps[pkg] , __a )
| 22 | 1 |
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = []
for i in range(len(__a ) - pat_len + 1 ):
lowerCAmelCase_ = True
for j in range(__a ):
if s[i + j] != pattern[j]:
lowerCAmelCase_ = False
break
if match_found:
position.append(__a )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 22 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
lowerCamelCase__ = ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCamelCase__ = {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
raise Exception('''requires fairseq >= 0.9.0''')
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = ''' Hello world! cécé herlolip'''
lowerCamelCase__ = [
('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def A(__a: Any ):
lowerCAmelCase_ = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
]
for k in ignore_keys:
state_dict.pop(__a , __a )
def A(__a: Optional[int] , __a: List[Any] , __a: Union[str, Any] ):
lowerCAmelCase_ = dct.pop(__a )
lowerCAmelCase_ = val
def A(__a: Tuple ):
lowerCAmelCase_ = torch.load(__a , map_location="cpu" )
lowerCAmelCase_ = torch.hub.load("pytorch/fairseq" , "bart.large.cnn" ).eval()
hub_interface.model.load_state_dict(sd["model"] )
return hub_interface
def A(__a: List[str] ):
lowerCAmelCase_ , lowerCAmelCase_ = emb.weight.shape
lowerCAmelCase_ = nn.Linear(__a , __a , bias=__a )
lowerCAmelCase_ = emb.weight.data
return lin_layer
@torch.no_grad()
def A(__a: Tuple , __a: Union[str, Any] , __a: str=None ):
if not os.path.exists(__a ):
lowerCAmelCase_ = torch.hub.load("pytorch/fairseq" , __a ).eval()
else:
lowerCAmelCase_ = load_xsum_checkpoint(__a )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
lowerCAmelCase_ = checkpoint_path.replace("." , "-" )
lowerCAmelCase_ = BartConfig.from_pretrained(__a )
lowerCAmelCase_ = bart.encode(__a ).unsqueeze(0 )
lowerCAmelCase_ = BartTokenizer.from_pretrained(__a ).encode(__a , return_tensors="pt" ).unsqueeze(0 )
if not torch.eq(__a , __a ).all():
raise ValueError(
F"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}" )
if checkpoint_path == "bart.large.mnli":
lowerCAmelCase_ = bart.state_dict()
remove_ignore_keys_(__a )
lowerCAmelCase_ = state_dict["model.decoder.embed_tokens.weight"]
for src, dest in mnli_rename_keys:
rename_key(__a , __a , __a )
lowerCAmelCase_ = BartForSequenceClassification(__a ).eval()
model.load_state_dict(__a )
lowerCAmelCase_ = bart.predict("mnli" , __a , return_logits=__a )
lowerCAmelCase_ = model(__a )[0] # logits
else: # no classification heads to worry about
lowerCAmelCase_ = bart.model.state_dict()
remove_ignore_keys_(__a )
lowerCAmelCase_ = state_dict["decoder.embed_tokens.weight"]
lowerCAmelCase_ = bart.extract_features(__a )
if hf_checkpoint_name == "facebook/bart-large":
lowerCAmelCase_ = BartModel(__a ).eval()
model.load_state_dict(__a )
lowerCAmelCase_ = model(__a ).model[0]
else:
lowerCAmelCase_ = BartForConditionalGeneration(__a ).eval() # an existing summarization ckpt
model.model.load_state_dict(__a )
if hasattr(__a , "lm_head" ):
lowerCAmelCase_ = make_linear_from_emb(model.model.shared )
lowerCAmelCase_ = model.model(__a )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
F"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}" )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
)
lowerCamelCase__ = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 22 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __magic_name__ :
def __init__( self ) -> str:
lowerCAmelCase_ = {}
def __a ( self , _a ) -> None:
lowerCAmelCase_ = {}
def __a ( self , _a , _a , _a ) -> None:
if nodea not in self.connections:
self.add_node(_a )
if nodea not in self.connections:
self.add_node(_a )
lowerCAmelCase_ = probability
def __a ( self ) -> list[str]:
return list(self.connections )
def __a ( self , _a ) -> str:
lowerCAmelCase_ = 0
lowerCAmelCase_ = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def A(__a: str , __a: list[tuple[str, str, float]] , __a: int ):
lowerCAmelCase_ = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__a , __a , __a )
lowerCAmelCase_ = Counter(graph.get_nodes() )
lowerCAmelCase_ = start
for _ in range(__a ):
lowerCAmelCase_ = graph.transition(__a )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class __magic_name__ (__lowercase , unittest.TestCase ):
lowerCamelCase__ = MobileBertTokenizer
lowerCamelCase__ = MobileBertTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = filter_non_english
lowerCamelCase__ = '''google/mobilebert-uncased'''
def __a ( self ) -> Optional[Any]:
super().setUp()
lowerCAmelCase_ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowerCAmelCase_ = 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] ) )
lowerCAmelCase_ = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def __a ( self , _a ) -> Any:
lowerCAmelCase_ = "UNwant\u00E9d,running"
lowerCAmelCase_ = "unwanted, running"
return input_text, output_text
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase_ = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(_a , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [9, 6, 7, 12, 10, 11] )
def __a ( self ) -> Tuple:
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_rust_tokenizer()
lowerCAmelCase_ = "UNwant\u00E9d,running"
lowerCAmelCase_ = tokenizer.tokenize(_a )
lowerCAmelCase_ = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowerCAmelCase_ = self.get_rust_tokenizer()
lowerCAmelCase_ = tokenizer.encode(_a )
lowerCAmelCase_ = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
# With lower casing
lowerCAmelCase_ = self.get_tokenizer(do_lower_case=_a )
lowerCAmelCase_ = self.get_rust_tokenizer(do_lower_case=_a )
lowerCAmelCase_ = "UNwant\u00E9d,running"
lowerCAmelCase_ = tokenizer.tokenize(_a )
lowerCAmelCase_ = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
lowerCAmelCase_ = tokenizer.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
lowerCAmelCase_ = self.get_rust_tokenizer()
lowerCAmelCase_ = tokenizer.encode(_a )
lowerCAmelCase_ = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def __a ( self ) -> Any:
lowerCAmelCase_ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def __a ( self ) -> Dict:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def __a ( self ) -> str:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self ) -> str:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self ) -> str:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self ) -> List[str]:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self ) -> Any:
lowerCAmelCase_ = BasicTokenizer(do_lower_case=_a , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def __a ( self ) -> Any:
lowerCAmelCase_ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
lowerCAmelCase_ = {}
for i, token in enumerate(_a ):
lowerCAmelCase_ = i
lowerCAmelCase_ = WordpieceTokenizer(vocab=_a , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def __a ( self ) -> Optional[int]:
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def __a ( self ) -> List[str]:
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def __a ( self ) -> Dict:
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def __a ( self ) -> Any:
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(_a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(_a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
lowerCAmelCase_ = tokenizer.encode("sequence builders" , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer.encode("multi-sequence build" , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer.build_inputs_with_special_tokens(_a )
lowerCAmelCase_ = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def __a ( self ) -> Union[str, Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase_ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
lowerCAmelCase_ = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
lowerCAmelCase_ = tokenizer_r.encode_plus(
_a , return_attention_mask=_a , return_token_type_ids=_a , return_offsets_mapping=_a , add_special_tokens=_a , )
lowerCAmelCase_ = tokenizer_r.do_lower_case if hasattr(_a , "do_lower_case" ) else False
lowerCAmelCase_ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = ["的", "人", "有"]
lowerCAmelCase_ = "".join(_a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase_ = True
lowerCAmelCase_ = self.tokenizer_class.from_pretrained(_a , **_a )
lowerCAmelCase_ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
lowerCAmelCase_ = tokenizer_p.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer_r.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer_r.convert_ids_to_tokens(_a )
lowerCAmelCase_ = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
lowerCAmelCase_ = False
lowerCAmelCase_ = self.rust_tokenizer_class.from_pretrained(_a , **_a )
lowerCAmelCase_ = self.tokenizer_class.from_pretrained(_a , **_a )
lowerCAmelCase_ = tokenizer_r.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer_p.encode(_a , add_special_tokens=_a )
lowerCAmelCase_ = tokenizer_r.convert_ids_to_tokens(_a )
lowerCAmelCase_ = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that only the first Chinese character is not preceded by "##".
lowerCAmelCase_ = [
f"##{token}" if idx != 0 else token for idx, token in enumerate(_a )
]
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
| 22 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=True , _a=False , _a=False , _a=False , _a=2 , _a=99 , _a=0 , _a=32 , _a=5 , _a=4 , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.0_2 , _a=2 , _a=4 , _a="last" , _a=True , _a=None , _a=0 , ) -> Union[str, Any]:
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = seq_length
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_input_lengths
lowerCAmelCase_ = use_token_type_ids
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = gelu_activation
lowerCAmelCase_ = sinusoidal_embeddings
lowerCAmelCase_ = causal
lowerCAmelCase_ = asm
lowerCAmelCase_ = n_langs
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = n_special
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_choices
lowerCAmelCase_ = summary_type
lowerCAmelCase_ = use_proj
lowerCAmelCase_ = scope
lowerCAmelCase_ = bos_token_id
def __a ( self ) -> Dict:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ = None
if self.use_input_lengths:
lowerCAmelCase_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCAmelCase_ = None
if self.use_token_type_ids:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ = ids_tensor([self.batch_size] , 2 ).float()
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __a ( self ) -> List[str]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> List[str]:
lowerCAmelCase_ = XLMModel(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , lengths=_a , langs=_a )
lowerCAmelCase_ = model(_a , langs=_a )
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Optional[int]:
lowerCAmelCase_ = XLMWithLMHeadModel(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> List[Any]:
lowerCAmelCase_ = XLMForQuestionAnsweringSimple(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(_a , start_positions=_a , end_positions=_a )
lowerCAmelCase_ = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
lowerCAmelCase_ = XLMForQuestionAnswering(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(
_a , start_positions=_a , end_positions=_a , cls_index=_a , is_impossible=_a , p_mask=_a , )
lowerCAmelCase_ = model(
_a , start_positions=_a , end_positions=_a , cls_index=_a , is_impossible=_a , )
((lowerCAmelCase_) , ) = result_with_labels.to_tuple()
lowerCAmelCase_ = model(_a , start_positions=_a , end_positions=_a )
((lowerCAmelCase_) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> int:
lowerCAmelCase_ = XLMForSequenceClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
lowerCAmelCase_ = model(_a , labels=_a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Any:
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = XLMForTokenClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , attention_mask=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Tuple:
lowerCAmelCase_ = self.num_choices
lowerCAmelCase_ = XLMForMultipleChoice(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) = config_and_inputs
lowerCAmelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ (__lowercase , __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase__ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowerCamelCase__ = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __a ( self , _a , _a , _a , _a , _a ) -> Optional[int]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __a ( self , _a , _a , _a=False ) -> str:
lowerCAmelCase_ = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
lowerCAmelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
lowerCAmelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = XLMModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=_a , emb_dim=37 )
def __a ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_a )
def __a ( self ) -> Tuple:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_a )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_a )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_a )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_a )
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_a )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_a )
def __a ( self , _a , _a , _a , _a , _a , _a=False , _a=1 ) -> List[str]:
self.assertIsInstance(_a , _a )
self.assertListEqual(
[isinstance(_a , _a ) for iter_attentions in attentions] , [True] * len(_a ) )
self.assertEqual(len(_a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_a ):
# adds PAD dummy token
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_a ) )
def __a ( self , _a , _a , _a , _a , _a , _a=False , _a=1 ) -> str:
self.assertIsInstance(_a , _a )
self.assertListEqual(
[isinstance(_a , _a ) for iter_hidden_states in hidden_states] , [True] * len(_a ) , )
self.assertEqual(len(_a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_a ):
# adds PAD dummy token
lowerCAmelCase_ = min_length + idx + 1
lowerCAmelCase_ = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_a ) , )
pass
@slow
def __a ( self ) -> Dict:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = XLMModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __magic_name__ (unittest.TestCase ):
@slow
def __a ( self ) -> List[str]:
lowerCAmelCase_ = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(_a )
lowerCAmelCase_ = torch.tensor([[14, 447]] , dtype=torch.long , device=_a ) # the president
lowerCAmelCase_ = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
lowerCAmelCase_ = model.generate(_a , do_sample=_a )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _a )
| 22 |
import math
from collections.abc import Iterator
from itertools import takewhile
def A(__a: int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A():
lowerCAmelCase_ = 2
while True:
if is_prime(__a ):
yield num
num += 1
def A(__a: int = 200_0000 ):
return sum(takewhile(lambda __a : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 22 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ (__lowercase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''BlipImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _a , _a ) -> Union[str, Any]:
lowerCAmelCase_ = False
super().__init__(_a , _a )
lowerCAmelCase_ = self.image_processor
def __call__( self , _a = None , _a = None , _a = True , _a = False , _a = None , _a = None , _a = 0 , _a = None , _a = None , _a = False , _a = False , _a = False , _a = False , _a = False , _a = True , _a = None , **_a , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError("You have to specify either images or text." )
# Get only text
if images is None:
lowerCAmelCase_ = self.tokenizer
lowerCAmelCase_ = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
return text_encoding
# add pixel_values
lowerCAmelCase_ = self.image_processor(_a , return_tensors=_a )
if text is not None:
lowerCAmelCase_ = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
else:
lowerCAmelCase_ = None
if text_encoding is not None:
encoding_image_processor.update(_a )
return encoding_image_processor
def __a ( self , *_a , **_a ) -> Tuple:
return self.tokenizer.batch_decode(*_a , **_a )
def __a ( self , *_a , **_a ) -> Dict:
return self.tokenizer.decode(*_a , **_a )
@property
def __a ( self ) -> Tuple:
lowerCAmelCase_ = self.tokenizer.model_input_names
lowerCAmelCase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 22 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class __magic_name__ (__lowercase ):
lowerCamelCase__ = '''mobilenet_v2'''
def __init__( self , _a=3 , _a=224 , _a=1.0 , _a=8 , _a=8 , _a=6 , _a=32 , _a=True , _a=True , _a="relu6" , _a=True , _a=0.8 , _a=0.0_2 , _a=0.0_0_1 , _a=255 , **_a , ) -> Dict:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = image_size
lowerCAmelCase_ = depth_multiplier
lowerCAmelCase_ = depth_divisible_by
lowerCAmelCase_ = min_depth
lowerCAmelCase_ = expand_ratio
lowerCAmelCase_ = output_stride
lowerCAmelCase_ = first_layer_is_expansion
lowerCAmelCase_ = finegrained_output
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = tf_padding
lowerCAmelCase_ = classifier_dropout_prob
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = semantic_loss_ignore_index
class __magic_name__ (__lowercase ):
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def __a ( self ) -> float:
return 1E-4
| 22 | 1 |
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