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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : List[Any] = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Any = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 361 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : Any = OrderedDict(
[
# Base model mapping
('albert', 'FlaxAlbertModel'),
('bart', 'FlaxBartModel'),
('beit', 'FlaxBeitModel'),
('bert', 'FlaxBertModel'),
('big_bird', 'FlaxBigBirdModel'),
('blenderbot', 'FlaxBlenderbotModel'),
('blenderbot-small', 'FlaxBlenderbotSmallModel'),
('clip', 'FlaxCLIPModel'),
('distilbert', 'FlaxDistilBertModel'),
('electra', 'FlaxElectraModel'),
('gpt-sw3', 'FlaxGPT2Model'),
('gpt2', 'FlaxGPT2Model'),
('gpt_neo', 'FlaxGPTNeoModel'),
('gptj', 'FlaxGPTJModel'),
('longt5', 'FlaxLongT5Model'),
('marian', 'FlaxMarianModel'),
('mbart', 'FlaxMBartModel'),
('mt5', 'FlaxMT5Model'),
('opt', 'FlaxOPTModel'),
('pegasus', 'FlaxPegasusModel'),
('regnet', 'FlaxRegNetModel'),
('resnet', 'FlaxResNetModel'),
('roberta', 'FlaxRobertaModel'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
('roformer', 'FlaxRoFormerModel'),
('t5', 'FlaxT5Model'),
('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
('vit', 'FlaxViTModel'),
('wav2vec2', 'FlaxWav2Vec2Model'),
('whisper', 'FlaxWhisperModel'),
('xglm', 'FlaxXGLMModel'),
('xlm-roberta', 'FlaxXLMRobertaModel'),
]
)
_lowerCamelCase : Optional[Any] = OrderedDict(
[
# Model for pre-training mapping
('albert', 'FlaxAlbertForPreTraining'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForPreTraining'),
('big_bird', 'FlaxBigBirdForPreTraining'),
('electra', 'FlaxElectraForPreTraining'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('t5', 'FlaxT5ForConditionalGeneration'),
('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
_lowerCamelCase : int = OrderedDict(
[
# Model for Masked LM mapping
('albert', 'FlaxAlbertForMaskedLM'),
('bart', 'FlaxBartForConditionalGeneration'),
('bert', 'FlaxBertForMaskedLM'),
('big_bird', 'FlaxBigBirdForMaskedLM'),
('distilbert', 'FlaxDistilBertForMaskedLM'),
('electra', 'FlaxElectraForMaskedLM'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('roberta', 'FlaxRobertaForMaskedLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
('roformer', 'FlaxRoFormerForMaskedLM'),
('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
]
)
_lowerCamelCase : List[str] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('bart', 'FlaxBartForConditionalGeneration'),
('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
('encoder-decoder', 'FlaxEncoderDecoderModel'),
('longt5', 'FlaxLongT5ForConditionalGeneration'),
('marian', 'FlaxMarianMTModel'),
('mbart', 'FlaxMBartForConditionalGeneration'),
('mt5', 'FlaxMT5ForConditionalGeneration'),
('pegasus', 'FlaxPegasusForConditionalGeneration'),
('t5', 'FlaxT5ForConditionalGeneration'),
]
)
_lowerCamelCase : int = OrderedDict(
[
# Model for Image-classsification
('beit', 'FlaxBeitForImageClassification'),
('regnet', 'FlaxRegNetForImageClassification'),
('resnet', 'FlaxResNetForImageClassification'),
('vit', 'FlaxViTForImageClassification'),
]
)
_lowerCamelCase : int = OrderedDict(
[
('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
]
)
_lowerCamelCase : Optional[int] = OrderedDict(
[
# Model for Causal LM mapping
('bart', 'FlaxBartForCausalLM'),
('bert', 'FlaxBertForCausalLM'),
('big_bird', 'FlaxBigBirdForCausalLM'),
('electra', 'FlaxElectraForCausalLM'),
('gpt-sw3', 'FlaxGPT2LMHeadModel'),
('gpt2', 'FlaxGPT2LMHeadModel'),
('gpt_neo', 'FlaxGPTNeoForCausalLM'),
('gptj', 'FlaxGPTJForCausalLM'),
('opt', 'FlaxOPTForCausalLM'),
('roberta', 'FlaxRobertaForCausalLM'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
('xglm', 'FlaxXGLMForCausalLM'),
('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
]
)
_lowerCamelCase : Optional[Any] = OrderedDict(
[
# Model for Sequence Classification mapping
('albert', 'FlaxAlbertForSequenceClassification'),
('bart', 'FlaxBartForSequenceClassification'),
('bert', 'FlaxBertForSequenceClassification'),
('big_bird', 'FlaxBigBirdForSequenceClassification'),
('distilbert', 'FlaxDistilBertForSequenceClassification'),
('electra', 'FlaxElectraForSequenceClassification'),
('mbart', 'FlaxMBartForSequenceClassification'),
('roberta', 'FlaxRobertaForSequenceClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
('roformer', 'FlaxRoFormerForSequenceClassification'),
('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
]
)
_lowerCamelCase : Any = OrderedDict(
[
# Model for Question Answering mapping
('albert', 'FlaxAlbertForQuestionAnswering'),
('bart', 'FlaxBartForQuestionAnswering'),
('bert', 'FlaxBertForQuestionAnswering'),
('big_bird', 'FlaxBigBirdForQuestionAnswering'),
('distilbert', 'FlaxDistilBertForQuestionAnswering'),
('electra', 'FlaxElectraForQuestionAnswering'),
('mbart', 'FlaxMBartForQuestionAnswering'),
('roberta', 'FlaxRobertaForQuestionAnswering'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
('roformer', 'FlaxRoFormerForQuestionAnswering'),
('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
]
)
_lowerCamelCase : Union[str, Any] = OrderedDict(
[
# Model for Token Classification mapping
('albert', 'FlaxAlbertForTokenClassification'),
('bert', 'FlaxBertForTokenClassification'),
('big_bird', 'FlaxBigBirdForTokenClassification'),
('distilbert', 'FlaxDistilBertForTokenClassification'),
('electra', 'FlaxElectraForTokenClassification'),
('roberta', 'FlaxRobertaForTokenClassification'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
('roformer', 'FlaxRoFormerForTokenClassification'),
('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
]
)
_lowerCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
('albert', 'FlaxAlbertForMultipleChoice'),
('bert', 'FlaxBertForMultipleChoice'),
('big_bird', 'FlaxBigBirdForMultipleChoice'),
('distilbert', 'FlaxDistilBertForMultipleChoice'),
('electra', 'FlaxElectraForMultipleChoice'),
('roberta', 'FlaxRobertaForMultipleChoice'),
('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
('roformer', 'FlaxRoFormerForMultipleChoice'),
('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
]
)
_lowerCamelCase : int = OrderedDict(
[
('bert', 'FlaxBertForNextSentencePrediction'),
]
)
_lowerCamelCase : Union[str, Any] = OrderedDict(
[
('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
]
)
_lowerCamelCase : Any = OrderedDict(
[
('whisper', 'FlaxWhisperForAudioClassification'),
]
)
_lowerCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_lowerCamelCase : Dict = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_lowerCamelCase : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_lowerCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_lowerCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_lowerCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_lowerCamelCase : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_lowerCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_lowerCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_lowerCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_lowerCamelCase : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_MAPPING
_lowerCamelCase : Optional[Any] = auto_class_update(FlaxAutoModel)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
_lowerCamelCase : List[str] = auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_lowerCamelCase : List[Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
_lowerCamelCase : List[str] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_lowerCamelCase : Tuple = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_lowerCamelCase : Tuple = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_lowerCamelCase : Any = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_lowerCamelCase : str = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_lowerCamelCase : Tuple = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_lowerCamelCase : List[Any] = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_lowerCamelCase : Union[str, Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='image classification'
)
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_lowerCamelCase : Optional[int] = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class __UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCAmelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_lowerCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)
| 337 | 0 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __UpperCAmelCase ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
'''simple docstring'''
def __init__(self : Dict , _lowerCAmelCase : Union[str, Any]=None , **_lowerCAmelCase : Optional[Any] ):
super().__init__(features=_lowerCAmelCase )
A = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A (self : Dict , _lowerCAmelCase : str ):
import torch
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and column:
if all(
isinstance(_lowerCAmelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_lowerCAmelCase )
return column
def A (self : Tuple , _lowerCAmelCase : List[Any] ):
import torch
if isinstance(_lowerCAmelCase , (str, bytes, type(_lowerCAmelCase )) ):
return value
elif isinstance(_lowerCAmelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
A = {}
if isinstance(_lowerCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
A = {"""dtype""": torch.intaa}
elif isinstance(_lowerCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
A = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_lowerCAmelCase , PIL.Image.Image ):
A = np.asarray(_lowerCAmelCase )
return torch.tensor(_lowerCAmelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def A (self : List[str] , _lowerCAmelCase : int ):
import torch
# support for torch, tf, jax etc.
if hasattr(_lowerCAmelCase , """__array__""" ) and not isinstance(_lowerCAmelCase , torch.Tensor ):
A = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_lowerCAmelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_lowerCAmelCase ) for substruct in data_struct] )
elif isinstance(_lowerCAmelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_lowerCAmelCase ) for substruct in data_struct] )
return self._tensorize(_lowerCAmelCase )
def A (self : Dict , _lowerCAmelCase : dict ):
return map_nested(self._recursive_tensorize , _lowerCAmelCase , map_list=_lowerCAmelCase )
def A (self : Tuple , _lowerCAmelCase : pa.Table ):
A = self.numpy_arrow_extractor().extract_row(_lowerCAmelCase )
A = self.python_features_decoder.decode_row(_lowerCAmelCase )
return self.recursive_tensorize(_lowerCAmelCase )
def A (self : Optional[int] , _lowerCAmelCase : pa.Table ):
A = self.numpy_arrow_extractor().extract_column(_lowerCAmelCase )
A = self.python_features_decoder.decode_column(_lowerCAmelCase , pa_table.column_names[0] )
A = self.recursive_tensorize(_lowerCAmelCase )
A = self._consolidate(_lowerCAmelCase )
return column
def A (self : List[str] , _lowerCAmelCase : pa.Table ):
A = self.numpy_arrow_extractor().extract_batch(_lowerCAmelCase )
A = self.python_features_decoder.decode_batch(_lowerCAmelCase )
A = self.recursive_tensorize(_lowerCAmelCase )
for column_name in batch:
A = self._consolidate(batch[column_name] )
return batch
| 362 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def A (self : Any ):
A = pipeline(
task="""zero-shot-audio-classification""" , model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
A = load_dataset("""ashraq/esc50""" )
A = dataset["""train"""]["""audio"""][-1]["""array"""]
A = audio_classifier(_lowerCAmelCase , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(_lowerCAmelCase ) , [{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}] , )
@unittest.skip("""No models are available in TF""" )
def A (self : List[str] ):
pass
@slow
@require_torch
def A (self : int ):
A = pipeline(
task="""zero-shot-audio-classification""" , model="""laion/clap-htsat-unfused""" , )
# This is an audio of a dog
A = load_dataset("""ashraq/esc50""" )
A = dataset["""train"""]["""audio"""][-1]["""array"""]
A = audio_classifier(_lowerCAmelCase , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(_lowerCAmelCase ) , [
{"""score""": 0.999, """label""": """Sound of a dog"""},
{"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
] , )
A = audio_classifier([audio] * 5 , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(_lowerCAmelCase ) , [
[
{"""score""": 0.999, """label""": """Sound of a dog"""},
{"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
],
]
* 5 , )
A = audio_classifier(
[audio] * 5 , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] , batch_size=5 )
self.assertEqual(
nested_simplify(_lowerCAmelCase ) , [
[
{"""score""": 0.999, """label""": """Sound of a dog"""},
{"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
],
]
* 5 , )
@unittest.skip("""No models are available in TF""" )
def A (self : Tuple ):
pass
| 337 | 0 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowerCamelCase : List[str] = {
'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
'mask_downscaling.0': 'mask_embed.conv1',
'mask_downscaling.1': 'mask_embed.layer_norm1',
'mask_downscaling.3': 'mask_embed.conv2',
'mask_downscaling.4': 'mask_embed.layer_norm2',
'mask_downscaling.6': 'mask_embed.conv3',
'point_embeddings': 'point_embed',
'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
'image_encoder': 'vision_encoder',
'neck.0': 'neck.conv1',
'neck.1': 'neck.layer_norm1',
'neck.2': 'neck.conv2',
'neck.3': 'neck.layer_norm2',
'patch_embed.proj': 'patch_embed.projection',
'.norm': '.layer_norm',
'blocks': 'layers',
}
def __a ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
A = {}
state_dict.pop("""pixel_mean""" , UpperCAmelCase )
state_dict.pop("""pixel_std""" , UpperCAmelCase )
A = R""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
A = key.replace(UpperCAmelCase , UpperCAmelCase )
if re.match(UpperCAmelCase , UpperCAmelCase ):
A = int(re.match(UpperCAmelCase , UpperCAmelCase ).group(2 ) )
if layer_nb == 0:
A = key.replace("""layers.0""" , """proj_in""" )
elif layer_nb == 1:
A = key.replace("""layers.1""" , """layers.0""" )
elif layer_nb == 2:
A = key.replace("""layers.2""" , """proj_out""" )
A = value
A = model_state_dict[
"""prompt_encoder.shared_embedding.positional_embedding"""
]
return model_state_dict
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase="ybelkada/segment-anything" ) ->Any:
"""simple docstring"""
A = hf_hub_download(UpperCAmelCase , F"""checkpoints/{model_name}.pth""" )
if "sam_vit_b" in model_name:
A = SamConfig()
elif "sam_vit_l" in model_name:
A = SamVisionConfig(
hidden_size=1024 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
A = SamConfig(
vision_config=UpperCAmelCase , )
elif "sam_vit_h" in model_name:
A = SamVisionConfig(
hidden_size=1280 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
A = SamConfig(
vision_config=UpperCAmelCase , )
A = torch.load(UpperCAmelCase , map_location="""cpu""" )
A = replace_keys(UpperCAmelCase )
A = SamImageProcessor()
A = SamProcessor(image_processor=UpperCAmelCase )
A = SamModel(UpperCAmelCase )
hf_model.load_state_dict(UpperCAmelCase )
A = hf_model.to("""cuda""" )
A = """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
A = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw ).convert("""RGB""" )
A = [[[400, 650]]]
A = [[1]]
A = processor(images=np.array(UpperCAmelCase ) , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
A = hf_model(**UpperCAmelCase )
A = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
A = processor(
images=np.array(UpperCAmelCase ) , input_points=UpperCAmelCase , input_labels=UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
A = hf_model(**UpperCAmelCase )
A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
A = ((75, 275, 1725, 850),)
A = processor(images=np.array(UpperCAmelCase ) , input_boxes=UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
A = hf_model(**UpperCAmelCase )
A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
A = [[[400, 650], [800, 650]]]
A = [[1, 1]]
A = processor(
images=np.array(UpperCAmelCase ) , input_points=UpperCAmelCase , input_labels=UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
A = hf_model(**UpperCAmelCase )
A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
_lowerCamelCase : List[Any] = ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
parser.add_argument(
'--model_name',
default='sam_vit_h_4b8939',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
parser.add_argument(
'--model_hub_id',
default='ybelkada/segment-anything',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
_lowerCamelCase : List[Any] = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 363 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowerCamelCase : Dict = 'src/diffusers'
_lowerCamelCase : Dict = '.'
# This is to make sure the diffusers module imported is the one in the repo.
_lowerCamelCase : List[str] = importlib.util.spec_from_file_location(
'diffusers',
os.path.join(DIFFUSERS_PATH, '__init__.py'),
submodule_search_locations=[DIFFUSERS_PATH],
)
_lowerCamelCase : Tuple = spec.loader.load_module()
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
return line.startswith(UpperCAmelCase ) or len(UpperCAmelCase ) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""" , UpperCAmelCase ) is not None
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
A = object_name.split(""".""" )
A = 0
# First let's find the module where our object lives.
A = parts[i]
while i < len(UpperCAmelCase ) and not os.path.isfile(os.path.join(UpperCAmelCase , f"""{module}.py""" ) ):
i += 1
if i < len(UpperCAmelCase ):
A = os.path.join(UpperCAmelCase , parts[i] )
if i >= len(UpperCAmelCase ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(UpperCAmelCase , f"""{module}.py""" ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
A = f.readlines()
# Now let's find the class / func in the code!
A = """"""
A = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCAmelCase ) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(UpperCAmelCase ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
A = line_index
while line_index < len(UpperCAmelCase ) and _should_continue(lines[line_index] , UpperCAmelCase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A = lines[start_index:line_index]
return "".join(UpperCAmelCase )
_lowerCamelCase : str = re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_lowerCamelCase : Any = re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
_lowerCamelCase : str = re.compile(R'<FILL\s+[^>]*>')
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
A = code.split("""\n""" )
A = 0
while idx < len(UpperCAmelCase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCAmelCase ):
return re.search(R"""^(\s*)\S""" , lines[idx] ).groups()[0]
return ""
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
A = len(get_indent(UpperCAmelCase ) ) > 0
if has_indent:
A = f"""class Bla:\n{code}"""
A = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=UpperCAmelCase )
A = black.format_str(UpperCAmelCase , mode=UpperCAmelCase )
A , A = style_docstrings_in_code(UpperCAmelCase )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def __a ( UpperCAmelCase , UpperCAmelCase=False ) ->List[str]:
"""simple docstring"""
with open(UpperCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
A = f.readlines()
A = []
A = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCAmelCase ):
A = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
A , A , A = search.groups()
A = find_code_in_diffusers(UpperCAmelCase )
A = get_indent(UpperCAmelCase )
A = line_index + 1 if indent == theoretical_indent else line_index + 2
A = theoretical_indent
A = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
A = True
while line_index < len(UpperCAmelCase ) and should_continue:
line_index += 1
if line_index >= len(UpperCAmelCase ):
break
A = lines[line_index]
A = _should_continue(UpperCAmelCase , UpperCAmelCase ) and re.search(f"""^{indent}# End copy""" , UpperCAmelCase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A = lines[start_index:line_index]
A = """""".join(UpperCAmelCase )
# Remove any nested `Copied from` comments to avoid circular copies
A = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(UpperCAmelCase ) is None]
A = """\n""".join(UpperCAmelCase )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCAmelCase ) > 0:
A = replace_pattern.replace("""with""" , """""" ).split(""",""" )
A = [_re_replace_pattern.search(UpperCAmelCase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
A , A , A = pattern.groups()
A = re.sub(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
if option.strip() == "all-casing":
A = re.sub(obja.lower() , obja.lower() , UpperCAmelCase )
A = re.sub(obja.upper() , obja.upper() , UpperCAmelCase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
A = blackify(lines[start_index - 1] + theoretical_code )
A = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
A = lines[:start_index] + [theoretical_code] + lines[line_index:]
A = start_index + 1
if overwrite and len(UpperCAmelCase ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(UpperCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(UpperCAmelCase )
return diffs
def __a ( UpperCAmelCase = False ) ->int:
"""simple docstring"""
A = glob.glob(os.path.join(UpperCAmelCase , """**/*.py""" ) , recursive=UpperCAmelCase )
A = []
for filename in all_files:
A = is_copy_consistent(UpperCAmelCase , UpperCAmelCase )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(UpperCAmelCase ) > 0:
A = """\n""".join(UpperCAmelCase )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
_lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_lowerCamelCase : Any = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 337 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
__lowerCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def A (self : str ):
A = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""pt""" )
# Using `do_sample=False` to force deterministic output
A = text_generator("""This is a test""" , do_sample=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
] , )
A = text_generator(["""This is a test""", """This is a second test"""] )
self.assertEqual(
_lowerCAmelCase , [
[
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
""" oscope. oscope. FiliFili@@"""
)
}
],
] , )
A = text_generator("""This is a test""" , do_sample=_lowerCAmelCase , num_return_sequences=2 , return_tensors=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
] , )
A = text_generator.model.config.eos_token_id
A = """<pad>"""
A = text_generator(
["""This is a test""", """This is a second test"""] , do_sample=_lowerCAmelCase , num_return_sequences=2 , batch_size=2 , return_tensors=_lowerCAmelCase , )
self.assertEqual(
_lowerCAmelCase , [
[
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
],
[
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
{"""generated_token_ids""": ANY(_lowerCAmelCase )},
],
] , )
@require_tf
def A (self : Optional[int] ):
A = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""tf""" )
# Using `do_sample=False` to force deterministic output
A = text_generator("""This is a test""" , do_sample=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
] , )
A = text_generator(["""This is a test""", """This is a second test"""] , do_sample=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
[
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
""" Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
)
}
],
] , )
def A (self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
A = TextGenerationPipeline(model=_lowerCAmelCase , tokenizer=_lowerCAmelCase )
return text_generator, ["This is a test", "Another test"]
def A (self : List[str] ):
A = """Hello I believe in"""
A = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
A = text_generator(_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}] , )
A = text_generator(_lowerCAmelCase , stop_sequence=""" fe""" )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": """Hello I believe in fe"""}] )
def A (self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] ):
A = text_generator.model
A = text_generator.tokenizer
A = text_generator("""This is a test""" )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": ANY(_lowerCAmelCase )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
A = text_generator("""This is a test""" , return_full_text=_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": ANY(_lowerCAmelCase )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
A = pipeline(task="""text-generation""" , model=_lowerCAmelCase , tokenizer=_lowerCAmelCase , return_full_text=_lowerCAmelCase )
A = text_generator("""This is a test""" )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": ANY(_lowerCAmelCase )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
A = text_generator("""This is a test""" , return_full_text=_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": ANY(_lowerCAmelCase )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
A = text_generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
[{"""generated_text""": ANY(_lowerCAmelCase )}, {"""generated_text""": ANY(_lowerCAmelCase )}],
[{"""generated_text""": ANY(_lowerCAmelCase )}, {"""generated_text""": ANY(_lowerCAmelCase )}],
] , )
if text_generator.tokenizer.pad_token is not None:
A = text_generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
[{"""generated_text""": ANY(_lowerCAmelCase )}, {"""generated_text""": ANY(_lowerCAmelCase )}],
[{"""generated_text""": ANY(_lowerCAmelCase )}, {"""generated_text""": ANY(_lowerCAmelCase )}],
] , )
with self.assertRaises(_lowerCAmelCase ):
A = text_generator("""test""" , return_full_text=_lowerCAmelCase , return_text=_lowerCAmelCase )
with self.assertRaises(_lowerCAmelCase ):
A = text_generator("""test""" , return_full_text=_lowerCAmelCase , return_tensors=_lowerCAmelCase )
with self.assertRaises(_lowerCAmelCase ):
A = text_generator("""test""" , return_text=_lowerCAmelCase , return_tensors=_lowerCAmelCase )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
A = text_generator("""""" )
self.assertEqual(_lowerCAmelCase , [{"""generated_text""": ANY(_lowerCAmelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
A = text_generator("""""" )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
A = ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator("""This is a test""" * 500 , max_new_tokens=20 )
A = text_generator("""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_lowerCAmelCase ):
text_generator(
"""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def A (self : Optional[Any] ):
import torch
# Classic `model_kwargs`
A = pipeline(
model="""hf-internal-testing/tiny-random-bloom""" , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
A = pipe("""This is a test""" )
self.assertEqual(
_lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
A = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
A = pipe("""This is a test""" )
self.assertEqual(
_lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
A = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
A = pipe("""This is a test""" )
self.assertEqual(
_lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
@require_torch
@require_torch_gpu
def A (self : Optional[int] ):
import torch
A = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device=0 , torch_dtype=torch.floataa )
pipe("""This is a test""" )
@require_torch
@require_accelerate
@require_torch_gpu
def A (self : Union[str, Any] ):
import torch
A = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.floataa )
pipe("""This is a test""" , do_sample=_lowerCAmelCase , top_p=0.5 )
def A (self : List[str] ):
A = """Hello world"""
A = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
if text_generator.model.framework == "tf":
A = logging.get_logger("""transformers.generation.tf_utils""" )
else:
A = logging.get_logger("""transformers.generation.utils""" )
A = """Both `max_new_tokens`""" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_lowerCAmelCase ) as cl:
A = text_generator(_lowerCAmelCase , max_length=10 , max_new_tokens=1 )
self.assertIn(_lowerCAmelCase , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_lowerCAmelCase ) as cl:
A = text_generator(_lowerCAmelCase , max_new_tokens=1 )
self.assertNotIn(_lowerCAmelCase , cl.out )
with CaptureLogger(_lowerCAmelCase ) as cl:
A = text_generator(_lowerCAmelCase , max_length=10 )
self.assertNotIn(_lowerCAmelCase , cl.out )
| 364 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
A = credit_card_number
A = 0
A = len(UpperCAmelCase ) - 2
for i in range(UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
A = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
A = cc_number[:i] + str(UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
A = f"""{credit_card_number} is an invalid credit card number because"""
if not credit_card_number.isdigit():
print(f"""{error_message} it has nonnumerical characters.""" )
return False
if not 13 <= len(UpperCAmelCase ) <= 16:
print(f"""{error_message} of its length.""" )
return False
if not validate_initial_digits(UpperCAmelCase ):
print(f"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(UpperCAmelCase ):
print(f"""{error_message} it fails the Luhn check.""" )
return False
print(f"""{credit_card_number} is a valid credit card number.""" )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('4111111111111111')
validate_credit_card_number('32323')
| 337 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def __a ( UpperCAmelCase = 10000 ) ->int:
"""simple docstring"""
A = []
for num in range(1 , UpperCAmelCase ):
A = 0
A = num
while iterations < 50:
A = sum_reverse(UpperCAmelCase )
iterations += 1
if is_palindrome(UpperCAmelCase ):
break
else:
lychrel_nums.append(UpperCAmelCase )
return len(UpperCAmelCase )
if __name__ == "__main__":
print(f"{solution() = }")
| 365 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : List[Any] ):
A = str(id_ )
A = None
A = None
A = []
A = {} # {vertex:distance}
def __lt__(self : List[Any] , _lowerCAmelCase : Tuple ):
return self.key < other.key
def __repr__(self : str ):
return self.id
def A (self : Union[str, Any] , _lowerCAmelCase : List[str] ):
self.neighbors.append(_lowerCAmelCase )
def A (self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] ):
A = weight
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , UpperCAmelCase )
graph[b - 1].add_edge(graph[a - 1] , UpperCAmelCase )
def __a ( UpperCAmelCase , UpperCAmelCase ) ->list:
"""simple docstring"""
A = []
for u in graph:
A = math.inf
A = None
A = 0
A = graph[:]
while q:
A = min(UpperCAmelCase )
q.remove(UpperCAmelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
A = u
A = u.edges[v.id]
for i in range(1 , len(UpperCAmelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Iterator[tuple]:
"""simple docstring"""
for u in graph:
A = math.inf
A = None
A = 0
A = list(UpperCAmelCase )
hq.heapify(UpperCAmelCase )
while h:
A = hq.heappop(UpperCAmelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
A = u
A = u.edges[v.id]
hq.heapify(UpperCAmelCase )
for i in range(1 , len(UpperCAmelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def __a ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __a ( ) ->Dict:
"""simple docstring"""
A = argparse.ArgumentParser()
parser.add_argument(
"""-m""" , """--pretrained_model_name_or_path""" , type=UpperCAmelCase , default=UpperCAmelCase , required=UpperCAmelCase , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , )
parser.add_argument(
"""-c""" , """--caption""" , type=UpperCAmelCase , default="""robotic cat with wings""" , help="""Text used to generate images.""" , )
parser.add_argument(
"""-n""" , """--images_num""" , type=UpperCAmelCase , default=4 , help="""How much images to generate.""" , )
parser.add_argument(
"""-s""" , """--seed""" , type=UpperCAmelCase , default=42 , help="""Seed for random process.""" , )
parser.add_argument(
"""-ci""" , """--cuda_id""" , type=UpperCAmelCase , default=0 , help="""cuda_id.""" , )
A = parser.parse_args()
return args
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
if not len(UpperCAmelCase ) == rows * cols:
raise ValueError("""The specified number of rows and columns are not correct.""" )
A , A = imgs[0].size
A = Image.new("""RGB""" , size=(cols * w, rows * h) )
A , A = grid.size
for i, img in enumerate(UpperCAmelCase ):
grid.paste(UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def __a ( UpperCAmelCase , UpperCAmelCase="robotic cat with wings" , UpperCAmelCase=7.5 , UpperCAmelCase=50 , UpperCAmelCase=1 , UpperCAmelCase=42 , ) ->Optional[int]:
"""simple docstring"""
A = torch.Generator(pipeline.device ).manual_seed(UpperCAmelCase )
A = pipeline(
UpperCAmelCase , guidance_scale=UpperCAmelCase , num_inference_steps=UpperCAmelCase , generator=UpperCAmelCase , num_images_per_prompt=UpperCAmelCase , ).images
A = int(math.sqrt(UpperCAmelCase ) )
A = image_grid(UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
_lowerCamelCase : str = parse_args()
# Load models and create wrapper for stable diffusion
_lowerCamelCase : Any = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
_lowerCamelCase : Dict = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
_lowerCamelCase : Union[str, Any] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
_lowerCamelCase : Union[str, Any] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
_lowerCamelCase : int = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowerCamelCase : Any = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
_lowerCamelCase : Optional[int] = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
_lowerCamelCase : str = unet.to(torch.device('cuda', args.cuda_id))
_lowerCamelCase : Union[str, Any] = pipeline.to(unet.device)
_lowerCamelCase : Tuple = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
_lowerCamelCase : Union[str, Any] = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 366 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = '''perceiver'''
def __init__(self : Dict , _lowerCAmelCase : List[str]=256 , _lowerCAmelCase : Any=1280 , _lowerCAmelCase : Dict=768 , _lowerCAmelCase : List[str]=1 , _lowerCAmelCase : Optional[int]=26 , _lowerCAmelCase : Any=8 , _lowerCAmelCase : Any=8 , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : List[Any]="kv" , _lowerCAmelCase : Optional[Any]=1 , _lowerCAmelCase : int=1 , _lowerCAmelCase : Dict="gelu" , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Any=1e-12 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=262 , _lowerCAmelCase : int=2048 , _lowerCAmelCase : int=56 , _lowerCAmelCase : List[Any]=[368, 496] , _lowerCAmelCase : List[Any]=16 , _lowerCAmelCase : Any=1920 , _lowerCAmelCase : Optional[int]=16 , _lowerCAmelCase : List[Any]=[1, 16, 224, 224] , **_lowerCAmelCase : Union[str, Any] , ):
super().__init__(**_lowerCAmelCase )
A = num_latents
A = d_latents
A = d_model
A = num_blocks
A = num_self_attends_per_block
A = num_self_attention_heads
A = num_cross_attention_heads
A = qk_channels
A = v_channels
A = cross_attention_shape_for_attention
A = self_attention_widening_factor
A = cross_attention_widening_factor
A = hidden_act
A = attention_probs_dropout_prob
A = initializer_range
A = layer_norm_eps
A = use_query_residual
# masked language modeling attributes
A = vocab_size
A = max_position_embeddings
# image classification attributes
A = image_size
# flow attributes
A = train_size
# multimodal autoencoding attributes
A = num_frames
A = audio_samples_per_frame
A = samples_per_patch
A = output_shape
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
@property
def A (self : List[str] ):
if self.task == "multiple-choice":
A = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""inputs""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
@property
def A (self : Dict ):
return 1e-4
def A (self : List[Any] , _lowerCAmelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _lowerCAmelCase : int = -1 , _lowerCAmelCase : int = -1 , _lowerCAmelCase : int = -1 , _lowerCAmelCase : bool = False , _lowerCAmelCase : Optional[TensorType] = None , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 40 , _lowerCAmelCase : int = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
A = compute_effective_axis_dimension(
_lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
A = preprocessor.num_special_tokens_to_add(_lowerCAmelCase )
A = compute_effective_axis_dimension(
_lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowerCAmelCase )
# Generate dummy inputs according to compute batch and sequence
A = [""" """.join(["""a"""] ) * seq_length] * batch_size
A = dict(preprocessor(_lowerCAmelCase , return_tensors=_lowerCAmelCase ) )
A = inputs.pop("""input_ids""" )
return inputs
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
A = compute_effective_axis_dimension(_lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch )
A = self._generate_dummy_images(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
A = dict(preprocessor(images=_lowerCAmelCase , return_tensors=_lowerCAmelCase ) )
A = inputs.pop("""pixel_values""" )
return inputs
else:
raise ValueError(
"""Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""" )
| 337 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase = 4000000 ) ->int:
"""simple docstring"""
A = []
A , A = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(UpperCAmelCase )
A , A = b, a + b
return sum(UpperCAmelCase )
if __name__ == "__main__":
print(f"{solution() = }")
| 367 |
'''simple docstring'''
import math
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : int , _lowerCAmelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
A = n
A = [
[math.inf for j in range(0 , _lowerCAmelCase )] for i in range(0 , _lowerCAmelCase )
] # adjacency matrix for weight
A = [
[math.inf for j in range(0 , _lowerCAmelCase )] for i in range(0 , _lowerCAmelCase )
] # dp[i][j] stores minimum distance from i to j
def A (self : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
A = w
def A (self : Union[str, Any] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
A = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def A (self : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCamelCase : str = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__(self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : str=7 , _lowerCAmelCase : Optional[Any]=3 , _lowerCAmelCase : Union[str, Any]=18 , _lowerCAmelCase : Optional[Any]=30 , _lowerCAmelCase : List[str]=400 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Any=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=[0.5, 0.5, 0.5] , _lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , ):
A = size if size is not None else {"""height""": 18, """width""": 18}
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size
A = do_normalize
A = image_mean
A = image_std
def A (self : Any ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __UpperCAmelCase ( A__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = DPTImageProcessor if is_vision_available() else None
def A (self : List[str] ):
A = DPTImageProcessingTester(self )
@property
def A (self : List[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def A (self : List[Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """image_mean""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """image_std""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """size""" ) )
def A (self : List[str] ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def A (self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
A = image_processing(_lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def A (self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , numpify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , np.ndarray )
# Test not batched input
A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
A = image_processing(_lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def A (self : Optional[int] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , torchify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , torch.Tensor )
# Test not batched input
A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
A = image_processing(_lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
| 368 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : int = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
_lowerCamelCase : List[str] = {
'vocab_file': {
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
},
'merges_file': {
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
},
'tokenizer_file': {
'Salesforce/codegen-350M-mono': (
'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
),
},
}
_lowerCamelCase : List[str] = {
'Salesforce/codegen-350M-mono': 2048,
}
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ['''input_ids''', '''attention_mask''']
__lowerCAmelCase = CodeGenTokenizer
def __init__(self : int , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Optional[Any]="<|endoftext|>" , _lowerCAmelCase : Dict="<|endoftext|>" , _lowerCAmelCase : Dict="<|endoftext|>" , _lowerCAmelCase : Any=False , **_lowerCAmelCase : Optional[int] , ):
super().__init__(
_lowerCAmelCase , _lowerCAmelCase , tokenizer_file=_lowerCAmelCase , unk_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , add_prefix_space=_lowerCAmelCase , **_lowerCAmelCase , )
if kwargs.pop("""add_bos_token""" , _lowerCAmelCase ):
A = kwargs.pop("""name_or_path""" , """""" )
raise ValueError(
"""Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
"""Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
"""This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
""" so that the fast tokenizer works correctly.""" )
A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , _lowerCAmelCase ) != add_prefix_space:
A = getattr(_lowerCAmelCase , pre_tok_state.pop("""type""" ) )
A = add_prefix_space
A = pre_tok_class(**_lowerCAmelCase )
A = add_prefix_space
def A (self : int , *_lowerCAmelCase : int , **_lowerCAmelCase : List[Any] ):
A = kwargs.get("""is_split_into_words""" , _lowerCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_lowerCAmelCase , **_lowerCAmelCase )
def A (self : Dict , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[Any] ):
A = kwargs.get("""is_split_into_words""" , _lowerCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_lowerCAmelCase , **_lowerCAmelCase )
def A (self : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
def A (self : Tuple , _lowerCAmelCase : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = None , _lowerCAmelCase : Optional[List[str]] = None , **_lowerCAmelCase : Tuple , ):
A = super().decode(
token_ids=_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase , **_lowerCAmelCase , )
if truncate_before_pattern is not None and len(_lowerCAmelCase ) > 0:
A = self.truncate(_lowerCAmelCase , _lowerCAmelCase )
return decoded_text
def A (self : List[str] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
def find_re(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ):
A = pattern.search(_lowerCAmelCase , _lowerCAmelCase )
return m.start() if m else -1
A = [re.compile(_lowerCAmelCase , re.MULTILINE ) for pattern in truncate_before_pattern]
A = list(re.finditer("""^print""" , _lowerCAmelCase , re.MULTILINE ) )
if len(_lowerCAmelCase ) > 1:
A = completion[: prints[1].start()]
A = list(re.finditer("""^def""" , _lowerCAmelCase , re.MULTILINE ) )
if len(_lowerCAmelCase ) > 1:
A = completion[: defs[1].start()]
A = 0
A = [
pos for pos in [find_re(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for terminal in terminals] if pos != -1
]
if len(_lowerCAmelCase ) > 0:
return completion[: min(_lowerCAmelCase )]
else:
return completion
| 337 | 0 |
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
while a != 0:
A , A = b % a, a
return b
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
if gcd(UpperCAmelCase , UpperCAmelCase ) != 1:
A = f"""mod inverse of {a!r} and {m!r} does not exist"""
raise ValueError(UpperCAmelCase )
A , A , A = 1, 0, a
A , A , A = 0, 1, m
while va != 0:
A = ua // va
A , A , A , A , A , A = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 369 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Optional[Any] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[str] = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
_lowerCamelCase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 337 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''image_processor''']
__lowerCAmelCase = '''SamImageProcessor'''
def __init__(self : Optional[Any] , _lowerCAmelCase : Dict ):
super().__init__(_lowerCAmelCase )
A = self.image_processor
A = -10
A = self.image_processor.size["""longest_edge"""]
def __call__(self : Optional[Any] , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , **_lowerCAmelCase : Union[str, Any] , ):
A = self.image_processor(
_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase , )
# pop arguments that are not used in the foward but used nevertheless
A = encoding_image_processor["""original_sizes"""]
if hasattr(_lowerCAmelCase , """numpy""" ): # Checks if Torch or TF tensor
A = original_sizes.numpy()
A , A , A = self._check_and_preprocess_points(
input_points=_lowerCAmelCase , input_labels=_lowerCAmelCase , input_boxes=_lowerCAmelCase , )
A = self._normalize_and_convert(
_lowerCAmelCase , _lowerCAmelCase , input_points=_lowerCAmelCase , input_labels=_lowerCAmelCase , input_boxes=_lowerCAmelCase , return_tensors=_lowerCAmelCase , )
return encoding_image_processor
def A (self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : List[str]="pt" , ):
if input_points is not None:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , original_sizes[0] ) for point in input_points
]
else:
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , _lowerCAmelCase )
for point, original_size in zip(_lowerCAmelCase , _lowerCAmelCase )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
A , A = self._pad_points_and_labels(_lowerCAmelCase , _lowerCAmelCase )
A = np.array(_lowerCAmelCase )
if input_labels is not None:
A = np.array(_lowerCAmelCase )
if input_boxes is not None:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , original_sizes[0] , is_bounding_box=_lowerCAmelCase )
for box in input_boxes
]
else:
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , _lowerCAmelCase , is_bounding_box=_lowerCAmelCase )
for box, original_size in zip(_lowerCAmelCase , _lowerCAmelCase )
]
A = np.array(_lowerCAmelCase )
if input_boxes is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# boxes batch size of 1 by default
A = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# boxes batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# point batch size of 1 by default
A = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# point batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# point batch size of 1 by default
A = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# point batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def A (self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ):
A = max([point.shape[0] for point in input_points] )
A = []
for i, point in enumerate(_lowerCAmelCase ):
if point.shape[0] != expected_nb_points:
A = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
A = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(_lowerCAmelCase )
A = processed_input_points
return input_points, input_labels
def A (self : Dict , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict=False ):
A , A = original_size
A , A = self.image_processor._get_preprocess_shape(_lowerCAmelCase , longest_edge=_lowerCAmelCase )
A = deepcopy(_lowerCAmelCase ).astype(_lowerCAmelCase )
if is_bounding_box:
A = coords.reshape(-1 , 2 , 2 )
A = coords[..., 0] * (new_w / old_w)
A = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
A = coords.reshape(-1 , 4 )
return coords
def A (self : Any , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Dict=None , ):
if input_points is not None:
if hasattr(_lowerCAmelCase , """numpy""" ): # Checks for TF or Torch tensor
A = input_points.numpy().tolist()
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not isinstance(input_points[0] , _lowerCAmelCase ):
raise ValueError("""Input points must be a list of list of floating points.""" )
A = [np.array(_lowerCAmelCase ) for input_point in input_points]
else:
A = None
if input_labels is not None:
if hasattr(_lowerCAmelCase , """numpy""" ):
A = input_labels.numpy().tolist()
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not isinstance(input_labels[0] , _lowerCAmelCase ):
raise ValueError("""Input labels must be a list of list integers.""" )
A = [np.array(_lowerCAmelCase ) for label in input_labels]
else:
A = None
if input_boxes is not None:
if hasattr(_lowerCAmelCase , """numpy""" ):
A = input_boxes.numpy().tolist()
if (
not isinstance(_lowerCAmelCase , _lowerCAmelCase )
or not isinstance(input_boxes[0] , _lowerCAmelCase )
or not isinstance(input_boxes[0][0] , _lowerCAmelCase )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
A = [np.array(_lowerCAmelCase ).astype(np.floataa ) for box in input_boxes]
else:
A = None
return input_points, input_labels, input_boxes
@property
def A (self : Tuple ):
A = self.image_processor.model_input_names
return list(dict.fromkeys(_lowerCAmelCase ) )
def A (self : Tuple , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Any ):
return self.image_processor.post_process_masks(*_lowerCAmelCase , **_lowerCAmelCase )
| 370 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : Optional[Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F"""Accelerated optimizer pickling failed with {e}""" )
AcceleratorState._reset_state()
| 337 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : Union[str, Any] ):
super().__init__()
A = nn.Linear(3 , 4 )
A = nn.BatchNormad(4 )
A = nn.Linear(4 , 5 )
def A (self : Dict , _lowerCAmelCase : str ):
return self.lineara(self.batchnorm(self.lineara(_lowerCAmelCase ) ) )
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
def A (self : Union[str, Any] , _lowerCAmelCase : List[str] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : List[Any] ):
return (args[0] + 1,) + args[1:], kwargs
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
def A (self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] ):
return output + 1
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : Union[str, Any] ):
A = ModelForTest()
A = ModelHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
self.assertEqual(test_model._hf_hook , _lowerCAmelCase )
self.assertTrue(hasattr(_lowerCAmelCase , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(_lowerCAmelCase )
self.assertFalse(hasattr(_lowerCAmelCase , """_hf_hook""" ) )
self.assertFalse(hasattr(_lowerCAmelCase , """_old_forward""" ) )
def A (self : List[Any] ):
A = ModelForTest()
A = ModelHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase , append=_lowerCAmelCase )
self.assertEqual(isinstance(test_model._hf_hook , _lowerCAmelCase ) , _lowerCAmelCase )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(_lowerCAmelCase , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(_lowerCAmelCase )
self.assertFalse(hasattr(_lowerCAmelCase , """_hf_hook""" ) )
self.assertFalse(hasattr(_lowerCAmelCase , """_old_forward""" ) )
def A (self : List[Any] ):
A = ModelForTest()
A = torch.randn(2 , 3 )
A = test_model(x + 1 )
A = test_model(x + 2 )
A = PreForwardHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
A = PreForwardHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
A = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-5 )
def A (self : List[str] ):
A = ModelForTest()
A = torch.randn(2 , 3 )
A = test_model(_lowerCAmelCase )
A = PostForwardHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase , output + 1 , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
A = PostForwardHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase , output + 1 , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
A = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
assert torch.allclose(_lowerCAmelCase , output + 2 , atol=1e-5 )
def A (self : Optional[int] ):
A = ModelForTest()
A = torch.randn(2 , 3 )
A = test_model(_lowerCAmelCase )
A = PostForwardHook()
add_hook_to_module(_lowerCAmelCase , _lowerCAmelCase )
A = test_model(_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase , output + 1 ) )
self.assertTrue(outputa.requires_grad )
A = True
A = test_model(_lowerCAmelCase )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A (self : List[str] ):
A = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(_lowerCAmelCase , AlignDevicesHook(io_same_device=_lowerCAmelCase ) )
A = torch.randn(2 , 3 ).to(0 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , torch.device(0 ) )
def A (self : Union[str, Any] ):
A = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
A = {"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**_lowerCAmelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**_lowerCAmelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**_lowerCAmelCase ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
A = torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , _lowerCAmelCase )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
A = {
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**_lowerCAmelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**_lowerCAmelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**_lowerCAmelCase ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A (self : int ):
A = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
A = 0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(_lowerCAmelCase , execution_device=_lowerCAmelCase , offload=_lowerCAmelCase )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
A = torch.device(_lowerCAmelCase )
self.assertEqual(model.batchnorm.running_mean.device , _lowerCAmelCase )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(_lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(_lowerCAmelCase , execution_device=_lowerCAmelCase , offload=_lowerCAmelCase , offload_buffers=_lowerCAmelCase )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(_lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A (self : List[Any] ):
A = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
A = 0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
_lowerCAmelCase , execution_device=_lowerCAmelCase , offload=_lowerCAmelCase , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
A = torch.device(_lowerCAmelCase )
self.assertEqual(model.batchnorm.running_mean.device , _lowerCAmelCase )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(_lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
_lowerCAmelCase , execution_device=_lowerCAmelCase , offload=_lowerCAmelCase , weights_map=model.state_dict() , offload_buffers=_lowerCAmelCase , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
A = torch.randn(2 , 3 )
A = model(_lowerCAmelCase )
self.assertEqual(output.device , _lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(_lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 371 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Tuple , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Dict ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[int] , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : str ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : int ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[Any] , *_lowerCAmelCase : str , **_lowerCAmelCase : str ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[str] , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : List[Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Union[str, Any] , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : int ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Any , *_lowerCAmelCase : str , **_lowerCAmelCase : Union[str, Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[Any] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Union[str, Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Any ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[int] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Dict ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Union[str, Any] , *_lowerCAmelCase : str , **_lowerCAmelCase : List[str] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Union[str, Any] , *_lowerCAmelCase : Any , **_lowerCAmelCase : str ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[Any] , *_lowerCAmelCase : int , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Dict , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : int ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Dict , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[int] ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Dict , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Tuple ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
| 337 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
if len(_lowercase ) == 0:
return False
UpperCAmelCase : List[Any] = len(_lowercase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , _lowercase )
else:
return binary_search(a_list[midpoint + 1 :] , _lowercase )
if __name__ == "__main__":
a : List[str] = input("""Enter numbers separated by comma:\n""").strip()
a : Dict = [int(item.strip()) for item in user_input.split(""",""")]
a : List[str] = int(input("""Enter the number to be found in the list:\n""").strip())
a : Dict = """""" if binary_search(sequence, target) else """not """
print(F'''{target} was {not_str}found in {sequence}''')
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = False , _lowercase = 1_0_0 , _lowercase = 0.01 , _lowercase = 1 , ) -> Any:
UpperCAmelCase : Any = False
UpperCAmelCase : Optional[int] = search_prob
UpperCAmelCase : int = start_temperate
UpperCAmelCase : Optional[Any] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : str = None
while not search_end:
UpperCAmelCase : int = current_state.score()
if best_state is None or current_score > best_state.score():
UpperCAmelCase : Optional[Any] = current_state
scores.append(_lowercase )
iterations += 1
UpperCAmelCase : Optional[Any] = None
UpperCAmelCase : Optional[Any] = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
UpperCAmelCase : str = random.randint(0 , len(_lowercase ) - 1 ) # picking a random neighbor
UpperCAmelCase : Optional[int] = neighbors.pop(_lowercase )
UpperCAmelCase : Optional[Any] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
UpperCAmelCase : List[str] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
UpperCAmelCase : int = picked_neighbor
else:
UpperCAmelCase : Dict = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
UpperCAmelCase : List[str] = picked_neighbor
UpperCAmelCase : Any = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
UpperCAmelCase : int = True
else:
UpperCAmelCase : Dict = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_lowercase ) , _lowercase )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
a : Optional[int] = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
a : Optional[int] = simulated_annealing(
prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
# starting the problem with initial coordinates (12, 47)
a : List[str] = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
a : int = simulated_annealing(
prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
def __lowerCamelCase ( _lowercase , _lowercase ) -> Any:
return (3 * x**2) - (6 * y)
a : str = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
a : Dict = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F'''{local_min.score()}'''
)
a : Any = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
a : Optional[Any] = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F'''{local_min.score()}'''
)
| 338 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
a : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
a : int = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A , A , ) -> Union[str, Any]:
super().__init__()
self.register_modules(
prior=A , image_encoder=A , image_processor=A , scheduler=A , renderer=A , )
def _lowercase( self , A , A , A , A , A , A ) -> Any:
if latents is None:
UpperCAmelCase : Optional[Any] = randn_tensor(A , generator=A , device=A , dtype=A )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
UpperCAmelCase : Union[str, Any] = latents.to(A )
UpperCAmelCase : Any = latents * scheduler.init_noise_sigma
return latents
def _lowercase( self , A=0 ) -> Optional[int]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
UpperCAmelCase : Union[str, Any] = torch.device(f'''cuda:{gpu_id}''' )
UpperCAmelCase : Dict = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A , A )
@property
def _lowercase( self ) -> str:
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(A , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def _lowercase( self , A , A , A , A , ) -> Optional[Any]:
if isinstance(A , A ) and isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : Union[str, Any] = torch.cat(A , axis=0 ) if image[0].ndim == 4 else torch.stack(A , axis=0 )
if not isinstance(A , torch.Tensor ):
UpperCAmelCase : Optional[Any] = self.image_processor(A , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
UpperCAmelCase : List[Any] = image.to(dtype=self.image_encoder.dtype , device=A )
UpperCAmelCase : Optional[int] = self.image_encoder(A )["""last_hidden_state"""]
UpperCAmelCase : Tuple = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
UpperCAmelCase : Tuple = image_embeds.repeat_interleave(A , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase : Optional[int] = torch.zeros_like(A )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : Any = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(A )
def __call__( self , A , A = 1 , A = 25 , A = None , A = None , A = 4.0 , A = 64 , A = "pil" , A = True , ) -> str:
if isinstance(A , PIL.Image.Image ):
UpperCAmelCase : Union[str, Any] = 1
elif isinstance(A , torch.Tensor ):
UpperCAmelCase : Any = image.shape[0]
elif isinstance(A , A ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
UpperCAmelCase : Any = len(A )
else:
raise ValueError(
f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(A )}''' )
UpperCAmelCase : Tuple = self._execution_device
UpperCAmelCase : str = batch_size * num_images_per_prompt
UpperCAmelCase : Optional[int] = guidance_scale > 1.0
UpperCAmelCase : int = self._encode_image(A , A , A , A )
# prior
self.scheduler.set_timesteps(A , device=A )
UpperCAmelCase : Union[str, Any] = self.scheduler.timesteps
UpperCAmelCase : Optional[Any] = self.prior.config.num_embeddings
UpperCAmelCase : int = self.prior.config.embedding_dim
UpperCAmelCase : Optional[Any] = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , A , A , A , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
UpperCAmelCase : Dict = latents.reshape(latents.shape[0] , A , A )
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase : int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase : str = self.scheduler.scale_model_input(A , A )
UpperCAmelCase : int = self.prior(
A , timestep=A , proj_embedding=A , ).predicted_image_embedding
# remove the variance
UpperCAmelCase , UpperCAmelCase : Optional[Any] = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
UpperCAmelCase , UpperCAmelCase : Dict = noise_pred.chunk(2 )
UpperCAmelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
UpperCAmelCase : Tuple = self.scheduler.step(
A , timestep=A , sample=A , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=A )
UpperCAmelCase : Union[str, Any] = []
for i, latent in enumerate(A ):
print()
UpperCAmelCase : List[str] = self.renderer.decode(
latent[None, :] , A , size=A , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(A )
UpperCAmelCase : Any = torch.stack(A )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
UpperCAmelCase : Dict = images.cpu().numpy()
if output_type == "pil":
UpperCAmelCase : Optional[int] = [self.numpy_to_pil(A ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=A )
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 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.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = 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 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """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}''')
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> list[int]:
return [ord(_lowercase ) - 9_6 for elem in plain]
def __lowerCamelCase ( _lowercase ) -> str:
return "".join(chr(elem + 9_6 ) for elem in encoded )
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : int = encode(input("""-> """ ).strip().lower() )
print("""Encoded: """ , _lowercase )
print("""Decoded:""" , decode(_lowercase ) )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if len(_lowercase ) < k or k < 0:
raise ValueError("""Invalid Input""" )
UpperCAmelCase : Optional[Any] = sum(array[:k] )
for i in range(len(_lowercase ) - k ):
UpperCAmelCase : Union[str, Any] = current_sum - array[i] + array[i + k]
UpperCAmelCase : Optional[int] = max(_lowercase , _lowercase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
a : List[Any] = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0_0)]
a : Optional[int] = randint(0, 1_1_0)
print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')
| 338 |
'''simple docstring'''
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 UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[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(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, 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(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[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(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : 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(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) 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 _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
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(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = 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
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = 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 _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
UpperCAmelCase : Any = {}
UpperCAmelCase : Optional[int] = tokenizer(example["""content"""] , truncation=_lowercase )["""input_ids"""]
UpperCAmelCase : Optional[int] = len(example["""content"""] ) / len(output["""input_ids"""] )
return output
a : List[Any] = HfArgumentParser(PretokenizationArguments)
a : Any = parser.parse_args()
if args.num_workers is None:
a : List[str] = multiprocessing.cpu_count()
a : int = AutoTokenizer.from_pretrained(args.tokenizer_dir)
a : Optional[int] = time.time()
a : int = load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')
a : Optional[Any] = time.time()
a : Union[str, Any] = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"""repo_name""",
"""path""",
"""copies""",
"""size""",
"""content""",
"""license""",
"""hash""",
"""line_mean""",
"""line_max""",
"""alpha_frac""",
"""autogenerated""",
],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')
a : str = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 338 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
assert column_title.isupper()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Tuple = len(_lowercase ) - 1
UpperCAmelCase : Tuple = 0
while index >= 0:
UpperCAmelCase : Optional[int] = (ord(column_title[index] ) - 6_4) * pow(2_6 , _lowercase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 1 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
lowercase = None
def __lowerCamelCase ( _lowercase , _lowercase=0.999 , _lowercase="cosine" , ) -> str:
if alpha_transform_type == "cosine":
def alpha_bar_fn(_lowercase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_lowercase ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
UpperCAmelCase : Dict = []
for i in range(_lowercase ):
UpperCAmelCase : Union[str, Any] = i / num_diffusion_timesteps
UpperCAmelCase : Optional[int] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_lowercase ) / alpha_bar_fn(_lowercase ) , _lowercase ) )
return torch.tensor(_lowercase , dtype=torch.floataa )
class UpperCamelCase_ ( __magic_name__ , __magic_name__ ):
@register_to_config
def __init__( self , A = 1000 , A = "fixed_small_log" , A = True , A = 1.0 , A = "epsilon" , A = "squaredcos_cap_v2" , ) -> Any:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
UpperCAmelCase : Tuple = betas_for_alpha_bar(A )
UpperCAmelCase : Tuple = 1.0 - self.betas
UpperCAmelCase : Optional[int] = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase : Dict = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase : str = 1.0
# setable values
UpperCAmelCase : Any = None
UpperCAmelCase : Any = torch.from_numpy(np.arange(0 , A )[::-1].copy() )
UpperCAmelCase : Union[str, Any] = variance_type
def _lowercase( self , A , A = None ) -> torch.FloatTensor:
return sample
def _lowercase( self , A , A = None ) -> Optional[Any]:
UpperCAmelCase : List[Any] = num_inference_steps
UpperCAmelCase : Dict = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase : int = (np.arange(0 , A ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase : Union[str, Any] = torch.from_numpy(A ).to(A )
def _lowercase( self , A , A=None , A=None , A=None ) -> int:
if prev_timestep is None:
UpperCAmelCase : int = t - 1
UpperCAmelCase : int = self.alphas_cumprod[t]
UpperCAmelCase : List[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Dict = 1 - alpha_prod_t
UpperCAmelCase : Union[str, Any] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : Optional[Any] = self.betas[t]
else:
UpperCAmelCase : Optional[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase : str = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase : List[str] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase : Dict = torch.log(torch.clamp(A , min=1e-20 ) )
UpperCAmelCase : List[str] = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase : Union[str, Any] = variance.log()
UpperCAmelCase : Optional[int] = beta.log()
UpperCAmelCase : Optional[Any] = (predicted_variance + 1) / 2
UpperCAmelCase : Dict = frac * max_log + (1 - frac) * min_log
return variance
def _lowercase( self , A , A , A , A = None , A=None , A = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
UpperCAmelCase : Optional[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase , UpperCAmelCase : Any = torch.split(A , sample.shape[1] , dim=1 )
else:
UpperCAmelCase : Optional[int] = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase : Dict = t - 1
UpperCAmelCase : List[Any] = self.alphas_cumprod[t]
UpperCAmelCase : Union[str, Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : int = 1 - alpha_prod_t
UpperCAmelCase : Optional[int] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : Tuple = self.betas[t]
UpperCAmelCase : Union[str, Any] = self.alphas[t]
else:
UpperCAmelCase : Tuple = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase : int = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase : List[str] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase : Dict = model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase : Union[str, Any] = torch.clamp(
A , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : str = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase : Optional[Any] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Optional[Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase : List[str] = 0
if t > 0:
UpperCAmelCase : Optional[Any] = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=A , device=model_output.device )
UpperCAmelCase : Optional[Any] = self._get_variance(
A , predicted_variance=A , prev_timestep=A , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase : Union[str, Any] = variance
elif self.variance_type == "learned_range":
UpperCAmelCase : Union[str, Any] = (0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
""" for the UnCLIPScheduler.""" )
UpperCAmelCase : Dict = variance * variance_noise
UpperCAmelCase : List[Any] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=A , pred_original_sample=A )
def _lowercase( self , A , A , A , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
UpperCAmelCase : Tuple = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase : Optional[Any] = timesteps.to(original_samples.device )
UpperCAmelCase : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase : int = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : Tuple = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : str = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase : List[Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : Dict = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Union[str, Any] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_lowercase , _lowercase ) ) )
def __lowerCamelCase ( _lowercase , _lowercase ) -> list[list[list[float] | float]]:
if dataset.ndim != value_array.ndim:
UpperCAmelCase : Optional[int] = (
"""Wrong input data's dimensions... """
F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(_lowercase )
try:
if dataset.shape[1] != value_array.shape[1]:
UpperCAmelCase : Optional[Any] = (
"""Wrong input data's shape... """
F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(_lowercase )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError("""Wrong shape""" )
if dataset.dtype != value_array.dtype:
UpperCAmelCase : Optional[int] = (
"""Input data have different datatype... """
F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(_lowercase )
UpperCAmelCase : Union[str, Any] = []
for value in value_array:
UpperCAmelCase : List[str] = euclidean(_lowercase , dataset[0] )
UpperCAmelCase : List[str] = dataset[0].tolist()
for dataset_value in dataset[1:]:
UpperCAmelCase : Tuple = euclidean(_lowercase , _lowercase )
if dist > temp_dist:
UpperCAmelCase : str = temp_dist
UpperCAmelCase : List[Any] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
return np.dot(_lowercase , _lowercase ) / (norm(_lowercase ) * norm(_lowercase ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 1 |
'''simple docstring'''
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 UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[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(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, 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(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[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(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : 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(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) 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 _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
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(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = 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
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = 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 _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : 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 UpperCamelCase_ ( __magic_name__ ):
lowercase = 'decision_transformer'
lowercase = ['past_key_values']
lowercase = {
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , A=17 , A=4 , A=128 , A=4096 , A=True , A=1 , A=1024 , A=3 , A=1 , A=None , A="relu" , A=0.1 , A=0.1 , A=0.1 , A=1e-5 , A=0.0_2 , A=True , A=True , A=50256 , A=50256 , A=False , A=False , **A , ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = state_dim
UpperCAmelCase : Optional[int] = act_dim
UpperCAmelCase : Optional[Any] = hidden_size
UpperCAmelCase : List[str] = max_ep_len
UpperCAmelCase : Optional[Any] = action_tanh
UpperCAmelCase : List[str] = vocab_size
UpperCAmelCase : Union[str, Any] = n_positions
UpperCAmelCase : List[str] = n_layer
UpperCAmelCase : str = n_head
UpperCAmelCase : Any = n_inner
UpperCAmelCase : Optional[int] = activation_function
UpperCAmelCase : Tuple = resid_pdrop
UpperCAmelCase : List[str] = embd_pdrop
UpperCAmelCase : Dict = attn_pdrop
UpperCAmelCase : List[Any] = layer_norm_epsilon
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scale_attn_weights
UpperCAmelCase : int = use_cache
UpperCAmelCase : Any = scale_attn_by_inverse_layer_idx
UpperCAmelCase : List[str] = reorder_and_upcast_attn
UpperCAmelCase : List[Any] = bos_token_id
UpperCAmelCase : str = eos_token_id
super().__init__(bos_token_id=A , eos_token_id=A , **A )
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
a : Union[str, Any] = threading.Lock()
a : Optional[logging.Handler] = None
a : List[str] = {
"""debug""": logging.DEBUG,
"""info""": logging.INFO,
"""warning""": logging.WARNING,
"""error""": logging.ERROR,
"""critical""": logging.CRITICAL,
}
a : Any = logging.WARNING
a : str = True
def __lowerCamelCase ( ) -> Dict:
UpperCAmelCase : Optional[Any] = os.getenv("""TRANSFORMERS_VERBOSITY""" , _lowercase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'''Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '''
F'''has to be one of: { ', '.join(log_levels.keys() ) }''' )
return _default_log_level
def __lowerCamelCase ( ) -> str:
return __name__.split(""".""" )[0]
def __lowerCamelCase ( ) -> logging.Logger:
return logging.getLogger(_get_library_name() )
def __lowerCamelCase ( ) -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
UpperCAmelCase : Tuple = logging.StreamHandler() # Set sys.stderr as stream.
UpperCAmelCase : Any = sys.stderr.flush
# Apply our default configuration to the library root logger.
UpperCAmelCase : int = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
UpperCAmelCase : Union[str, Any] = False
def __lowerCamelCase ( ) -> None:
global _default_handler
with _lock:
if not _default_handler:
return
UpperCAmelCase : List[str] = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
UpperCAmelCase : int = None
def __lowerCamelCase ( ) -> Union[str, Any]:
return log_levels
def __lowerCamelCase ( _lowercase = None ) -> logging.Logger:
if name is None:
UpperCAmelCase : List[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(_lowercase )
def __lowerCamelCase ( ) -> int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def __lowerCamelCase ( _lowercase ) -> None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(_lowercase )
def __lowerCamelCase ( ) -> Any:
return set_verbosity(_lowercase )
def __lowerCamelCase ( ) -> Optional[int]:
return set_verbosity(_lowercase )
def __lowerCamelCase ( ) -> int:
return set_verbosity(_lowercase )
def __lowerCamelCase ( ) -> List[str]:
return set_verbosity(_lowercase )
def __lowerCamelCase ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def __lowerCamelCase ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def __lowerCamelCase ( _lowercase ) -> None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(_lowercase )
def __lowerCamelCase ( _lowercase ) -> None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(_lowercase )
def __lowerCamelCase ( ) -> None:
_configure_library_root_logger()
UpperCAmelCase : int = False
def __lowerCamelCase ( ) -> None:
_configure_library_root_logger()
UpperCAmelCase : Optional[int] = True
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : str = _get_library_root_logger().handlers
for handler in handlers:
UpperCAmelCase : List[str] = logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s""" )
handler.setFormatter(_lowercase )
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Dict = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(_lowercase )
def __lowerCamelCase ( self , *_lowercase , **_lowercase ) -> Tuple:
UpperCAmelCase : Optional[Any] = os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS""" , _lowercase )
if no_advisory_warnings:
return
self.warning(*_lowercase , **_lowercase )
a : List[Any] = warning_advice
@functools.lru_cache(_lowercase )
def __lowerCamelCase ( self , *_lowercase , **_lowercase ) -> int:
self.warning(*_lowercase , **_lowercase )
a : Union[str, Any] = warning_once
class UpperCamelCase_ :
def __init__( self , *A , **A ) -> Optional[Any]: # pylint: disable=unused-argument
UpperCAmelCase : Any = args[0] if args else None
def __iter__( self ) -> int:
return iter(self._iterator )
def __getattr__( self , A ) -> Optional[Any]:
def empty_fn(*A , **A ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> List[Any]:
return self
def __exit__( self , A , A , A ) -> Any:
return
class UpperCamelCase_ :
def __call__( self , *A , **A ) -> List[Any]:
if _tqdm_active:
return tqdm_lib.tqdm(*A , **A )
else:
return EmptyTqdm(*A , **A )
def _lowercase( self , *A , **A ) -> Any:
UpperCAmelCase : Any = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*A , **A )
def _lowercase( self ) -> int:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
a : Tuple = _tqdm_cls()
def __lowerCamelCase ( ) -> bool:
global _tqdm_active
return bool(_tqdm_active )
def __lowerCamelCase ( ) -> Any:
global _tqdm_active
UpperCAmelCase : int = True
hf_hub_utils.enable_progress_bars()
def __lowerCamelCase ( ) -> Optional[Any]:
global _tqdm_active
UpperCAmelCase : Optional[int] = False
hf_hub_utils.disable_progress_bars()
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> list[int]:
UpperCAmelCase : List[Any] = 2
UpperCAmelCase : Any = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_lowercase )
if n > 1:
factors.append(_lowercase )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = BlenderbotSmallTokenizer
lowercase = False
def _lowercase( self ) -> List[Any]:
super().setUp()
UpperCAmelCase : int = ["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
UpperCAmelCase : Optional[Any] = dict(zip(A , range(len(A ) ) ) )
UpperCAmelCase : Optional[int] = ["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
UpperCAmelCase : Tuple = {"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}
UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def _lowercase( self , **A ) -> Tuple:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **A )
def _lowercase( self , A ) -> Any:
UpperCAmelCase : Tuple = """adapt act apte"""
UpperCAmelCase : Dict = """adapt act apte"""
return input_text, output_text
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[Any] = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase : Optional[int] = """adapt act apte"""
UpperCAmelCase : Dict = ["""adapt""", """act""", """ap@@""", """te"""]
UpperCAmelCase : Tuple = tokenizer.tokenize(A )
self.assertListEqual(A , A )
UpperCAmelCase : Any = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
UpperCAmelCase : List[Any] = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
assert tok("""sam""" ).input_ids == [1384]
UpperCAmelCase : Any = """I am a small frog."""
UpperCAmelCase : Tuple = tok([src_text] , padding=A , truncation=A )["""input_ids"""]
UpperCAmelCase : List[Any] = tok.batch_decode(A , skip_special_tokens=A , clean_up_tokenization_spaces=A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def _lowercase( self ) -> str:
UpperCAmelCase : Union[str, Any] = BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
UpperCAmelCase : Any = """I am a small frog ."""
UpperCAmelCase : str = """."""
UpperCAmelCase : Optional[Any] = tok(A )["""input_ids"""]
UpperCAmelCase : int = tok(A )["""input_ids"""]
assert encoded[-1] == encoded_dot[0]
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = KandinskyVaaPriorPipeline
lowercase = ['prompt']
lowercase = ['prompt', 'negative_prompt']
lowercase = [
'num_images_per_prompt',
'generator',
'num_inference_steps',
'latents',
'negative_prompt',
'guidance_scale',
'output_type',
'return_dict',
]
lowercase = False
@property
def _lowercase( self ) -> Dict:
return 32
@property
def _lowercase( self ) -> List[str]:
return 32
@property
def _lowercase( self ) -> Any:
return self.time_input_dim
@property
def _lowercase( self ) -> List[Any]:
return self.time_input_dim * 4
@property
def _lowercase( self ) -> str:
return 100
@property
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def _lowercase( self ) -> Any:
torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(A )
@property
def _lowercase( self ) -> Any:
torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 12,
"""embedding_dim""": self.text_embedder_hidden_size,
"""num_layers""": 1,
}
UpperCAmelCase : Optional[int] = PriorTransformer(**A )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
UpperCAmelCase : Optional[Any] = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def _lowercase( self ) -> Any:
torch.manual_seed(0 )
UpperCAmelCase : List[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
UpperCAmelCase : Dict = CLIPVisionModelWithProjection(A )
return model
@property
def _lowercase( self ) -> Dict:
UpperCAmelCase : List[str] = CLIPImageProcessor(
crop_size=224 , do_center_crop=A , do_normalize=A , do_resize=A , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Tuple = self.dummy_prior
UpperCAmelCase : Optional[int] = self.dummy_image_encoder
UpperCAmelCase : List[Any] = self.dummy_text_encoder
UpperCAmelCase : Dict = self.dummy_tokenizer
UpperCAmelCase : Any = self.dummy_image_processor
UpperCAmelCase : List[str] = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1000 , clip_sample=A , clip_sample_range=1_0.0 , )
UpperCAmelCase : int = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""scheduler""": scheduler,
"""image_processor""": image_processor,
}
return components
def _lowercase( self , A , A=0 ) -> List[str]:
if str(A ).startswith("""mps""" ):
UpperCAmelCase : Tuple = torch.manual_seed(A )
else:
UpperCAmelCase : Optional[Any] = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase : Union[str, Any] = {
"""prompt""": """horse""",
"""generator""": generator,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = """cpu"""
UpperCAmelCase : Optional[int] = self.get_dummy_components()
UpperCAmelCase : int = self.pipeline_class(**A )
UpperCAmelCase : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase : str = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase : Optional[int] = output.image_embeds
UpperCAmelCase : List[str] = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
UpperCAmelCase : str = image[0, -10:]
UpperCAmelCase : int = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
UpperCAmelCase : Optional[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[int] = torch_device == """cpu"""
UpperCAmelCase : List[Any] = True
UpperCAmelCase : str = False
self._test_inference_batch_single_identical(
test_max_difference=A , relax_max_difference=A , test_mean_pixel_difference=A , )
@skip_mps
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Tuple = torch_device == """cpu"""
UpperCAmelCase : Union[str, Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=A , test_mean_pixel_difference=A , )
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a : Tuple = 4
a : Tuple = 3
class UpperCamelCase_ ( __magic_name__ ):
pass
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
for shard in shards:
for i in range(_lowercase ):
yield {"i": i, "shard": shard}
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Tuple = int(os.environ["""RANK"""] )
UpperCAmelCase : List[str] = int(os.environ["""WORLD_SIZE"""] )
UpperCAmelCase : List[str] = ArgumentParser()
parser.add_argument("""--streaming""" , type=_lowercase )
parser.add_argument("""--local_rank""" , type=_lowercase )
parser.add_argument("""--num_workers""" , type=_lowercase , default=0 )
UpperCAmelCase : Optional[int] = parser.parse_args()
UpperCAmelCase : Optional[Any] = args.streaming
UpperCAmelCase : Union[str, Any] = args.num_workers
UpperCAmelCase : Dict = {"""shards""": [F'''shard_{shard_idx}''' for shard_idx in range(_lowercase )]}
UpperCAmelCase : Union[str, Any] = IterableDataset.from_generator(_lowercase , gen_kwargs=_lowercase )
if not streaming:
UpperCAmelCase : Optional[Any] = Dataset.from_list(list(_lowercase ) )
UpperCAmelCase : int = split_dataset_by_node(_lowercase , rank=_lowercase , world_size=_lowercase )
UpperCAmelCase : Optional[Any] = torch.utils.data.DataLoader(_lowercase , num_workers=_lowercase )
UpperCAmelCase : str = NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : Union[str, Any] = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : Any = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Any = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'mgp-str'
def __init__( self , A=[32, 128] , A=4 , A=3 , A=27 , A=38 , A=50257 , A=30522 , A=768 , A=12 , A=12 , A=4.0 , A=True , A=False , A=1e-5 , A=0.0 , A=0.0 , A=0.0 , A=False , A=0.0_2 , **A , ) -> str:
super().__init__(**A )
UpperCAmelCase : str = image_size
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = num_channels
UpperCAmelCase : List[str] = max_token_length
UpperCAmelCase : Union[str, Any] = num_character_labels
UpperCAmelCase : List[Any] = num_bpe_labels
UpperCAmelCase : Dict = num_wordpiece_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : int = mlp_ratio
UpperCAmelCase : List[Any] = distilled
UpperCAmelCase : Dict = layer_norm_eps
UpperCAmelCase : List[str] = drop_rate
UpperCAmelCase : List[Any] = qkv_bias
UpperCAmelCase : int = attn_drop_rate
UpperCAmelCase : Tuple = drop_path_rate
UpperCAmelCase : Optional[int] = output_aa_attentions
UpperCAmelCase : Dict = initializer_range
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
while b:
UpperCAmelCase , UpperCAmelCase : str = b, a % b
return a
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
return a if b == 0 else euclidean_gcd_recursive(_lowercase , a % b )
def __lowerCamelCase ( ) -> Optional[Any]:
print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
"""Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
"""Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
"""Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
"""Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
"""Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
"""Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
"""Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
"""Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
"""Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
"""Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
"""Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'codegen'
lowercase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , A=50400 , A=2048 , A=2048 , A=4096 , A=28 , A=16 , A=64 , A=None , A="gelu_new" , A=0.0 , A=0.0 , A=0.0 , A=1e-5 , A=0.0_2 , A=True , A=50256 , A=50256 , A=False , **A , ) -> str:
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Union[str, Any] = n_ctx
UpperCAmelCase : Optional[Any] = n_positions
UpperCAmelCase : Tuple = n_embd
UpperCAmelCase : Any = n_layer
UpperCAmelCase : Tuple = n_head
UpperCAmelCase : Optional[Any] = n_inner
UpperCAmelCase : List[Any] = rotary_dim
UpperCAmelCase : Union[str, Any] = activation_function
UpperCAmelCase : Any = resid_pdrop
UpperCAmelCase : Optional[int] = embd_pdrop
UpperCAmelCase : Dict = attn_pdrop
UpperCAmelCase : Dict = layer_norm_epsilon
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : Any = use_cache
UpperCAmelCase : Optional[int] = bos_token_id
UpperCAmelCase : Tuple = eos_token_id
super().__init__(
bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A )
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A = "default" , A = None , A = False , ) -> Union[str, Any]:
super().__init__(A , task=A , patching_specs=A , use_past=A )
if not getattr(self._config , """pad_token_id""" , A ):
# TODO: how to do that better?
UpperCAmelCase : Optional[int] = 0
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : int = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
UpperCAmelCase : Optional[int] = {0: """batch""", 1: """past_sequence + sequence"""}
else:
UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _lowercase( self ) -> int:
return self._config.n_layer
@property
def _lowercase( self ) -> int:
return self._config.n_head
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
UpperCAmelCase : Any = super(A , self ).generate_dummy_inputs(
A , batch_size=A , seq_length=A , is_pair=A , framework=A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Any = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
UpperCAmelCase , UpperCAmelCase : List[str] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
UpperCAmelCase : Dict = seqlen + 2
UpperCAmelCase : List[Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(A ), torch.zeros(A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs["""attention_mask"""]
if self.use_past:
UpperCAmelCase : Optional[int] = ordered_inputs["""attention_mask"""].dtype
UpperCAmelCase : List[Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(A , A , dtype=A )] , dim=1 )
return ordered_inputs
@property
def _lowercase( self ) -> int:
return 13
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCamelCase_ ( __magic_name__ ):
# to overwrite at feature extractactor specific tests
lowercase = None
lowercase = None
@property
def _lowercase( self ) -> Optional[int]:
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(A , """feature_size""" ) )
self.assertTrue(hasattr(A , """sampling_rate""" ) )
self.assertTrue(hasattr(A , """padding_value""" ) )
def _lowercase( self ) -> int:
UpperCAmelCase : List[Any] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : Tuple = feat_extract.model_input_names[0]
UpperCAmelCase : Optional[int] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(A ) == len(A ) for x, y in zip(A , processed_features[input_name] ) ) )
UpperCAmelCase : List[str] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
UpperCAmelCase : List[Any] = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
UpperCAmelCase : Tuple = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase : Tuple = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
UpperCAmelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : Union[str, Any] = feat_extract.model_input_names[0]
UpperCAmelCase : int = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
UpperCAmelCase : int = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowercase( self ) -> str:
UpperCAmelCase : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
UpperCAmelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : Optional[int] = feat_extract.model_input_names[0]
UpperCAmelCase : List[Any] = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
UpperCAmelCase : Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase : List[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowercase( self , A=False ) -> int:
def _inputs_have_equal_length(A ):
UpperCAmelCase : Union[str, Any] = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1e-3 ):
return False
return True
UpperCAmelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : List[str] = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
UpperCAmelCase : Any = feat_extract.model_input_names[0]
UpperCAmelCase : str = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase : Optional[int] = self.feat_extract_tester.seq_length_diff
UpperCAmelCase : int = self.feat_extract_tester.max_seq_length + pad_diff
UpperCAmelCase : str = self.feat_extract_tester.min_seq_length
UpperCAmelCase : List[Any] = self.feat_extract_tester.batch_size
UpperCAmelCase : Optional[int] = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
UpperCAmelCase : Dict = feat_extract.pad(A , padding=A )
UpperCAmelCase : Optional[Any] = input_a[input_name]
UpperCAmelCase : List[Any] = feat_extract.pad(A , padding="""longest""" )
UpperCAmelCase : Union[str, Any] = input_a[input_name]
UpperCAmelCase : Optional[Any] = feat_extract.pad(A , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
UpperCAmelCase : List[Any] = input_a[input_name]
UpperCAmelCase : str = feat_extract.pad(A , padding="""longest""" , return_tensors="""np""" )
UpperCAmelCase : Optional[int] = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(A ):
feat_extract.pad(A , padding="""max_length""" )[input_name]
UpperCAmelCase : str = feat_extract.pad(
A , padding="""max_length""" , max_length=A , return_tensors="""np""" )
UpperCAmelCase : List[Any] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase : List[Any] = feat_extract.pad(A , pad_to_multiple_of=10 )
UpperCAmelCase : str = input_a[input_name]
UpperCAmelCase : List[Any] = feat_extract.pad(A , padding="""longest""" , pad_to_multiple_of=10 )
UpperCAmelCase : List[str] = input_a[input_name]
UpperCAmelCase : Tuple = feat_extract.pad(
A , padding="""max_length""" , pad_to_multiple_of=10 , max_length=A )
UpperCAmelCase : Optional[Any] = input_a[input_name]
UpperCAmelCase : int = feat_extract.pad(
A , padding="""max_length""" , pad_to_multiple_of=10 , max_length=A , return_tensors="""np""" , )
UpperCAmelCase : Any = input_a[input_name]
self.assertTrue(all(len(A ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(A , A ) )
UpperCAmelCase : Tuple = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(A ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
UpperCAmelCase : Tuple = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowercase( self , A=False ) -> List[str]:
def _inputs_have_equal_length(A ):
UpperCAmelCase : str = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1e-3 ):
return False
return True
UpperCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
UpperCAmelCase : int = feat_extract.model_input_names[0]
UpperCAmelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
UpperCAmelCase : List[Any] = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=A )
UpperCAmelCase : List[Any] = input_a[input_name]
UpperCAmelCase : List[Any] = feat_extract.pad(A , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
UpperCAmelCase : str = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to smallest with np
UpperCAmelCase : int = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=A , )
UpperCAmelCase : int = input_a[input_name]
UpperCAmelCase : Union[str, Any] = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
UpperCAmelCase : Optional[int] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to middle
UpperCAmelCase : int = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=A , return_tensors="""np""" , )
UpperCAmelCase : str = input_a[input_name]
UpperCAmelCase : Dict = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=A )
UpperCAmelCase : Union[str, Any] = input_a[input_name]
UpperCAmelCase : int = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
UpperCAmelCase : Tuple = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding="""longest""" , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding="""longest""" , truncation=A )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(A ):
feat_extract.pad(A , padding="""max_length""" , truncation=A )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase : Tuple = 12
UpperCAmelCase : Optional[Any] = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , truncation=A , )
UpperCAmelCase : Tuple = input_a[input_name]
UpperCAmelCase : Optional[int] = feat_extract.pad(
A , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , )
UpperCAmelCase : Optional[Any] = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
UpperCAmelCase : Optional[Any] = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
UpperCAmelCase : Tuple = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
def _lowercase( self ) -> int:
self._check_padding(numpify=A )
def _lowercase( self ) -> Tuple:
self._check_padding(numpify=A )
def _lowercase( self ) -> Tuple:
self._check_truncation(numpify=A )
def _lowercase( self ) -> Dict:
self._check_truncation(numpify=A )
@require_torch
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : str = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase : List[str] = feat_extract.model_input_names[0]
UpperCAmelCase : Dict = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase : Any = feat_extract.pad(A , padding="""longest""" , return_tensors="""np""" )[input_name]
UpperCAmelCase : Optional[int] = feat_extract.pad(A , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase : Tuple = feat_extract.model_input_names[0]
UpperCAmelCase : Tuple = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase : List[Any] = feat_extract.pad(A , padding="""longest""" , return_tensors="""np""" )[input_name]
UpperCAmelCase : Dict = feat_extract.pad(A , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = self.feat_extract_dict
UpperCAmelCase : List[str] = True
UpperCAmelCase : Any = self.feature_extraction_class(**A )
UpperCAmelCase : int = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase : Optional[int] = [len(A ) for x in speech_inputs]
UpperCAmelCase : List[Any] = feat_extract.model_input_names[0]
UpperCAmelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase : str = feat_extract.pad(A , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , A )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , A )
def _lowercase( self ) -> Any:
UpperCAmelCase : Union[str, Any] = self.feat_extract_dict
UpperCAmelCase : Dict = True
UpperCAmelCase : int = self.feature_extraction_class(**A )
UpperCAmelCase : Any = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase : str = [len(A ) for x in speech_inputs]
UpperCAmelCase : Optional[int] = feat_extract.model_input_names[0]
UpperCAmelCase : Optional[Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase : List[Any] = min(A )
UpperCAmelCase : Tuple = feat_extract.pad(
A , padding="""max_length""" , max_length=A , truncation=A , return_tensors="""np""" )
self.assertIn("""attention_mask""" , A )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
UpperCAmelCase : Tuple = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
UpperCAmelCase : str = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(_lowercase , 1 ):
if n < _p:
# then we have our last prime to check
UpperCAmelCase : int = primes[:idx]
break
UpperCAmelCase , UpperCAmelCase : Any = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
UpperCAmelCase : str = False
for r in range(_lowercase ):
UpperCAmelCase : Any = pow(_lowercase , d * 2**r , _lowercase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
UpperCAmelCase : Any = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCamelCase ( ) -> None:
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 338 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 1 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
a : List[str] = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=None , **A ) -> Dict:
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" , A , )
super().__init__(args=A , **A )
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 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.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = 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 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """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}''')
| 338 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a : Optional[int] = {
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt""",
"""tiny""": """https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt""",
"""base.en""": """https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt""",
"""base""": """https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt""",
"""small.en""": """https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt""",
"""small""": """https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt""",
"""medium.en""": """https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt""",
"""medium""": """https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt""",
"""large""": """https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt""",
"""large-v2""": """https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt""",
}
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = ["""layers""", """blocks"""]
for k in ignore_keys:
state_dict.pop(_lowercase , _lowercase )
a : List[Any] = {
"""blocks""": """layers""",
"""mlp.0""": """fc1""",
"""mlp.2""": """fc2""",
"""mlp_ln""": """final_layer_norm""",
""".attn.query""": """.self_attn.q_proj""",
""".attn.key""": """.self_attn.k_proj""",
""".attn.value""": """.self_attn.v_proj""",
""".attn_ln""": """.self_attn_layer_norm""",
""".attn.out""": """.self_attn.out_proj""",
""".cross_attn.query""": """.encoder_attn.q_proj""",
""".cross_attn.key""": """.encoder_attn.k_proj""",
""".cross_attn.value""": """.encoder_attn.v_proj""",
""".cross_attn_ln""": """.encoder_attn_layer_norm""",
""".cross_attn.out""": """.encoder_attn.out_proj""",
"""decoder.ln.""": """decoder.layer_norm.""",
"""encoder.ln.""": """encoder.layer_norm.""",
"""token_embedding""": """embed_tokens""",
"""encoder.positional_embedding""": """encoder.embed_positions.weight""",
"""decoder.positional_embedding""": """decoder.embed_positions.weight""",
"""ln_post""": """layer_norm""",
}
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
UpperCAmelCase : List[str] = list(s_dict.keys() )
for key in keys:
UpperCAmelCase : Optional[int] = key
for k, v in WHISPER_MAPPING.items():
if k in key:
UpperCAmelCase : Tuple = new_key.replace(_lowercase , _lowercase )
print(F'''{key} -> {new_key}''' )
UpperCAmelCase : List[str] = s_dict.pop(_lowercase )
return s_dict
def __lowerCamelCase ( _lowercase ) -> Tuple:
UpperCAmelCase , UpperCAmelCase : Dict = emb.weight.shape
UpperCAmelCase : int = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
UpperCAmelCase : Union[str, Any] = emb.weight.data
return lin_layer
def __lowerCamelCase ( _lowercase , _lowercase ) -> bytes:
os.makedirs(_lowercase , exist_ok=_lowercase )
UpperCAmelCase : int = os.path.basename(_lowercase )
UpperCAmelCase : Dict = url.split("""/""" )[-2]
UpperCAmelCase : Dict = os.path.join(_lowercase , _lowercase )
if os.path.exists(_lowercase ) and not os.path.isfile(_lowercase ):
raise RuntimeError(F'''{download_target} exists and is not a regular file''' )
if os.path.isfile(_lowercase ):
UpperCAmelCase : str = open(_lowercase , """rb""" ).read()
if hashlib.shaaaa(_lowercase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''' )
with urllib.request.urlopen(_lowercase ) as source, open(_lowercase , """wb""" ) as output:
with tqdm(
total=int(source.info().get("""Content-Length""" ) ) , ncols=8_0 , unit="""iB""" , unit_scale=_lowercase , unit_divisor=1_0_2_4 ) as loop:
while True:
UpperCAmelCase : str = source.read(8_1_9_2 )
if not buffer:
break
output.write(_lowercase )
loop.update(len(_lowercase ) )
UpperCAmelCase : int = open(_lowercase , """rb""" ).read()
if hashlib.shaaaa(_lowercase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
"""Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" )
return model_bytes
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if ".pt" not in checkpoint_path:
UpperCAmelCase : Optional[Any] = _download(_MODELS[checkpoint_path] )
else:
UpperCAmelCase : str = torch.load(_lowercase , map_location="""cpu""" )
UpperCAmelCase : Optional[int] = original_checkpoint["""dims"""]
UpperCAmelCase : List[str] = original_checkpoint["""model_state_dict"""]
UpperCAmelCase : int = state_dict["""decoder.token_embedding.weight"""]
remove_ignore_keys_(_lowercase )
rename_keys(_lowercase )
UpperCAmelCase : Union[str, Any] = True
UpperCAmelCase : Union[str, Any] = state_dict["""decoder.layers.0.fc1.weight"""].shape[0]
UpperCAmelCase : Any = WhisperConfig(
vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=_lowercase , decoder_ffn_dim=_lowercase , num_mel_bins=dimensions["""n_mels"""] , d_model=dimensions["""n_audio_state"""] , max_target_positions=dimensions["""n_text_ctx"""] , encoder_layers=dimensions["""n_audio_layer"""] , encoder_attention_heads=dimensions["""n_audio_head"""] , decoder_layers=dimensions["""n_text_layer"""] , decoder_attention_heads=dimensions["""n_text_state"""] , max_source_positions=dimensions["""n_audio_ctx"""] , )
UpperCAmelCase : List[Any] = WhisperForConditionalGeneration(_lowercase )
UpperCAmelCase , UpperCAmelCase : Dict = model.model.load_state_dict(_lowercase , strict=_lowercase )
if len(_lowercase ) > 0 and not set(_lowercase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"""Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"""
F''' but all the following weights are missing {missing}''' )
if tie_embeds:
UpperCAmelCase : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
UpperCAmelCase : str = proj_out_weights
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
a : Optional[int] = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 1 |
'''simple docstring'''
import sys
import turtle
def __lowerCamelCase ( _lowercase , _lowercase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , ) -> None:
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
triangle(_lowercase , get_mid(_lowercase , _lowercase ) , get_mid(_lowercase , _lowercase ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
"""Correct format for using this script: """
"""python fractals.py <int:depth_for_fractal>"""
)
a : Optional[Any] = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("""red""")
a : Tuple = [(-1_7_5, -1_2_5), (0, 1_7_5), (1_7_5, -1_2_5)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 338 |
'''simple docstring'''
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 UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[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(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, 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(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[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(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : 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(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) 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 _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
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(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = 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
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = 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 _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
else:
return a * actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
if b < 0:
return 1 / actual_power(_lowercase , _lowercase )
return actual_power(_lowercase , _lowercase )
if __name__ == "__main__":
print(power(-2, -3))
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> List[str]:
if isinstance(_lowercase , torch.Tensor ):
return image
elif isinstance(_lowercase , PIL.Image.Image ):
UpperCAmelCase : Tuple = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase : List[str] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
UpperCAmelCase : List[Any] = np.concatenate(_lowercase , axis=0 )
UpperCAmelCase : List[Any] = np.array(_lowercase ).astype(np.floataa ) / 255.0
UpperCAmelCase : List[str] = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase : int = 2.0 * image - 1.0
UpperCAmelCase : str = torch.from_numpy(_lowercase )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : str = torch.cat(_lowercase , dim=0 )
return image
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=0.9995 ) -> Tuple:
if not isinstance(_lowercase , np.ndarray ):
UpperCAmelCase : Tuple = True
UpperCAmelCase : Union[str, Any] = va.device
UpperCAmelCase : List[str] = va.cpu().numpy()
UpperCAmelCase : List[Any] = va.cpu().numpy()
UpperCAmelCase : Optional[Any] = np.sum(va * va / (np.linalg.norm(_lowercase ) * np.linalg.norm(_lowercase )) )
if np.abs(_lowercase ) > DOT_THRESHOLD:
UpperCAmelCase : str = (1 - t) * va + t * va
else:
UpperCAmelCase : Dict = np.arccos(_lowercase )
UpperCAmelCase : Union[str, Any] = np.sin(_lowercase )
UpperCAmelCase : List[str] = theta_a * t
UpperCAmelCase : Union[str, Any] = np.sin(_lowercase )
UpperCAmelCase : Optional[Any] = np.sin(theta_a - theta_t ) / sin_theta_a
UpperCAmelCase : Dict = sin_theta_t / sin_theta_a
UpperCAmelCase : List[Any] = sa * va + sa * va
if inputs_are_torch:
UpperCAmelCase : str = torch.from_numpy(_lowercase ).to(_lowercase )
return va
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = F.normalize(_lowercase , dim=-1 )
UpperCAmelCase : Optional[int] = F.normalize(_lowercase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[Any]:
for param in model.parameters():
UpperCAmelCase : Optional[int] = value
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> List[Any]:
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
UpperCAmelCase : Optional[Any] = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size["""shortest_edge"""]
)
UpperCAmelCase : Any = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def _lowercase( self , A = "auto" ) -> int:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase : Optional[int] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def _lowercase( self ) -> Optional[Any]:
self.enable_attention_slicing(A )
def _lowercase( self ) -> List[str]:
set_requires_grad(self.vae , A )
def _lowercase( self ) -> str:
set_requires_grad(self.vae , A )
def _lowercase( self ) -> Tuple:
set_requires_grad(self.unet , A )
def _lowercase( self ) -> Optional[int]:
set_requires_grad(self.unet , A )
def _lowercase( self , A , A , A ) -> Any:
# get the original timestep using init_timestep
UpperCAmelCase : Dict = min(int(num_inference_steps * strength ) , A )
UpperCAmelCase : Optional[Any] = max(num_inference_steps - init_timestep , 0 )
UpperCAmelCase : Dict = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _lowercase( self , A , A , A , A , A , A=None ) -> Dict:
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
UpperCAmelCase : Tuple = image.to(device=A , dtype=A )
if isinstance(A , A ):
UpperCAmelCase : Optional[Any] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
UpperCAmelCase : List[str] = torch.cat(A , dim=0 )
else:
UpperCAmelCase : List[Any] = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase : Tuple = 0.1_8_2_1_5 * init_latents
UpperCAmelCase : Optional[int] = init_latents.repeat_interleave(A , dim=0 )
UpperCAmelCase : Tuple = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
UpperCAmelCase : str = self.scheduler.add_noise(A , A , A )
UpperCAmelCase : str = init_latents
return latents
def _lowercase( self , A ) -> Tuple:
UpperCAmelCase : str = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
UpperCAmelCase : List[str] = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
UpperCAmelCase : Dict = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" , """""" ).rstrip(""" .,""" )
def _lowercase( self , A , A ) -> int:
UpperCAmelCase : Optional[Any] = self.feature_extractor.preprocess(A )
UpperCAmelCase : Any = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
UpperCAmelCase : str = self.clip_model.get_image_features(A )
UpperCAmelCase : str = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
UpperCAmelCase : Tuple = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _lowercase( self , A , A , A , A , A , A , A , ) -> str:
UpperCAmelCase : Optional[Any] = latents.detach().requires_grad_()
UpperCAmelCase : str = self.scheduler.scale_model_input(A , A )
# predict the noise residual
UpperCAmelCase : str = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
UpperCAmelCase : Dict = self.scheduler.alphas_cumprod[timestep]
UpperCAmelCase : str = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase : Dict = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
UpperCAmelCase : Optional[Any] = torch.sqrt(A )
UpperCAmelCase : Optional[Any] = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
UpperCAmelCase : List[Any] = self.scheduler.sigmas[index]
UpperCAmelCase : Dict = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase : Optional[Any] = 1 / 0.1_8_2_1_5 * sample
UpperCAmelCase : str = self.vae.decode(A ).sample
UpperCAmelCase : int = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Union[str, Any] = transforms.Resize(self.feature_extractor_size )(A )
UpperCAmelCase : Union[str, Any] = self.normalize(A ).to(latents.dtype )
UpperCAmelCase : str = self.clip_model.get_image_features(A )
UpperCAmelCase : Tuple = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
UpperCAmelCase : List[Any] = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
UpperCAmelCase : Any = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
UpperCAmelCase : List[str] = latents.detach() + grads * (sigma**2)
UpperCAmelCase : List[str] = noise_pred_original
else:
UpperCAmelCase : Any = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> List[Any]:
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
UpperCAmelCase : Any = [generator] + [None] * (batch_size - 1)
UpperCAmelCase : Any = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
UpperCAmelCase : int = [x[0] for x in coca_is_none if x[1]]
UpperCAmelCase : str = """, """.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
UpperCAmelCase : Optional[int] = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
UpperCAmelCase : Union[str, Any] = self.get_image_description(A )
# get prompt text embeddings for content and style
UpperCAmelCase : List[Any] = self.tokenizer(
A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors="""pt""" , )
UpperCAmelCase : Dict = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase : str = self.tokenizer(
A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors="""pt""" , )
UpperCAmelCase : Optional[int] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase : Any = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : Any = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
UpperCAmelCase : Union[str, Any] = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
UpperCAmelCase : str = {}
if accepts_offset:
UpperCAmelCase : List[str] = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
UpperCAmelCase , UpperCAmelCase : Tuple = self.get_timesteps(A , A , self.device )
UpperCAmelCase : Dict = timesteps[:1].repeat(A )
# Preprocess image
UpperCAmelCase : int = preprocess(A , A , A )
UpperCAmelCase : Dict = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
UpperCAmelCase : str = preprocess(A , A , A )
UpperCAmelCase : Any = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
UpperCAmelCase : List[str] = slerp(A , A , A )
if clip_guidance_scale > 0:
UpperCAmelCase : Optional[Any] = self.get_clip_image_embeddings(A , A )
UpperCAmelCase : Optional[Any] = self.get_clip_image_embeddings(A , A )
UpperCAmelCase : Tuple = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase : List[str] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase : Union[str, Any] = content_text_input.input_ids.shape[-1]
UpperCAmelCase : List[str] = self.tokenizer([""""""] , padding="""max_length""" , max_length=A , return_tensors="""pt""" )
UpperCAmelCase : List[Any] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
UpperCAmelCase : str = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : List[Any] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase : Optional[int] = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase : Dict = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
UpperCAmelCase : Dict = torch.randn(A , generator=A , device="""cpu""" , dtype=A ).to(
self.device )
else:
UpperCAmelCase : Optional[int] = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
UpperCAmelCase : Tuple = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase : int = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase : int = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase : Dict = {}
if accepts_eta:
UpperCAmelCase : Any = eta
# check if the scheduler accepts generator
UpperCAmelCase : Tuple = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
UpperCAmelCase : Optional[Any] = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase : List[str] = self.scheduler.scale_model_input(A , A )
# predict the noise residual
UpperCAmelCase : Union[str, Any] = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : Tuple = noise_pred.chunk(2 )
UpperCAmelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
UpperCAmelCase : List[Any] = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
UpperCAmelCase , UpperCAmelCase : int = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : Tuple = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase : int = 1 / 0.1_8_2_1_5 * latents
UpperCAmelCase : Tuple = self.vae.decode(A ).sample
UpperCAmelCase : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : Optional[Any] = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
a : int = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def __lowerCamelCase ( _lowercase ) -> str:
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
UpperCAmelCase : int = list(s_dict.keys() )
for key in keys:
UpperCAmelCase : Any = R""".*/layers_(\d+)"""
UpperCAmelCase : Dict = key
if re.match(_lowercase , _lowercase ):
UpperCAmelCase : List[Any] = re.sub(R"""layers_(\d+)""" , R"""block/\1/layer""" , _lowercase )
UpperCAmelCase : int = R"""(encoder|decoder)\/"""
if re.match(_lowercase , _lowercase ):
UpperCAmelCase : Optional[int] = re.match(_lowercase , _lowercase ).groups()
if groups[0] == "encoder":
UpperCAmelCase : Optional[Any] = re.sub(R"""/mlp/""" , R"""/1/mlp/""" , _lowercase )
UpperCAmelCase : str = re.sub(R"""/pre_mlp_layer_norm/""" , R"""/1/layer_norm/""" , _lowercase )
elif groups[0] == "decoder":
UpperCAmelCase : List[Any] = re.sub(R"""/mlp/""" , R"""/2/mlp/""" , _lowercase )
UpperCAmelCase : Tuple = re.sub(R"""/pre_mlp_layer_norm/""" , R"""/2/layer_norm/""" , _lowercase )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
UpperCAmelCase : List[Any] = new_key.replace(_lowercase , _lowercase )
print(F'''{key} -> {new_key}''' )
UpperCAmelCase : Optional[Any] = s_dict.pop(_lowercase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase : List[Any] = s_dict[
"""encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase : Tuple = s_dict[
"""decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
UpperCAmelCase : Any = s_dict[key].shape[0]
UpperCAmelCase : Union[str, Any] = s_dict[key]
for idx in range(_lowercase ):
UpperCAmelCase : Union[str, Any] = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring' )}''' )
s_dict.pop(_lowercase )
return s_dict
a : Optional[Any] = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
# Convert a google style config to the hugging face fromat
import regex as re
with open(_lowercase , """r""" ) as f:
UpperCAmelCase : Any = f.read()
UpperCAmelCase : str = re.findall(R"""(.*) = ([0-9.]*)""" , _lowercase )
UpperCAmelCase : str = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
UpperCAmelCase : Dict = float(_lowercase ) if """.""" in value else int(_lowercase )
UpperCAmelCase : Union[str, Any] = re.findall(R"""(.*activations) = \(\'(.*)\',\)""" , _lowercase )[0]
UpperCAmelCase : Dict = str(activation[1] )
UpperCAmelCase : List[str] = num_experts
UpperCAmelCase : Tuple = SwitchTransformersConfig(**_lowercase )
return config
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None , _lowercase="./" , _lowercase=8 ) -> List[Any]:
# Initialise PyTorch model
print(F'''Loading flax weights from : {flax_checkpoint_path}''' )
UpperCAmelCase : Optional[Any] = checkpoints.load_tax_checkpoint(_lowercase )
if gin_file is not None:
UpperCAmelCase : int = convert_gin_to_config(_lowercase , _lowercase )
else:
UpperCAmelCase : List[Any] = SwitchTransformersConfig.from_pretrained(_lowercase )
UpperCAmelCase : Dict = SwitchTransformersForConditionalGeneration(_lowercase )
UpperCAmelCase : str = flax_params["""target"""]
UpperCAmelCase : Optional[int] = flatten_dict(_lowercase , sep="""/""" )
UpperCAmelCase : Union[str, Any] = rename_keys(_lowercase )
UpperCAmelCase : Optional[int] = unflatten_dict(_lowercase , sep="""/""" )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(_lowercase , _lowercase )
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
pt_model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
""" model architecture. If not provided, a `gin_file` has to be provided."""
),
)
parser.add_argument(
"""--gin_file""",
default=None,
type=str,
required=False,
help="""Path to the gin config file. If not provided, a `config_file` has to be passed """,
)
parser.add_argument(
"""--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
)
parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
a : int = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
a : Tuple = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use DeformableDetrImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
from typing import Any
class UpperCamelCase_ :
def __init__( self , A ) -> List[str]:
UpperCAmelCase : Tuple = data
UpperCAmelCase : Union[str, Any] = None
def __repr__( self ) -> str:
return f'''Node({self.data})'''
class UpperCamelCase_ :
def __init__( self ) -> Optional[Any]:
UpperCAmelCase : List[Any] = None
def __iter__( self ) -> Any:
UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
UpperCAmelCase : Optional[int] = node.next
def __len__( self ) -> int:
return sum(1 for _ in self )
def __repr__( self ) -> str:
return "->".join([str(A ) for item in self] )
def __getitem__( self , A ) -> Any:
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , A , A ) -> None:
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCAmelCase : List[str] = self.head
for _ in range(A ):
UpperCAmelCase : Any = current.next
UpperCAmelCase : List[str] = data
def _lowercase( self , A ) -> None:
self.insert_nth(len(self ) , A )
def _lowercase( self , A ) -> None:
self.insert_nth(0 , A )
def _lowercase( self , A , A ) -> None:
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCAmelCase : str = Node(A )
if self.head is None:
UpperCAmelCase : List[Any] = new_node
elif index == 0:
UpperCAmelCase : List[str] = self.head # link new_node to head
UpperCAmelCase : Dict = new_node
else:
UpperCAmelCase : List[str] = self.head
for _ in range(index - 1 ):
UpperCAmelCase : Optional[int] = temp.next
UpperCAmelCase : int = temp.next
UpperCAmelCase : str = new_node
def _lowercase( self ) -> None: # print every node data
print(self )
def _lowercase( self ) -> Any:
return self.delete_nth(0 )
def _lowercase( self ) -> Any: # delete from tail
return self.delete_nth(len(self ) - 1 )
def _lowercase( self , A = 0 ) -> Any:
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCAmelCase : List[Any] = self.head # default first node
if index == 0:
UpperCAmelCase : Union[str, Any] = self.head.next
else:
UpperCAmelCase : Optional[int] = self.head
for _ in range(index - 1 ):
UpperCAmelCase : List[Any] = temp.next
UpperCAmelCase : str = temp.next
UpperCAmelCase : Optional[int] = temp.next.next
return delete_node.data
def _lowercase( self ) -> bool:
return self.head is None
def _lowercase( self ) -> None:
UpperCAmelCase : Dict = None
UpperCAmelCase : Dict = self.head
while current:
# Store the current node's next node.
UpperCAmelCase : Any = current.next
# Make the current node's next point backwards
UpperCAmelCase : Dict = prev
# Make the previous node be the current node
UpperCAmelCase : Dict = current
# Make the current node the next node (to progress iteration)
UpperCAmelCase : Dict = next_node
# Return prev in order to put the head at the end
UpperCAmelCase : Dict = prev
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[Any] = LinkedList()
assert linked_list.is_empty() is True
assert str(_lowercase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(_lowercase ) == i
linked_list.insert_nth(_lowercase , i + 1 )
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1 , 1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0 , 1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(_lowercase ) == 9
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1 , 1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
UpperCAmelCase : Optional[int] = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8 , 1 ) )
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Any = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.5_5555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCAmelCase : Union[str, Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(_lowercase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCAmelCase : Optional[int] = linked_list.delete_head()
assert result == -9
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCAmelCase : Optional[int] = linked_list.delete_tail()
assert result == 12.2
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCAmelCase : str = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(_lowercase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_lowercase )
assert (
str(_lowercase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_lowercase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def __lowerCamelCase ( ) -> Dict:
from doctest import testmod
testmod()
UpperCAmelCase : Tuple = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(_lowercase )
print("""\nReading/changing Node data using indexing:""" )
print(F'''Element at Position 1: {linked_list[1]}''' )
UpperCAmelCase : int = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(_lowercase )
print(F'''length of linked_list is : {len(_lowercase )}''' )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 1 |
'''simple docstring'''
import pprint
import requests
a : int = """https://zenquotes.io/api"""
def __lowerCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __lowerCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
a : int = random_quotes()
pprint.pprint(response)
| 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> list:
if any(not isinstance(_lowercase , _lowercase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowercase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_lowercase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[str]:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCamelCase ( _lowercase , _lowercase=0 ) -> Union[str, Any]:
return sorted(_lowercase , key=lambda _lowercase : x[column] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=float("""inf""" ) ) -> Tuple:
for i in range(points_counts - 1 ):
for j in range(i + 1 , _lowercase ):
UpperCAmelCase : Optional[int] = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCAmelCase : List[Any] = current_dis
return min_dis
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=float("""inf""" ) ) -> Optional[int]:
for i in range(min(6 , points_counts - 1 ) , _lowercase ):
for j in range(max(0 , i - 6 ) , _lowercase ):
UpperCAmelCase : Tuple = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCAmelCase : Dict = current_dis
return min_dis
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
# base case
if points_counts <= 3:
return dis_between_closest_pair(_lowercase , _lowercase )
# recursion
UpperCAmelCase : List[Any] = points_counts // 2
UpperCAmelCase : Dict = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[:mid] , _lowercase )
UpperCAmelCase : str = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[mid:] , points_counts - mid )
UpperCAmelCase : List[str] = min(_lowercase , _lowercase )
UpperCAmelCase : Any = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(_lowercase )
UpperCAmelCase : Dict = dis_between_closest_in_strip(
_lowercase , len(_lowercase ) , _lowercase )
return min(_lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> Tuple:
UpperCAmelCase : Union[str, Any] = column_based_sort(_lowercase , column=0 )
UpperCAmelCase : Tuple = column_based_sort(_lowercase , column=1 )
return (
closest_pair_of_points_sqr(
_lowercase , _lowercase , _lowercase )
) ** 0.5
if __name__ == "__main__":
a : Union[str, Any] = [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __lowerCamelCase ( _lowercase ) -> list[int]:
if num <= 0:
UpperCAmelCase : List[str] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(_lowercase )
UpperCAmelCase : Tuple = [True] * (num + 1)
UpperCAmelCase : str = []
UpperCAmelCase : List[str] = 2
UpperCAmelCase : Any = int(math.sqrt(_lowercase ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(_lowercase )
# Set multiples of start be False
for i in range(start * start , num + 1 , _lowercase ):
if sieve[i] is True:
UpperCAmelCase : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(_lowercase )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCamelCase_ :
def __init__( self , A , A=2 , A=3 , A=4 , A=2 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=36 , A=3 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=6 , A=6 , A=3 , A=4 , A=None , A=1000 , ) -> Union[str, Any]:
UpperCAmelCase : List[str] = parent
UpperCAmelCase : Tuple = batch_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : Any = text_seq_length
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Optional[int] = use_input_mask
UpperCAmelCase : Tuple = use_token_type_ids
UpperCAmelCase : Tuple = use_labels
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : List[Any] = hidden_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : str = hidden_act
UpperCAmelCase : Optional[Any] = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : List[Any] = type_vocab_size
UpperCAmelCase : Optional[int] = type_sequence_label_size
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Dict = coordinate_size
UpperCAmelCase : Optional[int] = shape_size
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Tuple = num_choices
UpperCAmelCase : Optional[Any] = scope
UpperCAmelCase : Union[str, Any] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
UpperCAmelCase : Any = text_seq_length
UpperCAmelCase : Optional[int] = (image_size // patch_size) ** 2 + 1
UpperCAmelCase : str = self.text_seq_length + self.image_seq_length
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
UpperCAmelCase : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCAmelCase : Optional[Any] = bbox[i, j, 3]
UpperCAmelCase : int = bbox[i, j, 1]
UpperCAmelCase : Any = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCAmelCase : List[str] = bbox[i, j, 2]
UpperCAmelCase : List[str] = bbox[i, j, 0]
UpperCAmelCase : int = t
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = None
if self.use_input_mask:
UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.text_seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
UpperCAmelCase : Tuple = None
UpperCAmelCase : Optional[Any] = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
UpperCAmelCase : Any = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = LayoutLMvaModel(config=A )
model.to(A )
model.eval()
# text + image
UpperCAmelCase : Dict = model(A , pixel_values=A )
UpperCAmelCase : List[Any] = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A )
UpperCAmelCase : int = model(A , bbox=A , pixel_values=A , token_type_ids=A )
UpperCAmelCase : List[Any] = model(A , bbox=A , pixel_values=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
UpperCAmelCase : Optional[Any] = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
UpperCAmelCase : Optional[int] = model(pixel_values=A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Dict:
UpperCAmelCase : Any = self.num_labels
UpperCAmelCase : List[str] = LayoutLMvaForSequenceClassification(A )
model.to(A )
model.eval()
UpperCAmelCase : Dict = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Optional[Any]:
UpperCAmelCase : Any = self.num_labels
UpperCAmelCase : Optional[Any] = LayoutLMvaForTokenClassification(config=A )
model.to(A )
model.eval()
UpperCAmelCase : int = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Any:
UpperCAmelCase : Optional[Any] = LayoutLMvaForQuestionAnswering(config=A )
model.to(A )
model.eval()
UpperCAmelCase : List[str] = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase( self ) -> int:
UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) : Optional[int] = config_and_inputs
UpperCAmelCase : Dict = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = False
lowercase = False
lowercase = False
lowercase = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _lowercase( self , A , A , A , A , A ) -> str:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Any = LayoutLMvaModelTester(self )
UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , hidden_size=37 )
def _lowercase( self , A , A , A=False ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = copy.deepcopy(A )
if model_class in get_values(A ):
UpperCAmelCase : str = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(A , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(A ):
UpperCAmelCase : Any = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in get_values(A ):
UpperCAmelCase : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
UpperCAmelCase : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
UpperCAmelCase : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
UpperCAmelCase : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=A , )
return inputs_dict
def _lowercase( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase : List[Any] = type
self.model_tester.create_and_check_model(*A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
@slow
def _lowercase( self ) -> int:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : List[str] = LayoutLMvaModel.from_pretrained(A )
self.assertIsNotNone(A )
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@cached_property
def _lowercase( self ) -> List[Any]:
return LayoutLMvaImageProcessor(apply_ocr=A ) if is_vision_available() else None
@slow
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" ).to(A )
UpperCAmelCase : Dict = self.default_image_processor
UpperCAmelCase : str = prepare_img()
UpperCAmelCase : List[str] = image_processor(images=A , return_tensors="""pt""" ).pixel_values.to(A )
UpperCAmelCase : Tuple = torch.tensor([[1, 2]] )
UpperCAmelCase : Union[str, Any] = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
UpperCAmelCase : Dict = model(
input_ids=input_ids.to(A ) , bbox=bbox.to(A ) , pixel_values=pixel_values.to(A ) , )
# verify the logits
UpperCAmelCase : List[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , A )
UpperCAmelCase : Tuple = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(A )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , A , atol=1e-4 ) )
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = StableUnCLIPPipeline
lowercase = TEXT_TO_IMAGE_PARAMS
lowercase = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase = TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
lowercase = False
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = 32
UpperCAmelCase : List[Any] = embedder_hidden_size
# prior components
torch.manual_seed(0 )
UpperCAmelCase : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
UpperCAmelCase : int = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=A , projection_dim=A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=A , num_layers=1 , )
torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = DDPMScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1000 , clip_sample=A , clip_sample_range=5.0 , beta_schedule="""squaredcos_cap_v2""" , )
# regular denoising components
torch.manual_seed(0 )
UpperCAmelCase : List[str] = StableUnCLIPImageNormalizer(embedding_dim=A )
UpperCAmelCase : List[Any] = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
UpperCAmelCase : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=A , layers_per_block=1 , upcast_attention=A , use_linear_projection=A , )
torch.manual_seed(0 )
UpperCAmelCase : List[str] = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , prediction_type="""v_prediction""" , set_alpha_to_one=A , steps_offset=1 , )
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = AutoencoderKL()
UpperCAmelCase : Tuple = {
# prior components
"""prior_tokenizer""": prior_tokenizer,
"""prior_text_encoder""": prior_text_encoder,
"""prior""": prior,
"""prior_scheduler""": prior_scheduler,
# image noising components
"""image_normalizer""": image_normalizer,
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder,
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
}
return components
def _lowercase( self , A , A=0 ) -> Optional[int]:
if str(A ).startswith("""mps""" ):
UpperCAmelCase : Dict = torch.manual_seed(A )
else:
UpperCAmelCase : int = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""prior_num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def _lowercase( self ) -> str:
UpperCAmelCase : Any = torch_device == """cpu"""
self._test_attention_slicing_forward_pass(test_max_difference=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[Any] = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=A )
@slow
@require_torch_gpu
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase( self ) -> Dict:
UpperCAmelCase : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy""" )
UpperCAmelCase : Dict = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase : Optional[int] = torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCAmelCase : Tuple = pipe("""anime turle""" , generator=A , output_type="""np""" )
UpperCAmelCase : Union[str, Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(A , A )
def _lowercase( self ) -> Optional[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase : Any = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
UpperCAmelCase : Dict = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase : Tuple = pipe(
"""anime turtle""" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="""np""" , )
UpperCAmelCase : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : Tuple = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'rwkv'
lowercase = {'max_position_embeddings': 'context_length'}
def __init__( self , A=50277 , A=1024 , A=4096 , A=32 , A=None , A=None , A=1e-5 , A=0 , A=0 , A=6 , A=False , A=True , **A , ) -> str:
UpperCAmelCase : List[str] = vocab_size
UpperCAmelCase : List[str] = context_length
UpperCAmelCase : Dict = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Union[str, Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size
UpperCAmelCase : Any = intermediate_size if intermediate_size is not None else 4 * hidden_size
UpperCAmelCase : Optional[int] = layer_norm_epsilon
UpperCAmelCase : List[str] = rescale_every
UpperCAmelCase : List[str] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
tie_word_embeddings=A , bos_token_id=A , eos_token_id=A , **A )
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Any = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": """bpe.codes""",
}
a : Any = {
"""vocab_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
},
"""merges_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
},
}
a : str = {
"""vinai/phobert-base""": 2_5_6,
"""vinai/phobert-large""": 2_5_6,
}
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : str = set()
UpperCAmelCase : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase : int = char
UpperCAmelCase : Any = set(_lowercase )
return pairs
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , A , A , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , **A , ) -> Dict:
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , **A , )
UpperCAmelCase : Dict = vocab_file
UpperCAmelCase : List[str] = merges_file
UpperCAmelCase : Tuple = {}
UpperCAmelCase : List[str] = 0
UpperCAmelCase : str = 1
UpperCAmelCase : Optional[Any] = 2
UpperCAmelCase : Tuple = 3
self.add_from_file(A )
UpperCAmelCase : Optional[int] = {v: k for k, v in self.encoder.items()}
with open(A , encoding="""utf-8""" ) as merges_handle:
UpperCAmelCase : Union[str, Any] = merges_handle.read().split("""\n""" )[:-1]
UpperCAmelCase : int = [tuple(merge.split()[:-1] ) for merge in merges]
UpperCAmelCase : Tuple = dict(zip(A , range(len(A ) ) ) )
UpperCAmelCase : Dict = {}
def _lowercase( self , A , A = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : Any = [self.cls_token_id]
UpperCAmelCase : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : List[Any] = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _lowercase( self ) -> Any:
return len(self.encoder )
def _lowercase( self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def _lowercase( self , A ) -> Dict:
if token in self.cache:
return self.cache[token]
UpperCAmelCase : Optional[Any] = tuple(A )
UpperCAmelCase : List[Any] = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
UpperCAmelCase : List[Any] = get_pairs(A )
if not pairs:
return token
while True:
UpperCAmelCase : List[Any] = min(A , key=lambda A : self.bpe_ranks.get(A , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase , UpperCAmelCase : Tuple = bigram
UpperCAmelCase : Dict = []
UpperCAmelCase : Optional[Any] = 0
while i < len(A ):
try:
UpperCAmelCase : Optional[int] = word.index(A , A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase : int = j
if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase : Tuple = tuple(A )
UpperCAmelCase : Tuple = new_word
if len(A ) == 1:
break
else:
UpperCAmelCase : Optional[int] = get_pairs(A )
UpperCAmelCase : List[str] = """@@ """.join(A )
UpperCAmelCase : Any = word[:-4]
UpperCAmelCase : Optional[Any] = word
return word
def _lowercase( self , A ) -> Any:
UpperCAmelCase : Dict = []
UpperCAmelCase : int = re.findall(r"""\S+\n?""" , A )
for token in words:
split_tokens.extend(list(self.bpe(A ).split(""" """ ) ) )
return split_tokens
def _lowercase( self , A ) -> int:
return self.encoder.get(A , self.encoder.get(self.unk_token ) )
def _lowercase( self , A ) -> Union[str, Any]:
return self.decoder.get(A , self.unk_token )
def _lowercase( self , A ) -> str:
UpperCAmelCase : int = """ """.join(A ).replace("""@@ """ , """""" ).strip()
return out_string
def _lowercase( self , A , A = None ) -> Tuple[str]:
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : str = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase : Dict = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
if os.path.abspath(self.merges_file ) != os.path.abspath(A ):
copyfile(self.merges_file , A )
return out_vocab_file, out_merge_file
def _lowercase( self , A ) -> str:
if isinstance(A , A ):
try:
with open(A , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(A )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
UpperCAmelCase : Optional[Any] = f.readlines()
for lineTmp in lines:
UpperCAmelCase : Optional[int] = lineTmp.strip()
UpperCAmelCase : List[str] = line.rfind(""" """ )
if idx == -1:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'""" )
UpperCAmelCase : Any = line[:idx]
UpperCAmelCase : Optional[Any] = len(self.encoder )
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'linear'
lowercase = 'cosine'
lowercase = 'cosine_with_restarts'
lowercase = 'polynomial'
lowercase = 'constant'
lowercase = 'constant_with_warmup'
lowercase = 'piecewise_constant'
def __lowerCamelCase ( _lowercase , _lowercase = -1 ) -> List[str]:
return LambdaLR(_lowercase , lambda _lowercase : 1 , last_epoch=_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase = -1 ) -> List[str]:
def lr_lambda(_lowercase ):
if current_step < num_warmup_steps:
return float(_lowercase ) / float(max(1.0 , _lowercase ) )
return 1.0
return LambdaLR(_lowercase , _lowercase , last_epoch=_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase = -1 ) -> List[Any]:
UpperCAmelCase : List[Any] = {}
UpperCAmelCase : Tuple = step_rules.split(""",""" )
for rule_str in rule_list[:-1]:
UpperCAmelCase , UpperCAmelCase : Optional[Any] = rule_str.split(""":""" )
UpperCAmelCase : Optional[Any] = int(_lowercase )
UpperCAmelCase : Any = float(_lowercase )
UpperCAmelCase : Union[str, Any] = value
UpperCAmelCase : List[str] = float(rule_list[-1] )
def create_rules_function(_lowercase , _lowercase ):
def rule_func(_lowercase ) -> float:
UpperCAmelCase : Dict = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(_lowercase ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase : Tuple = create_rules_function(_lowercase , _lowercase )
return LambdaLR(_lowercase , _lowercase , last_epoch=_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=-1 ) -> Union[str, Any]:
def lr_lambda(_lowercase ):
if current_step < num_warmup_steps:
return float(_lowercase ) / float(max(1 , _lowercase ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(_lowercase , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase = 0.5 , _lowercase = -1 ) -> Any:
def lr_lambda(_lowercase ):
if current_step < num_warmup_steps:
return float(_lowercase ) / float(max(1 , _lowercase ) )
UpperCAmelCase : List[Any] = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(_lowercase ) * 2.0 * progress )) )
return LambdaLR(_lowercase , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase = 1 , _lowercase = -1 ) -> int:
def lr_lambda(_lowercase ):
if current_step < num_warmup_steps:
return float(_lowercase ) / float(max(1 , _lowercase ) )
UpperCAmelCase : str = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(_lowercase ) * progress) % 1.0) )) )
return LambdaLR(_lowercase , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=1e-7 , _lowercase=1.0 , _lowercase=-1 ) -> Optional[Any]:
UpperCAmelCase : str = optimizer.defaults["""lr"""]
if not (lr_init > lr_end):
raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''' )
def lr_lambda(_lowercase ):
if current_step < num_warmup_steps:
return float(_lowercase ) / float(max(1 , _lowercase ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase : Union[str, Any] = lr_init - lr_end
UpperCAmelCase : List[str] = num_training_steps - num_warmup_steps
UpperCAmelCase : Optional[Any] = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase : Tuple = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(_lowercase , _lowercase , _lowercase )
a : Any = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = 1 , _lowercase = 1.0 , _lowercase = -1 , ) -> Union[str, Any]:
UpperCAmelCase : Any = SchedulerType(_lowercase )
UpperCAmelCase : Optional[int] = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(_lowercase , last_epoch=_lowercase )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(_lowercase , step_rules=_lowercase , last_epoch=_lowercase )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.''' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(_lowercase , num_warmup_steps=_lowercase , last_epoch=_lowercase )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.''' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
_lowercase , num_warmup_steps=_lowercase , num_training_steps=_lowercase , num_cycles=_lowercase , last_epoch=_lowercase , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
_lowercase , num_warmup_steps=_lowercase , num_training_steps=_lowercase , power=_lowercase , last_epoch=_lowercase , )
return schedule_func(
_lowercase , num_warmup_steps=_lowercase , num_training_steps=_lowercase , last_epoch=_lowercase )
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a : List[Any] = logging.getLogger(__name__)
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
# save results
if os.path.exists(_lowercase ):
if os.path.exists(os.path.join(_lowercase , """config.json""" ) ) and os.path.isfile(
os.path.join(_lowercase , """config.json""" ) ):
os.remove(os.path.join(_lowercase , """config.json""" ) )
if os.path.exists(os.path.join(_lowercase , """pytorch_model.bin""" ) ) and os.path.isfile(
os.path.join(_lowercase , """pytorch_model.bin""" ) ):
os.remove(os.path.join(_lowercase , """pytorch_model.bin""" ) )
else:
os.makedirs(_lowercase )
model.save_pretrained(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase=False ) -> List[Any]:
UpperCAmelCase : Tuple = 2
if unlogit:
UpperCAmelCase : Any = torch.pow(_lowercase , _lowercase )
UpperCAmelCase : Any = p * torch.log(_lowercase )
UpperCAmelCase : List[Any] = 0
return -plogp.sum(dim=-1 )
def __lowerCamelCase ( _lowercase ) -> Any:
logger.info("""lv, h >\t""" + """\t""".join(F'''{x + 1}''' for x in range(len(_lowercase ) ) ) )
for row in range(len(_lowercase ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + """\t""".join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + """\t""".join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=True , _lowercase=True , _lowercase=None , _lowercase=False ) -> int:
UpperCAmelCase , UpperCAmelCase : Any = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase : int = torch.zeros(_lowercase , _lowercase ).to(args.device )
UpperCAmelCase : Union[str, Any] = torch.zeros(_lowercase , _lowercase ).to(args.device )
if head_mask is None:
UpperCAmelCase : Optional[Any] = torch.ones(_lowercase , _lowercase ).to(args.device )
head_mask.requires_grad_(requires_grad=_lowercase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase : List[str] = None
UpperCAmelCase : List[str] = 0.0
UpperCAmelCase : List[Any] = 0.0
for step, inputs in enumerate(tqdm(_lowercase , desc="""Iteration""" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase : List[Any] = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase) , ) : Dict = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase : Optional[int] = model(_lowercase , labels=_lowercase , head_mask=_lowercase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[int] = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(_lowercase ):
UpperCAmelCase : Union[str, Any] = entropy(attn.detach() , _lowercase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(_lowercase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase : List[Any] = 2
UpperCAmelCase : Any = torch.pow(torch.pow(_lowercase , _lowercase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase : Union[str, Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("""Attention entropies""" )
print_ad_tensor(_lowercase )
if compute_importance:
logger.info("""Head importance scores""" )
print_ad_tensor(_lowercase )
logger.info("""Head ranked by importance scores""" )
UpperCAmelCase : str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase : Tuple = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase : Any = head_ranks.view_as(_lowercase )
print_ad_tensor(_lowercase )
return attn_entropy, head_importance, total_loss
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[int] = compute_heads_importance(_lowercase , _lowercase , _lowercase , compute_entropy=_lowercase )
UpperCAmelCase : List[Any] = 1 / loss # instead of downsteam score use the LM loss
logger.info("""Pruning: original score: %f, threshold: %f""" , _lowercase , original_score * args.masking_threshold )
UpperCAmelCase : Optional[Any] = torch.ones_like(_lowercase )
UpperCAmelCase : Optional[Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase : Dict = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase : Any = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase : Tuple = float("""Inf""" )
UpperCAmelCase : List[str] = head_importance.view(-1 ).sort()[1]
if len(_lowercase ) <= num_to_mask:
print("""BREAK BY num_to_mask""" )
break
# mask heads
UpperCAmelCase : Tuple = current_heads_to_mask[:num_to_mask]
logger.info("""Heads to mask: %s""" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase : Tuple = new_head_mask.view(-1 )
UpperCAmelCase : Dict = 0.0
UpperCAmelCase : Optional[int] = new_head_mask.view_as(_lowercase )
UpperCAmelCase : Any = new_head_mask.clone().detach()
print_ad_tensor(_lowercase )
# Compute metric and head importance again
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] = compute_heads_importance(
_lowercase , _lowercase , _lowercase , compute_entropy=_lowercase , head_mask=_lowercase )
UpperCAmelCase : Optional[int] = 1 / loss
logger.info(
"""Masking: current score: %f, remaining heads %d (%.1f percents)""" , _lowercase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_0_0 , )
logger.info("""Final head mask""" )
print_ad_tensor(_lowercase )
np.save(os.path.join(args.output_dir , """head_mask.npy""" ) , head_mask.detach().cpu().numpy() )
return head_mask
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : str = datetime.now()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = compute_heads_importance(
_lowercase , _lowercase , _lowercase , compute_entropy=_lowercase , compute_importance=_lowercase , head_mask=_lowercase )
UpperCAmelCase : List[Any] = 1 / loss
UpperCAmelCase : List[str] = datetime.now() - before_time
UpperCAmelCase : str = sum(p.numel() for p in model.parameters() )
UpperCAmelCase : Dict = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowercase ) )
}
for k, v in heads_to_prune.items():
if isinstance(_lowercase , _lowercase ):
UpperCAmelCase : Optional[int] = [
v,
]
assert sum(len(_lowercase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(_lowercase )
UpperCAmelCase : Optional[int] = sum(p.numel() for p in model.parameters() )
UpperCAmelCase : Any = datetime.now()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple = compute_heads_importance(
_lowercase , _lowercase , _lowercase , compute_entropy=_lowercase , compute_importance=_lowercase , head_mask=_lowercase , actually_pruned=_lowercase , )
UpperCAmelCase : List[Any] = 1 / loss
UpperCAmelCase : Optional[int] = datetime.now() - before_time
logger.info(
"""Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)""" , _lowercase , _lowercase , pruned_num_params / original_num_params * 1_0_0 , )
logger.info("""Pruning: score with masking: %f score with pruning: %f""" , _lowercase , _lowercase )
logger.info("""Pruning: speed ratio (original timing / new timing): %f percents""" , original_time / new_time * 1_0_0 )
save_model(_lowercase , args.output_dir )
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--data_dir""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""The input data dir. Should contain the .tsv files (or other data files) for the task.""" , )
parser.add_argument(
"""--model_name_or_path""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--output_dir""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""The output directory where the model predictions and checkpoints will be written.""" , )
# Other parameters
parser.add_argument(
"""--config_name""" , default="""""" , type=_lowercase , help="""Pretrained config name or path if not the same as model_name_or_path""" , )
parser.add_argument(
"""--tokenizer_name""" , default="""""" , type=_lowercase , help="""Pretrained tokenizer name or path if not the same as model_name_or_path""" , )
parser.add_argument(
"""--cache_dir""" , default=_lowercase , type=_lowercase , help="""Where do you want to store the pre-trained models downloaded from s3""" , )
parser.add_argument(
"""--data_subset""" , type=_lowercase , default=-1 , help="""If > 0: limit the data to a subset of data_subset instances.""" )
parser.add_argument(
"""--overwrite_output_dir""" , action="""store_true""" , help="""Whether to overwrite data in output directory""" )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
parser.add_argument(
"""--dont_normalize_importance_by_layer""" , action="""store_true""" , help="""Don't normalize importance score by layers""" )
parser.add_argument(
"""--dont_normalize_global_importance""" , action="""store_true""" , help="""Don't normalize all importance scores between 0 and 1""" , )
parser.add_argument(
"""--try_masking""" , action="""store_true""" , help="""Whether to try to mask head until a threshold of accuracy.""" )
parser.add_argument(
"""--masking_threshold""" , default=0.9 , type=_lowercase , help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value).""" , )
parser.add_argument(
"""--masking_amount""" , default=0.1 , type=_lowercase , help="""Amount to heads to masking at each masking step.""" )
parser.add_argument("""--metric_name""" , default="""acc""" , type=_lowercase , help="""Metric to use for head masking.""" )
parser.add_argument(
"""--max_seq_length""" , default=1_2_8 , type=_lowercase , help=(
"""The maximum total input sequence length after WordPiece tokenization. \n"""
"""Sequences longer than this will be truncated, sequences shorter padded."""
) , )
parser.add_argument("""--batch_size""" , default=1 , type=_lowercase , help="""Batch size.""" )
parser.add_argument("""--seed""" , type=_lowercase , default=4_2 )
parser.add_argument("""--local_rank""" , type=_lowercase , default=-1 , help="""local_rank for distributed training on gpus""" )
parser.add_argument("""--no_cuda""" , action="""store_true""" , help="""Whether not to use CUDA when available""" )
parser.add_argument("""--server_ip""" , type=_lowercase , default="""""" , help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""" , type=_lowercase , default="""""" , help="""Can be used for distant debugging.""" )
UpperCAmelCase : List[str] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_lowercase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase : List[Any] = torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu""" )
UpperCAmelCase : Dict = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase : Tuple = torch.device("""cuda""" , args.local_rank )
UpperCAmelCase : str = 1
torch.distributed.init_process_group(backend="""nccl""" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("""device: {} n_gpu: {}, distributed: {}""".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase : Dict = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase : Tuple = nn.parallel.DistributedDataParallel(
_lowercase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=_lowercase )
elif args.n_gpu > 1:
UpperCAmelCase : Dict = nn.DataParallel(_lowercase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=_lowercase )
torch.save(_lowercase , os.path.join(args.output_dir , """run_args.bin""" ) )
logger.info("""Training/evaluation parameters %s""" , _lowercase )
# Prepare dataset
UpperCAmelCase : List[str] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase : str = (torch.from_numpy(_lowercase ),)
UpperCAmelCase : Union[str, Any] = TensorDataset(*_lowercase )
UpperCAmelCase : List[str] = RandomSampler(_lowercase )
UpperCAmelCase : Any = DataLoader(_lowercase , sampler=_lowercase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(_lowercase , _lowercase , _lowercase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase : Optional[Any] = mask_heads(_lowercase , _lowercase , _lowercase )
prune_heads(_lowercase , _lowercase , _lowercase , _lowercase )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class UpperCamelCase_ ( unittest.TestCase ):
def __init__( self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=4 , ) -> Union[str, Any]:
UpperCAmelCase : str = parent
UpperCAmelCase : Union[str, Any] = batch_size
UpperCAmelCase : Union[str, Any] = seq_length
UpperCAmelCase : Dict = is_training
UpperCAmelCase : Optional[int] = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : Tuple = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Tuple = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : List[Any] = hidden_act
UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase : Dict = attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : int = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : List[str] = num_choices
def _lowercase( self ) -> int:
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Tuple = None
if self.use_attention_mask:
UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Optional[Any] = None
if self.use_token_type_ids:
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : int = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple = config_and_inputs
UpperCAmelCase : List[Any] = True
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = True
lowercase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Any = FlaxRobertaModelTester(self )
@slow
def _lowercase( self ) -> Tuple:
for model_class_name in self.all_model_classes:
UpperCAmelCase : int = model_class_name.from_pretrained("""roberta-base""" , from_pt=A )
UpperCAmelCase : Optional[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 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.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = 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 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """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}''')
| 338 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class UpperCamelCase_ :
def __init__( self , A=None , A=None ) -> Dict:
# Input as list
UpperCAmelCase : Optional[int] = list(poly_a or [0] )[:]
UpperCAmelCase : Any = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
UpperCAmelCase : Any = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
UpperCAmelCase : Tuple = len(self.polyB )
# Add 0 to make lengths equal a power of 2
UpperCAmelCase : Union[str, Any] = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
UpperCAmelCase : Optional[int] = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
UpperCAmelCase : Optional[int] = self.__multiply()
def _lowercase( self , A ) -> str:
UpperCAmelCase : Tuple = [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
# Corner case
if len(A ) <= 1:
return dft[0]
#
UpperCAmelCase : Optional[int] = self.c_max_length // 2
while next_ncol > 0:
UpperCAmelCase : Dict = [[] for i in range(A )]
UpperCAmelCase : List[str] = self.root**next_ncol
# First half of next step
UpperCAmelCase : List[str] = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(A ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
UpperCAmelCase : int = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(A ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
UpperCAmelCase : Any = new_dft
UpperCAmelCase : int = next_ncol // 2
return dft[0]
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Tuple = self.__dft("""A""" )
UpperCAmelCase : Tuple = self.__dft("""B""" )
UpperCAmelCase : int = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
UpperCAmelCase : Optional[int] = 2
while next_ncol <= self.c_max_length:
UpperCAmelCase : int = [[] for i in range(A )]
UpperCAmelCase : Optional[Any] = self.root ** (next_ncol // 2)
UpperCAmelCase : List[Any] = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
UpperCAmelCase : str = new_inverse_c
next_ncol *= 2
# Unpack
UpperCAmelCase : Dict = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1J for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self ) -> str:
UpperCAmelCase : Optional[int] = """A = """ + """ + """.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
UpperCAmelCase : Optional[Any] = """B = """ + """ + """.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
UpperCAmelCase : Union[str, Any] = """A*B = """ + """ + """.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase_ :
def __init__( self , A , A=13 , A=32 , A=3 , A=4 , A=[10, 20, 30, 40] , A=[2, 2, 3, 2] , A=True , A=True , A=37 , A="gelu" , A=10 , A=0.0_2 , A=["stage2", "stage3", "stage4"] , A=3 , A=None , ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Union[str, Any] = batch_size
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : Tuple = num_channels
UpperCAmelCase : int = num_stages
UpperCAmelCase : Dict = hidden_sizes
UpperCAmelCase : str = depths
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : List[str] = initializer_range
UpperCAmelCase : List[Any] = out_features
UpperCAmelCase : Optional[Any] = num_labels
UpperCAmelCase : int = scope
UpperCAmelCase : int = num_stages
def _lowercase( self ) -> Dict:
UpperCAmelCase : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def _lowercase( self ) -> Tuple:
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def _lowercase( self ) -> List[str]:
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=A , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=A , loss_ignore_index=255 , num_labels=self.num_labels , )
def _lowercase( self , A , A , A ) -> Any:
UpperCAmelCase : Any = UperNetForSemanticSegmentation(config=A )
model.to(A )
model.eval()
UpperCAmelCase : Optional[int] = model(A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) : Optional[int] = config_and_inputs
UpperCAmelCase : Any = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def _lowercase( self ) -> Any:
UpperCAmelCase : List[str] = UperNetModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=37 )
def _lowercase( self ) -> Tuple:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowercase( self ) -> List[str]:
return
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Union[str, Any] = model_class(A )
UpperCAmelCase : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
@unittest.skip(reason="""UperNet does not use inputs_embeds""" )
def _lowercase( self ) -> Dict:
pass
@unittest.skip(reason="""UperNet does not support input and output embeddings""" )
def _lowercase( self ) -> Any:
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def _lowercase( self ) -> Dict:
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def _lowercase( self ) -> List[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def _lowercase( self ) -> List[Any]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _lowercase( self ) -> Any:
pass
def _lowercase( self ) -> Dict:
def check_hidden_states_output(A , A , A ):
UpperCAmelCase : str = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
UpperCAmelCase : str = model(**self._prepare_for_class(A , A ) )
UpperCAmelCase : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase , UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : List[Any] = True
check_hidden_states_output(A , A , A )
def _lowercase( self ) -> Dict:
UpperCAmelCase , UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[int] = _config_zero_init(A )
UpperCAmelCase : Dict = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(config=A )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason="""UperNet does not have tied weights""" )
def _lowercase( self ) -> Any:
pass
@slow
def _lowercase( self ) -> Tuple:
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : List[str] = UperNetForSemanticSegmentation.from_pretrained(A )
self.assertIsNotNone(A )
def __lowerCamelCase ( ) -> Any:
UpperCAmelCase : Tuple = hf_hub_download(
repo_id="""hf-internal-testing/fixtures_ade20k""" , repo_type="""dataset""" , filename="""ADE_val_00000001.jpg""" )
UpperCAmelCase : Any = Image.open(_lowercase ).convert("""RGB""" )
return image
@require_torch
@require_vision
@slow
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""" )
UpperCAmelCase : Dict = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""" ).to(A )
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : int = processor(images=A , return_tensors="""pt""" ).to(A )
with torch.no_grad():
UpperCAmelCase : Dict = model(**A )
UpperCAmelCase : str = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , A )
UpperCAmelCase : Optional[Any] = torch.tensor(
[[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , A , atol=1e-4 ) )
def _lowercase( self ) -> str:
UpperCAmelCase : Any = AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""" )
UpperCAmelCase : List[str] = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""" ).to(A )
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : int = processor(images=A , return_tensors="""pt""" ).to(A )
with torch.no_grad():
UpperCAmelCase : List[Any] = model(**A )
UpperCAmelCase : int = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , A )
UpperCAmelCase : Dict = torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , A , atol=1e-4 ) )
| 338 |
'''simple docstring'''
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 UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[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(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, 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(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[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(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : 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(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) 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 _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
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(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = 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
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = 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 _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = tempfile.mkdtemp()
UpperCAmelCase : Any = BlipImageProcessor()
UpperCAmelCase : Any = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
UpperCAmelCase : Any = BlipaProcessor(A , A )
processor.save_pretrained(self.tmpdirname )
def _lowercase( self , **A ) -> List[str]:
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).tokenizer
def _lowercase( self , **A ) -> str:
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).image_processor
def _lowercase( self ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def _lowercase( self ) -> int:
UpperCAmelCase : List[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase : List[Any] = [Image.fromarray(np.moveaxis(A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCAmelCase : str = self.get_image_processor(do_normalize=A , padding_value=1.0 )
UpperCAmelCase : Tuple = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A )
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[Any] = self.get_image_processor()
UpperCAmelCase : Optional[int] = self.get_tokenizer()
UpperCAmelCase : Optional[Any] = BlipaProcessor(tokenizer=A , image_processor=A )
UpperCAmelCase : Dict = self.prepare_image_inputs()
UpperCAmelCase : Tuple = image_processor(A , return_tensors="""np""" )
UpperCAmelCase : Tuple = processor(images=A , 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 _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = self.get_image_processor()
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : Optional[int] = BlipaProcessor(tokenizer=A , image_processor=A )
UpperCAmelCase : List[Any] = """lower newer"""
UpperCAmelCase : Optional[int] = processor(text=A )
UpperCAmelCase : Union[str, Any] = tokenizer(A , return_token_type_ids=A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _lowercase( self ) -> int:
UpperCAmelCase : List[str] = self.get_image_processor()
UpperCAmelCase : Dict = self.get_tokenizer()
UpperCAmelCase : Optional[int] = BlipaProcessor(tokenizer=A , image_processor=A )
UpperCAmelCase : int = """lower newer"""
UpperCAmelCase : str = self.prepare_image_inputs()
UpperCAmelCase : List[str] = processor(text=A , images=A )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(A ):
processor()
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = self.get_image_processor()
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : Dict = BlipaProcessor(tokenizer=A , image_processor=A )
UpperCAmelCase : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase : Optional[int] = processor.batch_decode(A )
UpperCAmelCase : Any = tokenizer.batch_decode(A )
self.assertListEqual(A , A )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : Dict = BlipaProcessor(tokenizer=A , image_processor=A )
UpperCAmelCase : Any = """lower newer"""
UpperCAmelCase : Any = self.prepare_image_inputs()
UpperCAmelCase : Tuple = processor(text=A , images=A )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> float:
return 1_0 - x * x
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_lowercase ) * equation(_lowercase ) >= 0:
raise ValueError("""Wrong space!""" )
UpperCAmelCase : Any = a
while (b - a) >= 0.01:
# Find middle point
UpperCAmelCase : str = (a + b) / 2
# Check if middle point is root
if equation(_lowercase ) == 0.0:
break
# Decide the side to repeat the steps
if equation(_lowercase ) * equation(_lowercase ) < 0:
UpperCAmelCase : Union[str, Any] = c
else:
UpperCAmelCase : Any = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 338 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
a : Optional[int] = ["""gpt2"""]
a : Optional[int] = """gpt2"""
if is_tf_available():
class UpperCamelCase_ ( tf.Module ):
def __init__( self , A ) -> Dict:
super().__init__()
UpperCAmelCase : Any = tokenizer
UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(A )
UpperCAmelCase : Any = TFGPTaLMHeadModel.from_config(A )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : List[str] = self.tokenizer(A )
UpperCAmelCase : List[Any] = tokenized["""input_ids"""].to_tensor()
UpperCAmelCase : Optional[Any] = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
UpperCAmelCase : List[str] = self.model(input_ids=A , attention_mask=A )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Dict:
super().setUp()
UpperCAmelCase : Dict = [GPTaTokenizer.from_pretrained(A ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
UpperCAmelCase : Optional[int] = [TFGPTaTokenizer.from_pretrained(A ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
UpperCAmelCase : Optional[int] = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
UpperCAmelCase : str = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _lowercase( self ) -> Optional[int]:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
UpperCAmelCase : Dict = tokenizer([test_inputs] , return_tensors="""tf""" )
UpperCAmelCase : str = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
UpperCAmelCase : Optional[Any] = python_outputs[key].numpy()
UpperCAmelCase : List[str] = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(A , tf.intaa ) == tf_outputs_values ) )
@slow
def _lowercase( self ) -> int:
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : Dict = tf.function(A )
for test_inputs in self.test_sentences:
UpperCAmelCase : Any = tf.constant(A )
UpperCAmelCase : int = compiled_tokenizer(A )
UpperCAmelCase : Optional[int] = tf_tokenizer(A )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _lowercase( self ) -> Tuple:
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : List[Any] = ModelToSave(tokenizer=A )
UpperCAmelCase : Optional[int] = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase : Dict = model.serving(A ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
UpperCAmelCase : str = Path(A ) / """saved.model"""
tf.saved_model.save(A , A , signatures={"""serving_default""": model.serving} )
UpperCAmelCase : Union[str, Any] = tf.saved_model.load(A )
UpperCAmelCase : Optional[Any] = loaded_model.signatures["""serving_default"""](A )["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def _lowercase( self ) -> Dict:
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : List[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase : Optional[int] = tf_tokenizer(A ) # Build model with some sample inputs
UpperCAmelCase : int = tf_tokenizer.get_config()
UpperCAmelCase : int = TFGPTaTokenizer.from_config(A )
UpperCAmelCase : Optional[Any] = model_from_config(A )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def _lowercase( self ) -> str:
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
UpperCAmelCase : List[Any] = 123123
for max_length in [3, 5, 1024]:
UpperCAmelCase : Tuple = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase : Union[str, Any] = tf_tokenizer(A , max_length=A )
UpperCAmelCase : List[str] = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase_ ( unittest.TestCase ):
@property
def _lowercase( self ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase( self ) -> int:
UpperCAmelCase : Optional[int] = self.dummy_uncond_unet
UpperCAmelCase : Union[str, Any] = KarrasVeScheduler()
UpperCAmelCase : Optional[Any] = KarrasVePipeline(unet=A , scheduler=A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase : Optional[Any] = torch.manual_seed(0 )
UpperCAmelCase : List[Any] = pipe(num_inference_steps=2 , generator=A , output_type="""numpy""" ).images
UpperCAmelCase : str = torch.manual_seed(0 )
UpperCAmelCase : int = pipe(num_inference_steps=2 , generator=A , output_type="""numpy""" , return_dict=A )[0]
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : List[str] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Tuple:
UpperCAmelCase : List[str] = """google/ncsnpp-celebahq-256"""
UpperCAmelCase : Tuple = UNetaDModel.from_pretrained(A )
UpperCAmelCase : Optional[Any] = KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] = KarrasVePipeline(unet=A , scheduler=A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase : str = torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe(num_inference_steps=20 , generator=A , output_type="""numpy""" ).images
UpperCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Dict = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'wav2vec2'
def __init__( self , A=32 , A=768 , A=12 , A=12 , A=3072 , A="gelu" , A=0.1 , A=0.1 , A=0.1 , A=0.0 , A=0.0 , A=0.1 , A=0.1 , A=0.0_2 , A=1e-5 , A="group" , A="gelu" , A=(512, 512, 512, 512, 512, 512, 512) , A=(5, 2, 2, 2, 2, 2, 2) , A=(10, 3, 3, 3, 3, 2, 2) , A=False , A=128 , A=16 , A=False , A=True , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=320 , A=2 , A=0.1 , A=100 , A=256 , A=256 , A=0.1 , A="sum" , A=False , A=False , A=256 , A=(512, 512, 512, 512, 1500) , A=(5, 3, 3, 1, 1) , A=(1, 2, 3, 1, 1) , A=512 , A=0 , A=1 , A=2 , A=False , A=3 , A=2 , A=3 , A=None , A=None , **A , ) -> str:
super().__init__(**A , pad_token_id=A , bos_token_id=A , eos_token_id=A )
UpperCAmelCase : Dict = hidden_size
UpperCAmelCase : str = feat_extract_norm
UpperCAmelCase : Any = feat_extract_activation
UpperCAmelCase : List[Any] = list(A )
UpperCAmelCase : str = list(A )
UpperCAmelCase : Optional[Any] = list(A )
UpperCAmelCase : Union[str, Any] = conv_bias
UpperCAmelCase : List[Any] = num_conv_pos_embeddings
UpperCAmelCase : str = num_conv_pos_embedding_groups
UpperCAmelCase : Optional[Any] = len(self.conv_dim )
UpperCAmelCase : Any = num_hidden_layers
UpperCAmelCase : Union[str, Any] = intermediate_size
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : Tuple = num_attention_heads
UpperCAmelCase : Union[str, Any] = hidden_dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Union[str, Any] = activation_dropout
UpperCAmelCase : Any = feat_proj_dropout
UpperCAmelCase : Tuple = final_dropout
UpperCAmelCase : Tuple = layerdrop
UpperCAmelCase : Any = layer_norm_eps
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Any = vocab_size
UpperCAmelCase : Tuple = do_stable_layer_norm
UpperCAmelCase : str = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : List[Any] = apply_spec_augment
UpperCAmelCase : List[str] = mask_time_prob
UpperCAmelCase : Any = mask_time_length
UpperCAmelCase : List[Any] = mask_time_min_masks
UpperCAmelCase : Optional[int] = mask_feature_prob
UpperCAmelCase : Union[str, Any] = mask_feature_length
UpperCAmelCase : List[Any] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
UpperCAmelCase : List[str] = num_codevectors_per_group
UpperCAmelCase : List[str] = num_codevector_groups
UpperCAmelCase : List[str] = contrastive_logits_temperature
UpperCAmelCase : Optional[int] = feat_quantizer_dropout
UpperCAmelCase : List[Any] = num_negatives
UpperCAmelCase : Optional[int] = codevector_dim
UpperCAmelCase : int = proj_codevector_dim
UpperCAmelCase : Optional[Any] = diversity_loss_weight
# ctc loss
UpperCAmelCase : Tuple = ctc_loss_reduction
UpperCAmelCase : str = ctc_zero_infinity
# adapter
UpperCAmelCase : int = add_adapter
UpperCAmelCase : str = adapter_kernel_size
UpperCAmelCase : Tuple = adapter_stride
UpperCAmelCase : Dict = num_adapter_layers
UpperCAmelCase : Tuple = output_hidden_size or hidden_size
UpperCAmelCase : Optional[Any] = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCAmelCase : Optional[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = list(A )
UpperCAmelCase : Union[str, Any] = list(A )
UpperCAmelCase : int = list(A )
UpperCAmelCase : Any = xvector_output_dim
@property
def _lowercase( self ) -> Tuple:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> int:
try:
UpperCAmelCase : List[str] = int(_lowercase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
UpperCAmelCase : Dict = 2
UpperCAmelCase : Dict = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
UpperCAmelCase : int = i
while n % i == 0:
UpperCAmelCase : Optional[int] = n // i
i += 1
return int(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a : Any = 1.054571817E-34 # unit of ℏ : J * s
a : Any = 3E8 # unit of c : m * s^-1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> dict[str, float]:
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
UpperCAmelCase : Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
UpperCAmelCase : Tuple = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
UpperCAmelCase : Tuple = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {
"""google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
"""google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
"""google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit_text_model'
def __init__( self , A=49408 , A=512 , A=2048 , A=12 , A=8 , A=16 , A="quick_gelu" , A=1e-5 , A=0.0 , A=0.0_2 , A=1.0 , A=0 , A=49406 , A=49407 , **A , ) -> Dict:
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
UpperCAmelCase : str = vocab_size
UpperCAmelCase : List[Any] = hidden_size
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : List[str] = layer_norm_eps
UpperCAmelCase : str = attention_dropout
UpperCAmelCase : str = initializer_range
UpperCAmelCase : Optional[int] = initializer_factor
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : int = cls.get_config_dict(A , **A )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
UpperCAmelCase : Dict = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit_vision_model'
def __init__( self , A=768 , A=3072 , A=12 , A=12 , A=3 , A=768 , A=32 , A="quick_gelu" , A=1e-5 , A=0.0 , A=0.0_2 , A=1.0 , **A , ) -> Optional[int]:
super().__init__(**A )
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : int = intermediate_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : str = num_channels
UpperCAmelCase : Any = image_size
UpperCAmelCase : Tuple = patch_size
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : Any = layer_norm_eps
UpperCAmelCase : int = attention_dropout
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : List[Any] = initializer_factor
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = cls.get_config_dict(A , **A )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
UpperCAmelCase : List[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit'
lowercase = True
def __init__( self , A=None , A=None , A=512 , A=2.6_5_9_2 , A=True , **A , ) -> int:
super().__init__(**A )
if text_config is None:
UpperCAmelCase : str = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
UpperCAmelCase : List[Any] = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
UpperCAmelCase : int = OwlViTTextConfig(**A )
UpperCAmelCase : Optional[Any] = OwlViTVisionConfig(**A )
UpperCAmelCase : Union[str, Any] = projection_dim
UpperCAmelCase : int = logit_scale_init_value
UpperCAmelCase : Optional[Any] = return_dict
UpperCAmelCase : Any = 1.0
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : List[str] = cls.get_config_dict(A , **A )
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
@classmethod
def _lowercase( cls , A , A , **A ) -> Optional[Any]:
UpperCAmelCase : int = {}
UpperCAmelCase : int = text_config
UpperCAmelCase : Optional[Any] = vision_config
return cls.from_dict(A , **A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase : List[str] = self.text_config.to_dict()
UpperCAmelCase : List[str] = self.vision_config.to_dict()
UpperCAmelCase : List[str] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
def _lowercase( self , A , A = -1 , A = -1 , A = None , ) -> Mapping[str, Any]:
UpperCAmelCase : Any = super().generate_dummy_inputs(
processor.tokenizer , batch_size=A , seq_length=A , framework=A )
UpperCAmelCase : Optional[int] = super().generate_dummy_inputs(
processor.image_processor , batch_size=A , framework=A )
return {**text_input_dict, **image_input_dict}
@property
def _lowercase( self ) -> int:
return 14
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , ) -> str:
if config_name_or_path is None:
UpperCAmelCase : List[str] = """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""
if generator_tokenizer_name_or_path is None:
UpperCAmelCase : List[Any] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCAmelCase : Union[str, Any] = question_encoder_name_or_path
UpperCAmelCase : Tuple = RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration
# Save model.
UpperCAmelCase : Optional[int] = RagConfig.from_pretrained(_lowercase )
UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained(_lowercase )
UpperCAmelCase : int = AutoConfig.from_pretrained(_lowercase )
UpperCAmelCase : Tuple = gen_config
UpperCAmelCase : Optional[int] = question_encoder_config
UpperCAmelCase : Optional[Any] = model_class.from_pretrained_question_encoder_generator(
_lowercase , _lowercase , config=_lowercase )
rag_model.save_pretrained(_lowercase )
# Sanity check.
model_class.from_pretrained(_lowercase )
# Save tokenizers.
UpperCAmelCase : Any = AutoTokenizer.from_pretrained(_lowercase )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
UpperCAmelCase : int = AutoTokenizer.from_pretrained(_lowercase )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
a : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
a : str = parser.parse_args()
a : int = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> list:
UpperCAmelCase : List[str] = [True] * n
UpperCAmelCase : Tuple = False
UpperCAmelCase : List[str] = False
UpperCAmelCase : List[str] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
UpperCAmelCase : Dict = i * 2
while index < n:
UpperCAmelCase : Tuple = False
UpperCAmelCase : Dict = index + i
UpperCAmelCase : Union[str, Any] = [2]
for i in range(3 , _lowercase , 2 ):
if is_prime[i]:
primes.append(_lowercase )
return primes
def __lowerCamelCase ( _lowercase = 9_9_9_9_6_6_6_6_3_3_3_3 ) -> int:
UpperCAmelCase : Dict = math.floor(math.sqrt(_lowercase ) ) + 1_0_0
UpperCAmelCase : List[str] = prime_sieve(_lowercase )
UpperCAmelCase : Dict = 0
UpperCAmelCase : List[str] = 0
UpperCAmelCase : str = primes[prime_index]
while (last_prime**2) <= limit:
UpperCAmelCase : Any = primes[prime_index + 1]
UpperCAmelCase : Any = last_prime**2
UpperCAmelCase : List[Any] = next_prime**2
# Get numbers divisible by lps(current)
UpperCAmelCase : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
UpperCAmelCase : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
UpperCAmelCase : Optional[int] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
UpperCAmelCase : List[str] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCamelCase_ ( datasets.BeamBasedBuilder ):
def _lowercase( self ) -> Any:
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) , supervised_keys=A , )
def _lowercase( self , A , A ) -> Tuple:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def _lowercase( self , A , A ) -> Optional[Any]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A )
class UpperCamelCase_ ( datasets.BeamBasedBuilder ):
def _lowercase( self ) -> Union[str, Any]:
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) , supervised_keys=A , )
def _lowercase( self , A , A ) -> Any:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def _lowercase( self , A , A ) -> Union[str, Any]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A )
def __lowerCamelCase ( ) -> Optional[Any]:
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
def __lowerCamelCase ( ) -> int:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
class UpperCamelCase_ ( __magic_name__ ):
@require_beam
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : int = DummyBeamDataset(cache_dir=A , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
UpperCAmelCase : List[Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , A )
self.assertDictEqual(dset["""train"""][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def _lowercase( self ) -> Dict:
import apache_beam as beam
UpperCAmelCase : Optional[int] = beam.io.parquetio.WriteToParquet
UpperCAmelCase : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : Dict = DummyBeamDataset(cache_dir=A , beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
UpperCAmelCase : Optional[int] = partial(A , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
UpperCAmelCase : Optional[Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , A )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) , sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def _lowercase( self ) -> Any:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : Optional[Any] = DummyBeamDataset(cache_dir=A )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def _lowercase( self ) -> str:
UpperCAmelCase : Optional[Any] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : Any = NestedBeamDataset(cache_dir=A , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
UpperCAmelCase : List[str] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , A )
self.assertDictEqual(dset["""train"""][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = RobertaTokenizer
lowercase = RobertaTokenizerFast
lowercase = True
lowercase = {'cls_token': '<s>'}
def _lowercase( self ) -> int:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase : int = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
UpperCAmelCase : List[Any] = dict(zip(A , range(len(A ) ) ) )
UpperCAmelCase : Union[str, Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
UpperCAmelCase : Optional[int] = {"""unk_token""": """<unk>"""}
UpperCAmelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def _lowercase( self , **A ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **A )
def _lowercase( self , **A ) -> Any:
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **A )
def _lowercase( self , A ) -> List[str]:
UpperCAmelCase : int = """lower newer"""
UpperCAmelCase : List[str] = """lower newer"""
return input_text, output_text
def _lowercase( self ) -> Any:
UpperCAmelCase : int = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase : Any = """lower newer"""
UpperCAmelCase : Optional[int] = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
UpperCAmelCase : Union[str, Any] = tokenizer.tokenize(A ) # , add_prefix_space=True)
self.assertListEqual(A , A )
UpperCAmelCase : str = tokens + [tokenizer.unk_token]
UpperCAmelCase : List[str] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("""Hello world!""" , add_special_tokens=A ) , [0, 31414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("""Hello world! cécé herlolip 418""" , add_special_tokens=A ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , )
@slow
def _lowercase( self ) -> int:
UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained("""roberta-base""" )
UpperCAmelCase : Tuple = tokenizer.encode("""sequence builders""" , add_special_tokens=A )
UpperCAmelCase : Optional[Any] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A )
UpperCAmelCase : Tuple = tokenizer.encode(
"""sequence builders""" , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : Dict = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : Any = tokenizer.build_inputs_with_special_tokens(A )
UpperCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[str] = self.get_tokenizer()
UpperCAmelCase : List[str] = """Encode this sequence."""
UpperCAmelCase : Optional[int] = tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]
# Testing encoder arguments
UpperCAmelCase : str = tokenizer.encode(A , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : Tuple = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(A , A )
UpperCAmelCase : Union[str, Any] = tokenizer.encode(A , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(A , A )
tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
UpperCAmelCase : str = tokenizer.encode(A , add_special_tokens=A )
UpperCAmelCase : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(A , A )
# Testing spaces after special tokens
UpperCAmelCase : int = """<mask>"""
tokenizer.add_special_tokens(
{"""mask_token""": AddedToken(A , lstrip=A , rstrip=A )} ) # mask token has a left space
UpperCAmelCase : List[Any] = tokenizer.convert_tokens_to_ids(A )
UpperCAmelCase : Union[str, Any] = """Encode <mask> sequence"""
UpperCAmelCase : Any = """Encode <mask>sequence"""
UpperCAmelCase : Tuple = tokenizer.encode(A )
UpperCAmelCase : Dict = encoded.index(A )
UpperCAmelCase : Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(A , A )
UpperCAmelCase : List[Any] = tokenizer.encode(A )
UpperCAmelCase : List[Any] = encoded.index(A )
UpperCAmelCase : Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(A , A )
def _lowercase( self ) -> List[str]:
pass
def _lowercase( self ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCAmelCase : Any = self.rust_tokenizer_class.from_pretrained(A , **A )
UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained(A , **A )
UpperCAmelCase : str = """A, <mask> AllenNLP sentence."""
UpperCAmelCase : List[Any] = tokenizer_r.encode_plus(A , add_special_tokens=A , return_token_type_ids=A )
UpperCAmelCase : Dict = tokenizer_p.encode_plus(A , add_special_tokens=A , return_token_type_ids=A )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) , sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) , sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) , )
UpperCAmelCase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
UpperCAmelCase : int = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
A , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
A , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
def _lowercase( self ) -> Optional[int]:
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : List[str] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
UpperCAmelCase : Tuple = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] , A )
self.assertEqual(post_processor_state["""add_prefix_space"""] , A )
self.assertEqual(post_processor_state["""trim_offsets"""] , A )
def _lowercase( self ) -> Dict:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCAmelCase : Dict = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
UpperCAmelCase : str = f'''{text_of_1_token} {text_of_1_token}'''
UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : Optional[Any] = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ) + 1, len(A ) + 1 + len(A )) , )
UpperCAmelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : Optional[Any] = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ) + 1, len(A ) + 1 + len(A )) , )
UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : Union[str, Any] = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ), len(A ) + 1 + len(A )) , )
UpperCAmelCase : str = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : List[str] = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ), len(A ) + 1 + len(A )) , )
UpperCAmelCase : str = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
UpperCAmelCase : Any = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : Optional[Any] = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ) + 1, 1 + len(A ) + 1 + len(A )) , )
UpperCAmelCase : Tuple = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : int = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ), 1 + len(A ) + 1 + len(A )) , )
UpperCAmelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
UpperCAmelCase : Dict = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ), 1 + len(A ) + 1 + len(A )) , )
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
a : Tuple = logging.get_logger(__name__)
a : Tuple = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCamelCase_ :
lowercase = field(
default=__magic_name__ , metadata={'help': 'Model type selected in the list: ' + ', '.join(__magic_name__ )} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
lowercase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowercase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
lowercase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
lowercase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
lowercase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowercase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowercase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
lowercase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'train'
lowercase = 'dev'
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 42
def __init__( self , A , A , A = None , A = Split.train , A = False , A = None , A = "pt" , ) -> int:
UpperCAmelCase : List[str] = args
UpperCAmelCase : Union[str, Any] = is_language_sensitive
UpperCAmelCase : List[str] = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(A , A ):
try:
UpperCAmelCase : str = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
UpperCAmelCase : Union[str, Any] = mode
# Load data features from cache or dataset file
UpperCAmelCase : Union[str, Any] = """v2""" if args.version_2_with_negative else """v1"""
UpperCAmelCase : Optional[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCAmelCase : int = cached_features_file + """.lock"""
with FileLock(A ):
if os.path.exists(A ) and not args.overwrite_cache:
UpperCAmelCase : Optional[int] = time.time()
UpperCAmelCase : str = torch.load(A )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
UpperCAmelCase : Dict = self.old_features["""features"""]
UpperCAmelCase : Dict = self.old_features.get("""dataset""" , A )
UpperCAmelCase : Dict = self.old_features.get("""examples""" , A )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
""" future run""" )
else:
if mode == Split.dev:
UpperCAmelCase : int = self.processor.get_dev_examples(args.data_dir )
else:
UpperCAmelCase : str = self.processor.get_train_examples(args.data_dir )
UpperCAmelCase , UpperCAmelCase : Dict = squad_convert_examples_to_features(
examples=self.examples , tokenizer=A , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=A , )
UpperCAmelCase : Any = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , A , )
# ^ 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 ) -> Dict:
return len(self.features )
def __getitem__( self , A ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
UpperCAmelCase : Any = self.features[i]
UpperCAmelCase : Optional[Any] = torch.tensor(feature.input_ids , dtype=torch.long )
UpperCAmelCase : Any = torch.tensor(feature.attention_mask , dtype=torch.long )
UpperCAmelCase : int = torch.tensor(feature.token_type_ids , dtype=torch.long )
UpperCAmelCase : Dict = torch.tensor(feature.cls_index , dtype=torch.long )
UpperCAmelCase : Optional[int] = torch.tensor(feature.p_mask , dtype=torch.float )
UpperCAmelCase : Optional[Any] = torch.tensor(feature.is_impossible , dtype=torch.float )
UpperCAmelCase : Union[str, Any] = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
UpperCAmelCase : Tuple = torch.tensor(feature.start_position , dtype=torch.long )
UpperCAmelCase : Any = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
a : Optional[int] = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ :
lowercase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowercase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
@dataclass
class UpperCamelCase_ :
lowercase = field(default=__magic_name__ , metadata={'help': 'The input training data file (a text file).'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'The maximum total input sequence length after tokenization. If passed, sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'Whether to pad all samples to the maximum sentence length. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch. More '
'efficient on GPU but very bad for TPU.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def _lowercase( self ) -> Optional[Any]:
if self.train_file is not None:
UpperCAmelCase : Optional[Any] = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
UpperCAmelCase : Tuple = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class UpperCamelCase_ :
lowercase = 42
lowercase = True
lowercase = None
lowercase = None
def __call__( self , A ) -> List[str]:
UpperCAmelCase : str = """label""" if """label""" in features[0].keys() else """labels"""
UpperCAmelCase : str = [feature.pop(A ) for feature in features]
UpperCAmelCase : Tuple = len(A )
UpperCAmelCase : List[Any] = len(features[0]["""input_ids"""] )
UpperCAmelCase : Optional[Any] = [
[{k: v[i] for k, v in feature.items()} for i in range(A )] for feature in features
]
UpperCAmelCase : int = list(chain(*A ) )
UpperCAmelCase : Dict = self.tokenizer.pad(
A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
# Un-flatten
UpperCAmelCase : List[Any] = {k: v.view(A , A , -1 ) for k, v in batch.items()}
# Add back labels
UpperCAmelCase : Tuple = torch.tensor(A , dtype=torch.intaa )
return batch
def __lowerCamelCase ( ) -> Optional[int]:
# 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.
UpperCAmelCase : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : 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_swag""" , _lowercase , _lowercase )
# 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()
UpperCAmelCase : Optional[Any] = training_args.get_process_log_level()
logger.setLevel(_lowercase )
datasets.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.set_verbosity(_lowercase )
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.
UpperCAmelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase : str = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
UpperCAmelCase : List[Any] = {}
if data_args.train_file is not None:
UpperCAmelCase : Union[str, Any] = data_args.train_file
if data_args.validation_file is not None:
UpperCAmelCase : Dict = data_args.validation_file
UpperCAmelCase : int = data_args.train_file.split(""".""" )[-1]
UpperCAmelCase : Any = load_dataset(
_lowercase , data_files=_lowercase , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
UpperCAmelCase : Union[str, Any] = load_dataset(
"""swag""" , """regular""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase : Optional[Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
UpperCAmelCase : List[str] = [F'''ending{i}''' for i in range(4 )]
UpperCAmelCase : Optional[int] = """sent1"""
UpperCAmelCase : Optional[int] = """sent2"""
if data_args.max_seq_length is None:
UpperCAmelCase : str = tokenizer.model_max_length
if max_seq_length > 1_0_2_4:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
UpperCAmelCase : Any = 1_0_2_4
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
UpperCAmelCase : int = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(_lowercase ):
UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
UpperCAmelCase : Any = examples[question_header_name]
UpperCAmelCase : List[Any] = [
[F'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(_lowercase )
]
# Flatten out
UpperCAmelCase : List[str] = list(chain(*_lowercase ) )
UpperCAmelCase : Tuple = list(chain(*_lowercase ) )
# Tokenize
UpperCAmelCase : Dict = tokenizer(
_lowercase , _lowercase , truncation=_lowercase , max_length=_lowercase , padding="""max_length""" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(_lowercase ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
UpperCAmelCase : List[str] = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
UpperCAmelCase : int = min(len(_lowercase ) , data_args.max_train_samples )
UpperCAmelCase : str = train_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
UpperCAmelCase : int = train_dataset.map(
_lowercase , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
UpperCAmelCase : Dict = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
UpperCAmelCase : Optional[int] = min(len(_lowercase ) , data_args.max_eval_samples )
UpperCAmelCase : Optional[int] = eval_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
UpperCAmelCase : Dict = eval_dataset.map(
_lowercase , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
UpperCAmelCase : List[Any] = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=_lowercase , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(_lowercase ):
UpperCAmelCase , UpperCAmelCase : Tuple = eval_predictions
UpperCAmelCase : str = np.argmax(_lowercase , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
UpperCAmelCase : Any = Trainer(
model=_lowercase , args=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=_lowercase , data_collator=_lowercase , compute_metrics=_lowercase , )
# Training
if training_args.do_train:
UpperCAmelCase : int = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase : Tuple = last_checkpoint
UpperCAmelCase : List[str] = trainer.train(resume_from_checkpoint=_lowercase )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCAmelCase : Tuple = train_result.metrics
UpperCAmelCase : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
UpperCAmelCase : Optional[int] = min(_lowercase , len(_lowercase ) )
trainer.log_metrics("""train""" , _lowercase )
trainer.save_metrics("""train""" , _lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCAmelCase : Dict = trainer.evaluate()
UpperCAmelCase : Dict = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowercase )
UpperCAmelCase : Dict = min(_lowercase , len(_lowercase ) )
trainer.log_metrics("""eval""" , _lowercase )
trainer.save_metrics("""eval""" , _lowercase )
UpperCAmelCase : Any = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """multiple-choice""",
"""dataset_tags""": """swag""",
"""dataset_args""": """regular""",
"""dataset""": """SWAG""",
"""language""": """en""",
}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowercase )
else:
trainer.create_model_card(**_lowercase )
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a : List[str] = logging.get_logger(__name__)
a : int = {"""vocab_file""": """spiece.model"""}
a : Dict = {
"""vocab_file""": {
"""AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
}
}
a : Optional[Any] = {
"""AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self , A , A=False , A=False , A=False , A=None , A=None , A=None , A=None , A = None , **A , ) -> None:
UpperCAmelCase : Any = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase : Dict = kwargs.get("""name_or_path""" )
if name_or_path is None:
logger.warning(
"""name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
""" you are testing the model, this can safely be ignored""" )
UpperCAmelCase : int = """None"""
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase : Optional[int] = """<|endoftext|>""" if eos_token is None else eos_token
UpperCAmelCase : str = """<unk>""" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase : Optional[Any] = unk_token if pad_token is None else pad_token
UpperCAmelCase : Tuple = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase : List[str] = """<pad>""" if pad_token is None else pad_token
UpperCAmelCase : Optional[int] = """<s>""" if bos_token is None else bos_token
super().__init__(
do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , pad_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
UpperCAmelCase : str = do_lower_case
UpperCAmelCase : Optional[int] = remove_space
UpperCAmelCase : Optional[Any] = keep_accents
UpperCAmelCase : str = vocab_file
UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase : Optional[int] = {""" """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """""", """"""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase : Any = re.compile(
f'''[{''.join(map(A , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]''' )
def __getstate__( self ) -> int:
UpperCAmelCase : Dict = self.__dict__.copy()
UpperCAmelCase : Any = None
return state
def __setstate__( self , A ) -> int:
UpperCAmelCase : List[str] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase : Union[str, Any] = {}
UpperCAmelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _lowercase( self ) -> int:
return len(self.sp_model )
def _lowercase( self , A ) -> str:
UpperCAmelCase : List[str] = self.non_printing_characters_re.sub("""""" , A )
# Normalize whitespaces
UpperCAmelCase : List[str] = """""".join([char if char not in self.whitespaces else """ """ for char in text] )
# NFC Unicode normalization
UpperCAmelCase : List[str] = unicodedata.normalize("""NFC""" , A )
return text
def _lowercase( self , A , **A ) -> List[str]:
UpperCAmelCase : List[Any] = self.preprocess_text(A )
return self.sp_model.encode(A , out_type=A )
def _lowercase( self , A ) -> int:
return self.sp_model.PieceToId(A )
def _lowercase( self , A ) -> str:
return self.sp_model.IdToPiece(A )
@staticmethod
def _lowercase( A ) -> str:
return out_string
def _lowercase( self , A ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : str = """"""
UpperCAmelCase : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
UpperCAmelCase : Dict = True
UpperCAmelCase : Optional[Any] = []
else:
current_sub_tokens.append(A )
UpperCAmelCase : List[Any] = False
out_string += self.sp_model.decode(A )
return out_string
def _lowercase( self ) -> Dict[str, int]:
UpperCAmelCase : Tuple = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowercase( self , A , A = None ) -> Tuple[str]:
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : str = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , """wb""" ) as fi:
UpperCAmelCase : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def _lowercase( self , A , A = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(A , A ):
UpperCAmelCase : Tuple = self.preprocess_text(A )
UpperCAmelCase : Dict = self.sp_model.encode(A )
else:
UpperCAmelCase : Tuple = [self.preprocess_text(A ) for t in text]
UpperCAmelCase : Union[str, Any] = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase : Union[str, Any] = torch.tensor(A )
return token_ids
def _lowercase( self , A ) -> str:
return self.sp_model.decode(A )
def _lowercase( self , A ) -> List[int]:
UpperCAmelCase : Any = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
UpperCAmelCase : Dict = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'upernet'
def __init__( self , A=None , A=512 , A=0.0_2 , A=[1, 2, 3, 6] , A=True , A=0.4 , A=384 , A=256 , A=1 , A=False , A=255 , **A , ) -> Union[str, Any]:
super().__init__(**A )
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Union[str, Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Optional[Any] = backbone_config.get("""model_type""" )
UpperCAmelCase : List[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[str] = config_class.from_dict(A )
UpperCAmelCase : int = backbone_config
UpperCAmelCase : Tuple = hidden_size
UpperCAmelCase : List[str] = initializer_range
UpperCAmelCase : List[Any] = pool_scales
UpperCAmelCase : List[str] = use_auxiliary_head
UpperCAmelCase : Any = auxiliary_loss_weight
UpperCAmelCase : Dict = auxiliary_in_channels
UpperCAmelCase : Tuple = auxiliary_channels
UpperCAmelCase : List[Any] = auxiliary_num_convs
UpperCAmelCase : Union[str, Any] = auxiliary_concat_input
UpperCAmelCase : Union[str, Any] = loss_ignore_index
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase : Dict = self.backbone_config.to_dict()
UpperCAmelCase : int = self.__class__.model_type
return output
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
import math
a : Dict = 1_0
a : List[Any] = 7
a : Optional[Any] = BALLS_PER_COLOUR * NUM_COLOURS
def __lowerCamelCase ( _lowercase = 2_0 ) -> str:
UpperCAmelCase : Union[str, Any] = math.comb(_lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _lowercase )
UpperCAmelCase : str = NUM_COLOURS * (1 - missing_colour / total)
return F'''{result:.9f}'''
if __name__ == "__main__":
print(solution(2_0))
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
a : Optional[Any] = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class UpperCamelCase_ :
lowercase = PegasusConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=5 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Tuple:
UpperCAmelCase : List[str] = parent
UpperCAmelCase : Optional[Any] = batch_size
UpperCAmelCase : List[str] = seq_length
UpperCAmelCase : Any = is_training
UpperCAmelCase : str = use_labels
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : Any = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Dict = max_position_embeddings
UpperCAmelCase : List[str] = eos_token_id
UpperCAmelCase : Optional[Any] = pad_token_id
UpperCAmelCase : Optional[int] = bos_token_id
def _lowercase( self ) -> List[str]:
UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
UpperCAmelCase : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : int = np.concatenate([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] = prepare_pegasus_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A , A ) -> Optional[Any]:
UpperCAmelCase : Tuple = 20
UpperCAmelCase : Tuple = model_class_name(A )
UpperCAmelCase : List[str] = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase , UpperCAmelCase : Tuple = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase : Tuple = model.init_cache(decoder_input_ids.shape[0] , A , A )
UpperCAmelCase : Optional[Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
UpperCAmelCase : Optional[Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
UpperCAmelCase : Union[str, Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCAmelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, -1:] , A , decoder_attention_mask=A , past_key_values=outputs_cache.past_key_values , decoder_position_ids=A , )
UpperCAmelCase : List[Any] = model.decode(A , A )
UpperCAmelCase : Union[str, Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def _lowercase( self , A , A , A ) -> Dict:
UpperCAmelCase : Optional[int] = 20
UpperCAmelCase : List[Any] = model_class_name(A )
UpperCAmelCase : Optional[int] = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase : List[str] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCAmelCase : int = model.init_cache(decoder_input_ids.shape[0] , A , A )
UpperCAmelCase : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase : int = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
UpperCAmelCase : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCAmelCase : str = model.decode(
decoder_input_ids[:, -1:] , A , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=A , decoder_position_ids=A , )
UpperCAmelCase : str = model.decode(A , A , decoder_attention_mask=A )
UpperCAmelCase : List[str] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , ) -> Tuple:
if attention_mask is None:
UpperCAmelCase : List[str] = np.not_equal(_lowercase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Dict = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase = True
lowercase = False
lowercase = False
lowercase = False
def _lowercase( self ) -> int:
UpperCAmelCase : Any = FlaxPegasusModelTester(self )
UpperCAmelCase : Dict = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Any:
self.config_tester.run_common_tests()
def _lowercase( self ) -> List[Any]:
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A , A , A )
def _lowercase( self ) -> int:
UpperCAmelCase , UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A , A , A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase : Optional[int] = self._prepare_for_class(A , A )
UpperCAmelCase : Tuple = model_class(A )
@jax.jit
def encode_jitted(A , A=None , **A ):
return model.encode(input_ids=A , attention_mask=A )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase : Tuple = encode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase : Union[str, Any] = encode_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
def _lowercase( self ) -> str:
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase : List[Any] = model_class(A )
UpperCAmelCase : Tuple = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
UpperCAmelCase : List[Any] = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(A , A , A ):
return model.decode(
decoder_input_ids=A , decoder_attention_mask=A , encoder_outputs=A , )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase : Union[str, Any] = decode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase : Union[str, Any] = decode_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _lowercase( self ) -> str:
for model_class_name in self.all_model_classes:
UpperCAmelCase : Tuple = model_class_name.from_pretrained("""google/pegasus-large""" , from_pt=A )
UpperCAmelCase : Union[str, Any] = np.ones((1, 1) )
UpperCAmelCase : Optional[Any] = model(A )
self.assertIsNotNone(A )
@slow
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase : List[str] = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase : Optional[int] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
UpperCAmelCase : Optional[int] = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
UpperCAmelCase : Dict = tokenizer(A , return_tensors="""np""" , truncation=A , max_length=512 , padding=A )
UpperCAmelCase : Any = model.generate(**A , num_beams=2 ).sequences
UpperCAmelCase : int = tokenizer.batch_decode(A , skip_special_tokens=A )
assert tgt_text == decoded
| 338 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCamelCase ( _lowercase , _lowercase ) -> Tuple:
assert isinstance(_lowercase , _lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : Optional[Any] = tmp_path / """cache"""
UpperCAmelCase : Optional[int] = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Optional[int] = TextDatasetReader(_lowercase , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_text_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> List[str]:
UpperCAmelCase : Dict = tmp_path / """cache"""
UpperCAmelCase : Tuple = {"""text""": """string"""}
UpperCAmelCase : List[Any] = features.copy() if features else default_expected_features
UpperCAmelCase : str = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : List[Any] = TextDatasetReader(_lowercase , features=_lowercase , cache_dir=_lowercase ).read()
_check_text_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Any = tmp_path / """cache"""
UpperCAmelCase : Optional[Any] = {"""text""": """string"""}
UpperCAmelCase : Tuple = TextDatasetReader(_lowercase , cache_dir=_lowercase , split=_lowercase ).read()
_check_text_dataset(_lowercase , _lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
if issubclass(_lowercase , _lowercase ):
UpperCAmelCase : Union[str, Any] = text_path
elif issubclass(_lowercase , _lowercase ):
UpperCAmelCase : int = [text_path]
UpperCAmelCase : Dict = tmp_path / """cache"""
UpperCAmelCase : Optional[Any] = {"""text""": """string"""}
UpperCAmelCase : Optional[int] = TextDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_text_dataset(_lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=("train",) ) -> Dict:
assert isinstance(_lowercase , _lowercase )
for split in splits:
UpperCAmelCase : Dict = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = tmp_path / """cache"""
UpperCAmelCase : Tuple = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Tuple = TextDatasetReader({"""train""": text_path} , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_text_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = tmp_path / """cache"""
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
UpperCAmelCase : Optional[Any] = {"""text""": """string"""}
UpperCAmelCase : List[str] = features.copy() if features else default_expected_features
UpperCAmelCase : int = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : str = TextDatasetReader({"""train""": text_path} , features=_lowercase , cache_dir=_lowercase ).read()
_check_text_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
if split:
UpperCAmelCase : Tuple = {split: text_path}
else:
UpperCAmelCase : List[str] = """train"""
UpperCAmelCase : int = {"""train""": text_path, """test""": text_path}
UpperCAmelCase : Union[str, Any] = tmp_path / """cache"""
UpperCAmelCase : int = {"""text""": """string"""}
UpperCAmelCase : Optional[Any] = TextDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_text_datasetdict(_lowercase , _lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 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.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = 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 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """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}''')
| 338 | 1 |
'''simple docstring'''
import random
from typing import Any
def __lowerCamelCase ( _lowercase ) -> list[Any]:
for _ in range(len(_lowercase ) ):
UpperCAmelCase : Union[str, Any] = random.randint(0 , len(_lowercase ) - 1 )
UpperCAmelCase : int = random.randint(0 , len(_lowercase ) - 1 )
UpperCAmelCase , UpperCAmelCase : List[str] = data[b], data[a]
return data
if __name__ == "__main__":
a : Optional[int] = [0, 1, 2, 3, 4, 5, 6, 7]
a : Dict = ["""python""", """says""", """hello""", """!"""]
print("""Fisher-Yates Shuffle:""")
print("""List""", integers, strings)
print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
UpperCAmelCase : List[str] = sorted(string.lower() )
return len(_lowercase ) == len(set(_lowercase ) )
if __name__ == "__main__":
a : List[Any] = input("""Enter a string """).strip()
a : Dict = is_isogram(input_str)
print(F'''{input_str} is {'an' if isogram else 'not an'} isogram.''')
| 338 |
'''simple docstring'''
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 UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[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(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[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(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, 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(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[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(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : 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(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : 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(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[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(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[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(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) 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 _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
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(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = 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
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = 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 _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
UpperCAmelCase : Optional[int] = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
UpperCAmelCase : str = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
UpperCAmelCase : Dict = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
UpperCAmelCase : List[Any] = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
UpperCAmelCase : str = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
UpperCAmelCase : int = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
UpperCAmelCase : Tuple = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
UpperCAmelCase : Dict = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
UpperCAmelCase : int = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
UpperCAmelCase : Dict = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
UpperCAmelCase : Dict = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
UpperCAmelCase : str = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
UpperCAmelCase : int = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
UpperCAmelCase : Optional[Any] = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
UpperCAmelCase : Any = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
UpperCAmelCase : List[Any] = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
UpperCAmelCase : Any = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
UpperCAmelCase : Dict = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
UpperCAmelCase : List[str] = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
UpperCAmelCase : List[Any] = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
UpperCAmelCase : Any = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
UpperCAmelCase : Optional[Any] = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
for key in orig_state_dict.copy().keys():
UpperCAmelCase : str = orig_state_dict.pop(_lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
UpperCAmelCase : List[str] = key.split(""".""" )
UpperCAmelCase , UpperCAmelCase : Dict = int(key_split[2] ), int(key_split[4] )
UpperCAmelCase : List[str] = config.vision_config.hidden_size
if "weight" in key:
UpperCAmelCase : Dict = val[:dim, :]
UpperCAmelCase : Tuple = val[dim : dim * 2, :]
UpperCAmelCase : List[str] = val[-dim:, :]
else:
UpperCAmelCase : Union[str, Any] = val[:dim]
UpperCAmelCase : Any = val[dim : dim * 2]
UpperCAmelCase : List[str] = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
UpperCAmelCase : str = key.split(""".""" )
UpperCAmelCase : Union[str, Any] = int(key_split[3] )
UpperCAmelCase : Tuple = config.text_config.hidden_size
if "weight" in key:
UpperCAmelCase : Optional[int] = val[:dim, :]
UpperCAmelCase : List[str] = val[
dim : dim * 2, :
]
UpperCAmelCase : int = val[-dim:, :]
else:
UpperCAmelCase : Optional[int] = val[:dim]
UpperCAmelCase : Any = val[dim : dim * 2]
UpperCAmelCase : Optional[Any] = val[-dim:]
else:
UpperCAmelCase : Union[str, Any] = rename_key(_lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
UpperCAmelCase : int = val.squeeze_()
else:
UpperCAmelCase : List[Any] = val
return orig_state_dict
def __lowerCamelCase ( ) -> Tuple:
UpperCAmelCase : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCAmelCase : Dict = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase="groupvit-gcc-yfcc" , _lowercase=False ) -> Optional[int]:
UpperCAmelCase : int = GroupViTConfig()
UpperCAmelCase : List[Any] = GroupViTModel(_lowercase ).eval()
UpperCAmelCase : List[Any] = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
UpperCAmelCase : Union[str, Any] = convert_state_dict(_lowercase , _lowercase )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = model.load_state_dict(_lowercase , strict=_lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowercase ) == 0)
# verify result
UpperCAmelCase : Union[str, Any] = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : List[str] = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=_lowercase , padding=_lowercase , return_tensors="""pt""" )
with torch.no_grad():
UpperCAmelCase : Optional[int] = model(**_lowercase )
if model_name == "groupvit-gcc-yfcc":
UpperCAmelCase : Any = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
UpperCAmelCase : Dict = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(F'''Model name {model_name} not supported.''' )
assert torch.allclose(outputs.logits_per_image , _lowercase , atol=1e-3 )
processor.save_pretrained(_lowercase )
model.save_pretrained(_lowercase )
print("""Successfully saved processor and model to""" , _lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(_lowercase , organization="""nielsr""" )
model.push_to_hub(_lowercase , organization="""nielsr""" )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
a : str = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a : List[str] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
a : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=5 ) -> List[Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("""<mask>""" ) == 1
UpperCAmelCase : str = torch.tensor(tokenizer.encode(_lowercase , add_special_tokens=_lowercase ) ).unsqueeze(0 ) # Batch size 1
UpperCAmelCase : Dict = model(_lowercase )[0] # The last hidden-state is the first element of the output tuple
UpperCAmelCase : Tuple = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
UpperCAmelCase : Any = logits[0, masked_index, :]
UpperCAmelCase : Optional[Any] = logits.softmax(dim=0 )
UpperCAmelCase , UpperCAmelCase : Optional[int] = prob.topk(k=_lowercase , dim=0 )
UpperCAmelCase : Optional[Any] = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_lowercase ) )] )
UpperCAmelCase : List[str] = tokenizer.mask_token
UpperCAmelCase : Tuple = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
UpperCAmelCase : str = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(_lowercase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(_lowercase ) , _lowercase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_lowercase , _lowercase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
a : Union[str, Any] = CamembertTokenizer.from_pretrained("""camembert-base""")
a : Dict = CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()
a : Union[str, Any] = """Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> bool:
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> int:
if self.framework == "pytorch":
subprocess.run(
f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding="""utf-8""" , check=A , )
assert hasattr(self , """env""" )
def _lowercase( self , A ) -> Optional[int]:
UpperCAmelCase : List[str] = f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
# distributed data settings
UpperCAmelCase : Optional[int] = {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=A , instance_count=A , instance_type=self.instance_type , debugger_hook_config=A , hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=A , py_version="""py36""" , )
def _lowercase( self , A ) -> Tuple:
TrainingJobAnalytics(A ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )
@parameterized.expand([(2,)] )
def _lowercase( self , A ) -> str:
# create estimator
UpperCAmelCase : int = self.create_estimator(A )
# run training
estimator.fit()
# result dataframe
UpperCAmelCase : Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCAmelCase : Union[str, Any] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
UpperCAmelCase : str = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCAmelCase : Dict = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'''{estimator.latest_training_job.name}.json''' , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> Any:
UpperCAmelCase : Optional[Any] = """"""
for i in table:
res += inp[i - 1]
return res
def __lowerCamelCase ( _lowercase ) -> Tuple:
return data[1:] + data[0]
def __lowerCamelCase ( _lowercase , _lowercase ) -> Any:
UpperCAmelCase : List[str] = """"""
for i in range(len(_lowercase ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Union[str, Any] = int("""0b""" + data[0] + data[-1] , 2 )
UpperCAmelCase : List[str] = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> List[str]:
UpperCAmelCase : Dict = message[:4]
UpperCAmelCase : List[str] = message[4:]
UpperCAmelCase : Union[str, Any] = apply_table(_lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = xor(_lowercase , _lowercase )
UpperCAmelCase : List[str] = apply_sbox(_lowercase , temp[:4] ) # noqa: E741
UpperCAmelCase : Union[str, Any] = apply_sbox(_lowercase , temp[4:] )
UpperCAmelCase : Tuple = """0""" * (2 - len(_lowercase )) + l # noqa: E741
UpperCAmelCase : Optional[Any] = """0""" * (2 - len(_lowercase )) + r
UpperCAmelCase : Union[str, Any] = apply_table(l + r , _lowercase )
UpperCAmelCase : Any = xor(_lowercase , _lowercase )
return temp + right
if __name__ == "__main__":
a : Dict = input("""Enter 10 bit key: """)
a : int = input("""Enter 8 bit message: """)
a : Dict = [6, 3, 7, 4, 8, 5, 1_0, 9]
a : Optional[Any] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
a : List[str] = [2, 4, 3, 1]
a : Tuple = [2, 6, 3, 1, 4, 8, 5, 7]
a : List[str] = [4, 1, 3, 5, 7, 2, 8, 6]
a : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a : Tuple = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a : Union[str, Any] = apply_table(key, paa_table)
a : Union[str, Any] = temp[:5]
a : Optional[int] = temp[5:]
a : Optional[int] = left_shift(left)
a : List[str] = left_shift(right)
a : Any = apply_table(left + right, pa_table)
a : Optional[int] = left_shift(left)
a : int = left_shift(right)
a : str = left_shift(left)
a : Optional[Any] = left_shift(right)
a : Any = apply_table(left + right, pa_table)
# encryption
a : List[Any] = apply_table(message, IP)
a : List[str] = function(expansion, sa, sa, keya, temp)
a : List[str] = temp[4:] + temp[:4]
a : List[str] = function(expansion, sa, sa, keya, temp)
a : Optional[int] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a : Dict = apply_table(CT, IP)
a : List[Any] = function(expansion, sa, sa, keya, temp)
a : str = temp[4:] + temp[:4]
a : Optional[Any] = function(expansion, sa, sa, keya, temp)
a : Optional[Any] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 1 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __lowerCamelCase ( ) -> int:
UpperCAmelCase , UpperCAmelCase : List[Any] = 9, 1_4 # noqa: F841
UpperCAmelCase : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
UpperCAmelCase : List[Any] = defaultdict(_lowercase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCAmelCase : str = mst(_lowercase )
UpperCAmelCase : int = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCAmelCase : List[str] = tuple(answer[:2] )
UpperCAmelCase : str = tuple(edge[::-1] )
assert edge in result or reverse in result
| 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( __magic_name__ ):
lowercase = ['image_processor', 'feature_extractor']
lowercase = 'TvltImageProcessor'
lowercase = 'TvltFeatureExtractor'
def __init__( self , A , A ) -> List[str]:
super().__init__(image_processor=A , feature_extractor=A )
UpperCAmelCase : int = image_processor
UpperCAmelCase : List[str] = feature_extractor
def __call__( self , A=None , A=None , A=None , A=None , A=False , A=False , *A , **A , ) -> int:
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
UpperCAmelCase : List[str] = None
if images is not None:
UpperCAmelCase : List[Any] = self.image_processor(A , mask_pixel=A , *A , **A )
if images_mixed is not None:
UpperCAmelCase : Tuple = self.image_processor(A , is_mixed=A , *A , **A )
if audio is not None:
UpperCAmelCase : Any = self.feature_extractor(
A , *A , sampling_rate=A , mask_audio=A , **A )
UpperCAmelCase : Dict = {}
if audio is not None:
output_dict.update(A )
if images is not None:
output_dict.update(A )
if images_mixed_dict is not None:
output_dict.update(A )
return output_dict
@property
def _lowercase( self ) -> int:
UpperCAmelCase : List[Any] = self.image_processor.model_input_names
UpperCAmelCase : Optional[int] = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : str = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 1_0_2_4
UpperCAmelCase : str = 4_0_9_6
UpperCAmelCase : Union[str, Any] = 2_4
UpperCAmelCase : List[str] = 1_6
UpperCAmelCase : List[Any] = [5, 1_1, 1_7, 2_3]
UpperCAmelCase : str = [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
UpperCAmelCase : int = (1, 3_8_4, 3_8_4)
if "nyu" or "midas" in checkpoint_url:
UpperCAmelCase : Optional[int] = 7_6_8
UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5]
UpperCAmelCase : Any = [2_5_6, 5_1_2, 7_6_8, 7_6_8]
UpperCAmelCase : List[Any] = 1_5_0
UpperCAmelCase : Any = 1_6
UpperCAmelCase : Optional[Any] = (1, 3_8_4, 3_8_4)
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Optional[Any] = """project"""
if "ade" in checkpoint_url:
UpperCAmelCase : List[str] = True
UpperCAmelCase : List[Any] = 7_6_8
UpperCAmelCase : List[str] = [1, 1, 1, 0.5]
UpperCAmelCase : Dict = 1_5_0
UpperCAmelCase : int = 1_6
UpperCAmelCase : str = """huggingface/label-files"""
UpperCAmelCase : int = """ade20k-id2label.json"""
UpperCAmelCase : str = json.load(open(cached_download(hf_hub_url(_lowercase , _lowercase , repo_type="""dataset""" ) ) , """r""" ) )
UpperCAmelCase : Dict = {int(_lowercase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : str = {v: k for k, v in idalabel.items()}
UpperCAmelCase : Optional[Any] = [1, 1_5_0, 4_8_0, 4_8_0]
return config, expected_shape
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Union[str, Any] = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(_lowercase , _lowercase )
def __lowerCamelCase ( _lowercase ) -> Tuple:
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
UpperCAmelCase : Tuple = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
UpperCAmelCase : Any = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
UpperCAmelCase : str = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
UpperCAmelCase : Optional[int] = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
UpperCAmelCase : List[Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
UpperCAmelCase : int = name.replace("""proj""" , """projection""" )
if "blocks" in name:
UpperCAmelCase : Optional[Any] = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
UpperCAmelCase : List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCAmelCase : Optional[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
UpperCAmelCase : Dict = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
UpperCAmelCase : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
UpperCAmelCase : Dict = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
UpperCAmelCase : Optional[Any] = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
UpperCAmelCase : Optional[int] = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
UpperCAmelCase : Dict = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
UpperCAmelCase : int = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
UpperCAmelCase : int = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
UpperCAmelCase : Optional[int] = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
UpperCAmelCase : List[Any] = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
UpperCAmelCase : Any = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
UpperCAmelCase : str = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
UpperCAmelCase : Optional[Any] = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
UpperCAmelCase : Dict = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
UpperCAmelCase : List[str] = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
UpperCAmelCase : str = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
UpperCAmelCase : Tuple = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
UpperCAmelCase : Any = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
UpperCAmelCase : Any = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
UpperCAmelCase : Optional[Any] = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
UpperCAmelCase : int = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
UpperCAmelCase : Optional[Any] = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
UpperCAmelCase : int = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
UpperCAmelCase : Dict = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
UpperCAmelCase : List[Any] = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
UpperCAmelCase : List[str] = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
UpperCAmelCase : Optional[Any] = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
UpperCAmelCase : Any = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
UpperCAmelCase : Tuple = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
UpperCAmelCase : List[Any] = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
UpperCAmelCase : List[Any] = name.replace("""..""" , """.""" )
if "stem.conv" in name:
UpperCAmelCase : Optional[Any] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
UpperCAmelCase : Dict = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
UpperCAmelCase : Tuple = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
UpperCAmelCase : Any = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
UpperCAmelCase : List[Any] = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
UpperCAmelCase : Dict = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def __lowerCamelCase ( _lowercase , _lowercase ) -> Union[str, 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)
UpperCAmelCase : List[Any] = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
UpperCAmelCase : List[Any] = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
UpperCAmelCase : Tuple = in_proj_bias[: config.hidden_size]
UpperCAmelCase : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCAmelCase : Union[str, Any] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : Tuple = in_proj_bias[-config.hidden_size :]
def __lowerCamelCase ( ) -> str:
UpperCAmelCase : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCAmelCase : List[str] = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Any:
UpperCAmelCase , UpperCAmelCase : str = get_dpt_config(_lowercase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(_lowercase )
# rename keys
for key in state_dict.copy().keys():
UpperCAmelCase : Tuple = state_dict.pop(_lowercase )
UpperCAmelCase : Optional[Any] = val
# read in qkv matrices
read_in_q_k_v(_lowercase , _lowercase )
# load HuggingFace model
UpperCAmelCase : Optional[int] = DPTForSemanticSegmentation(_lowercase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# Check outputs on an image
UpperCAmelCase : Optional[Any] = 4_8_0 if """ade""" in checkpoint_url else 3_8_4
UpperCAmelCase : List[str] = DPTImageProcessor(size=_lowercase )
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Any = image_processor(_lowercase , return_tensors="""pt""" )
# forward pass
UpperCAmelCase : Optional[Any] = model(**_lowercase ).logits if """ade""" in checkpoint_url else model(**_lowercase ).predicted_depth
if show_prediction:
UpperCAmelCase : List[Any] = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=_lowercase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 2_5_5 ).show()
if pytorch_dump_folder_path is not None:
Path(_lowercase ).mkdir(exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_lowercase )
if push_to_hub:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
a : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
parser.add_argument(
"""--show_prediction""",
action="""store_true""",
)
a : int = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
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("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
a : Tuple = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ :
lowercase = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
lowercase = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
lowercase = field(
default=1_024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
lowercase = field(
default=__magic_name__ , 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.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'A csv or a json file containing the training data.'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
lowercase = field(default=__magic_name__ , metadata={'help': 'A csv or a json file containing the test data.'} )
def _lowercase( self ) -> str:
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
UpperCAmelCase : Optional[int] = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
UpperCAmelCase : List[str] = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class UpperCamelCase_ :
lowercase = field(
default=__magic_name__ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowercase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase = field(
default=__magic_name__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def __lowerCamelCase ( ) -> str:
# 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.
UpperCAmelCase : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = parser.parse_args_into_dataclasses()
# 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 )] , )
UpperCAmelCase : int = training_args.get_process_log_level()
logger.setLevel(_lowercase )
datasets.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.set_verbosity(_lowercase )
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.
UpperCAmelCase : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase : 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 and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCAmelCase : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
UpperCAmelCase : Tuple = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
UpperCAmelCase : Any = data_args.train_file.split(""".""" )[-1]
UpperCAmelCase : Tuple = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
UpperCAmelCase : int = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
UpperCAmelCase : str = load_dataset("""csv""" , data_files=_lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
UpperCAmelCase : Optional[int] = load_dataset("""json""" , data_files=_lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
UpperCAmelCase : Optional[int] = raw_datasets["""train"""].features["""label"""].names
UpperCAmelCase : Union[str, Any] = len(_lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
UpperCAmelCase : int = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_lowercase , )
UpperCAmelCase : str = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
UpperCAmelCase : List[Any] = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
UpperCAmelCase : Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
UpperCAmelCase : Any = {"""Refused""": 0, """Entailed""": 1}
UpperCAmelCase : str = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
UpperCAmelCase : Any = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(_lowercase ):
UpperCAmelCase : Optional[int] = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
UpperCAmelCase : Optional[int] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
UpperCAmelCase : Optional[Any] = examples["""statement"""]
UpperCAmelCase : Any = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
UpperCAmelCase : int = tokenizer(_lowercase , _lowercase , padding=_lowercase , max_length=_lowercase , truncation=_lowercase )
UpperCAmelCase : Optional[Any] = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
UpperCAmelCase : Optional[Any] = raw_datasets.map(
_lowercase , batched=_lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
UpperCAmelCase : Any = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
UpperCAmelCase : Any = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
UpperCAmelCase : Union[str, Any] = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
UpperCAmelCase : List[str] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
UpperCAmelCase : List[str] = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
UpperCAmelCase : Union[str, Any] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_lowercase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
# 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(_lowercase ):
UpperCAmelCase : Optional[Any] = p.predictions[0] if isinstance(p.predictions , _lowercase ) else p.predictions
UpperCAmelCase : Any = np.argmax(_lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
UpperCAmelCase : Tuple = default_data_collator
elif training_args.fpaa:
UpperCAmelCase : Tuple = DataCollatorWithPadding(_lowercase , pad_to_multiple_of=8 )
else:
UpperCAmelCase : Dict = None
# Initialize our Trainer
UpperCAmelCase : int = Trainer(
model=_lowercase , args=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_lowercase , tokenizer=_lowercase , data_collator=_lowercase , )
# Training
if training_args.do_train:
UpperCAmelCase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase : Dict = last_checkpoint
UpperCAmelCase : str = trainer.train(resume_from_checkpoint=_lowercase )
UpperCAmelCase : List[Any] = train_result.metrics
UpperCAmelCase : Optional[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
UpperCAmelCase : Dict = min(_lowercase , len(_lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , _lowercase )
trainer.save_metrics("""train""" , _lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCAmelCase : Union[str, Any] = trainer.evaluate(eval_dataset=_lowercase )
UpperCAmelCase : Any = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowercase )
UpperCAmelCase : Any = min(_lowercase , len(_lowercase ) )
trainer.log_metrics("""eval""" , _lowercase )
trainer.save_metrics("""eval""" , _lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
UpperCAmelCase : Tuple = predict_dataset.remove_columns("""label""" )
UpperCAmelCase : List[str] = trainer.predict(_lowercase , metric_key_prefix="""predict""" ).predictions
UpperCAmelCase : Dict = np.argmax(_lowercase , axis=1 )
UpperCAmelCase : Any = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(_lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(_lowercase ):
UpperCAmelCase : Tuple = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
UpperCAmelCase : Any = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowercase )
else:
trainer.create_model_card(**_lowercase )
def __lowerCamelCase ( _lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = DebertaTokenizer
lowercase = True
lowercase = DebertaTokenizerFast
def _lowercase( self ) -> Dict:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase : List[str] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""[UNK]""",
]
UpperCAmelCase : Optional[int] = dict(zip(A , range(len(A ) ) ) )
UpperCAmelCase : Any = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
UpperCAmelCase : List[Any] = {"""unk_token""": """[UNK]"""}
UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def _lowercase( self , **A ) -> Optional[int]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **A )
def _lowercase( self , A ) -> Tuple:
UpperCAmelCase : Optional[int] = """lower newer"""
UpperCAmelCase : Any = """lower newer"""
return input_text, output_text
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : Optional[int] = """lower newer"""
UpperCAmelCase : Optional[int] = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
UpperCAmelCase : Optional[int] = tokenizer.tokenize(A )
self.assertListEqual(A , A )
UpperCAmelCase : int = tokens + [tokenizer.unk_token]
UpperCAmelCase : List[Any] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : List[str] = tokenizer("""Hello""" , """World""" )
UpperCAmelCase : Tuple = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["""token_type_ids"""] , A )
@slow
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : int = self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
UpperCAmelCase : Tuple = tokenizer.encode("""sequence builders""" , add_special_tokens=A )
UpperCAmelCase : Tuple = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A )
UpperCAmelCase : List[Any] = tokenizer.encode(
"""sequence builders""" , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : Dict = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=A , add_prefix_space=A )
UpperCAmelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(A )
UpperCAmelCase : Optional[Any] = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : str = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
UpperCAmelCase : Any = tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
UpperCAmelCase : Tuple = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
UpperCAmelCase : str = tokenizer(A , padding=A )
UpperCAmelCase : List[Any] = [tokenizer.decode(A , skip_special_tokens=A ) for seq in encoding["""input_ids"""]]
# fmt: off
UpperCAmelCase : str = {
"""input_ids""": [
[1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
],
"""token_type_ids""": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
UpperCAmelCase : Optional[int] = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
self.assertDictEqual(encoding.data , A )
for expected, decoded in zip(A , A ):
self.assertEqual(A , A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase_ ( __magic_name__ ):
lowercase = (DPMSolverSDEScheduler,)
lowercase = 10
def _lowercase( self , **A ) -> Optional[int]:
UpperCAmelCase : Any = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**A )
return config
def _lowercase( self ) -> Optional[int]:
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _lowercase( self ) -> Optional[Any]:
for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=A , beta_end=A )
def _lowercase( self ) -> int:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=A )
def _lowercase( self ) -> str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _lowercase( self ) -> str:
UpperCAmelCase : str = self.scheduler_classes[0]
UpperCAmelCase : Dict = self.get_scheduler_config()
UpperCAmelCase : str = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[Any] = self.dummy_model()
UpperCAmelCase : int = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Tuple = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(A , A )
UpperCAmelCase : Optional[Any] = model(A , A )
UpperCAmelCase : Dict = scheduler.step(A , A , A )
UpperCAmelCase : Tuple = output.prev_sample
UpperCAmelCase : Tuple = torch.sum(torch.abs(A ) )
UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2 ) < 1e-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def _lowercase( self ) -> Any:
UpperCAmelCase : List[str] = self.scheduler_classes[0]
UpperCAmelCase : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCAmelCase : Optional[int] = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : int = self.dummy_model()
UpperCAmelCase : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Dict = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : int = scheduler.scale_model_input(A , A )
UpperCAmelCase : str = model(A , A )
UpperCAmelCase : Tuple = scheduler.step(A , A , A )
UpperCAmelCase : Optional[int] = output.prev_sample
UpperCAmelCase : Any = torch.sum(torch.abs(A ) )
UpperCAmelCase : List[Any] = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7 ) < 1e-3
else:
assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1 ) < 1e-3
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Tuple = self.scheduler_classes[0]
UpperCAmelCase : List[str] = self.get_scheduler_config()
UpperCAmelCase : Tuple = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps , device=A )
UpperCAmelCase : Tuple = self.dummy_model()
UpperCAmelCase : Tuple = self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : Any = scheduler.scale_model_input(A , A )
UpperCAmelCase : Dict = model(A , A )
UpperCAmelCase : Union[str, Any] = scheduler.step(A , A , A )
UpperCAmelCase : Dict = output.prev_sample
UpperCAmelCase : List[Any] = torch.sum(torch.abs(A ) )
UpperCAmelCase : Dict = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1 ) < 1e-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
UpperCAmelCase : Optional[int] = self.get_scheduler_config()
UpperCAmelCase : str = scheduler_class(**A , use_karras_sigmas=A )
scheduler.set_timesteps(self.num_inference_steps , device=A )
UpperCAmelCase : Dict = self.dummy_model()
UpperCAmelCase : List[str] = self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma
UpperCAmelCase : Any = sample.to(A )
for t in scheduler.timesteps:
UpperCAmelCase : Optional[Any] = scheduler.scale_model_input(A , A )
UpperCAmelCase : Tuple = model(A , A )
UpperCAmelCase : Optional[Any] = scheduler.step(A , A , A )
UpperCAmelCase : Dict = output.prev_sample
UpperCAmelCase : Dict = torch.sum(torch.abs(A ) )
UpperCAmelCase : Any = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
else:
assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
UpperCAmelCase : Tuple = int(np.ceil((x_end - xa) / h ) )
UpperCAmelCase : Optional[Any] = np.zeros((n + 1,) )
UpperCAmelCase : int = ya
UpperCAmelCase : Dict = xa
for k in range(_lowercase ):
UpperCAmelCase : int = f(_lowercase , y[k] )
UpperCAmelCase : Optional[int] = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
UpperCAmelCase : Dict = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
UpperCAmelCase : Union[str, Any] = f(x + h , y[k] + h * ka )
UpperCAmelCase : int = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""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 UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
a : List[Any] = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
return max(metric_fn(_lowercase , _lowercase ) for gt in ground_truths )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = [line.strip() for line in open(_lowercase , """r""" ).readlines()]
UpperCAmelCase : Optional[Any] = []
if args.gold_data_mode == "qa":
UpperCAmelCase : Dict = pd.read_csv(_lowercase , sep="""\t""" , header=_lowercase )
for answer_list in data[1]:
UpperCAmelCase : Tuple = ast.literal_eval(_lowercase )
answers.append(_lowercase )
else:
UpperCAmelCase : List[str] = [line.strip() for line in open(_lowercase , """r""" ).readlines()]
UpperCAmelCase : List[Any] = [[reference] for reference in references]
UpperCAmelCase : List[Any] = 0
for prediction, ground_truths in zip(_lowercase , _lowercase ):
total += 1
em += metric_max_over_ground_truths(_lowercase , _lowercase , _lowercase )
fa += metric_max_over_ground_truths(_lowercase , _lowercase , _lowercase )
UpperCAmelCase : Tuple = 100.0 * em / total
UpperCAmelCase : Optional[Any] = 100.0 * fa / total
logger.info(F'''F1: {fa:.2f}''' )
logger.info(F'''EM: {em:.2f}''' )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Union[str, Any] = args.k
UpperCAmelCase : Union[str, Any] = [line.strip() for line in open(_lowercase , """r""" ).readlines()]
UpperCAmelCase : Union[str, Any] = [line.strip() for line in open(_lowercase , """r""" ).readlines()]
UpperCAmelCase : List[Any] = 0
for hypo, reference in zip(_lowercase , _lowercase ):
UpperCAmelCase : int = set(hypo.split("""\t""" )[:k] )
UpperCAmelCase : Optional[Any] = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
UpperCAmelCase : Tuple = 100.0 * em / total
logger.info(F'''Precision@{k}: {em: .2f}''' )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
def strip_title(_lowercase ):
if title.startswith("""\"""" ):
UpperCAmelCase : Any = title[1:]
if title.endswith("""\"""" ):
UpperCAmelCase : Optional[Any] = title[:-1]
return title
UpperCAmelCase : Optional[int] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_lowercase , return_tensors="""pt""" , padding=_lowercase , truncation=_lowercase , )["""input_ids"""].to(args.device )
UpperCAmelCase : List[str] = rag_model.rag.question_encoder(_lowercase )
UpperCAmelCase : int = question_enc_outputs[0]
UpperCAmelCase : Union[str, Any] = rag_model.retriever(
_lowercase , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="""pt""" , )
UpperCAmelCase : Tuple = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
UpperCAmelCase : Any = []
for docs in all_docs:
UpperCAmelCase : Optional[Any] = [strip_title(_lowercase ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(_lowercase ) )
return provenance_strings
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[int]:
with torch.no_grad():
UpperCAmelCase : Dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_lowercase , return_tensors="""pt""" , padding=_lowercase , truncation=_lowercase )
UpperCAmelCase : Optional[Any] = inputs_dict.input_ids.to(args.device )
UpperCAmelCase : Optional[int] = inputs_dict.attention_mask.to(args.device )
UpperCAmelCase : str = rag_model.generate( # rag_model overwrites generate
_lowercase , attention_mask=_lowercase , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=_lowercase , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
UpperCAmelCase : Any = rag_model.retriever.generator_tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase )
if args.print_predictions:
for q, a in zip(_lowercase , _lowercase ):
logger.info("""Q: {} - A: {}""".format(_lowercase , _lowercase ) )
return answers
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" , choices=["""rag_sequence""", """rag_token""", """bart"""] , type=_lowercase , help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) , )
parser.add_argument(
"""--index_name""" , default=_lowercase , choices=["""exact""", """compressed""", """legacy"""] , type=_lowercase , help="""RAG model retriever type""" , )
parser.add_argument(
"""--index_path""" , default=_lowercase , type=_lowercase , help="""Path to the retrieval index""" , )
parser.add_argument("""--n_docs""" , default=5 , type=_lowercase , help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--eval_mode""" , choices=["""e2e""", """retrieval"""] , default="""e2e""" , type=_lowercase , help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) , )
parser.add_argument("""--k""" , default=1 , type=_lowercase , help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""Path to a file containing evaluation samples""" , )
parser.add_argument(
"""--gold_data_path""" , default=_lowercase , type=_lowercase , required=_lowercase , help="""Path to a tab-separated file with gold samples""" , )
parser.add_argument(
"""--gold_data_mode""" , default="""qa""" , type=_lowercase , choices=["""qa""", """ans"""] , help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) , )
parser.add_argument(
"""--predictions_path""" , type=_lowercase , default="""predictions.txt""" , help="""Name of the predictions file, to be stored in the checkpoints directory""" , )
parser.add_argument(
"""--eval_all_checkpoints""" , action="""store_true""" , help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" , )
parser.add_argument(
"""--eval_batch_size""" , default=8 , type=_lowercase , help="""Batch size per GPU/CPU for evaluation.""" , )
parser.add_argument(
"""--recalculate""" , help="""Recalculate predictions even if the prediction file exists""" , action="""store_true""" , )
parser.add_argument(
"""--num_beams""" , default=4 , type=_lowercase , help="""Number of beams to be used when generating answers""" , )
parser.add_argument("""--min_length""" , default=1 , type=_lowercase , help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" , default=5_0 , type=_lowercase , help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" , action="""store_true""" , help="""If True, prints predictions while evaluating.""" , )
parser.add_argument(
"""--print_docs""" , action="""store_true""" , help="""If True, prints docs retried while generating.""" , )
UpperCAmelCase : List[Any] = parser.parse_args()
UpperCAmelCase : List[Any] = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def __lowerCamelCase ( _lowercase ) -> Any:
UpperCAmelCase : List[str] = {}
if args.model_type is None:
UpperCAmelCase : Dict = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
UpperCAmelCase : Any = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
UpperCAmelCase : Union[str, Any] = args.n_docs
if args.index_name is not None:
UpperCAmelCase : Tuple = args.index_name
if args.index_path is not None:
UpperCAmelCase : List[Any] = args.index_path
else:
UpperCAmelCase : List[Any] = BartForConditionalGeneration
UpperCAmelCase : Union[str, Any] = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" , _lowercase )
UpperCAmelCase : Optional[int] = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
UpperCAmelCase : Optional[int] = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(_lowercase , args.predictions_path , args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(_lowercase ) )
logger.info(""" Batch size = %d""" , args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
UpperCAmelCase : Optional[int] = RagRetriever.from_pretrained(_lowercase , **_lowercase )
UpperCAmelCase : Tuple = model_class.from_pretrained(_lowercase , retriever=_lowercase , **_lowercase )
model.retriever.init_retrieval()
else:
UpperCAmelCase : Optional[int] = model_class.from_pretrained(_lowercase , **_lowercase )
model.to(args.device )
with open(args.evaluation_set , """r""" ) as eval_file, open(args.predictions_path , """w""" ) as preds_file:
UpperCAmelCase : List[Any] = []
for line in tqdm(_lowercase ):
questions.append(line.strip() )
if len(_lowercase ) == args.eval_batch_size:
UpperCAmelCase : str = evaluate_batch_fn(_lowercase , _lowercase , _lowercase )
preds_file.write("""\n""".join(_lowercase ) + """\n""" )
preds_file.flush()
UpperCAmelCase : Dict = []
if len(_lowercase ) > 0:
UpperCAmelCase : List[str] = evaluate_batch_fn(_lowercase , _lowercase , _lowercase )
preds_file.write("""\n""".join(_lowercase ) )
preds_file.flush()
score_fn(_lowercase , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
a : List[Any] = get_args()
main(args)
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : List[str] = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
"""FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FalconForCausalLM""",
"""FalconModel""",
"""FalconPreTrainedModel""",
"""FalconForSequenceClassification""",
"""FalconForTokenClassification""",
"""FalconForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A ) -> Any:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
UpperCAmelCase : int = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(A )
def _lowercase( self ) -> List[str]:
UpperCAmelCase : List[str] = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Optional[Any] = PyTorchBenchmark(A )
UpperCAmelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : int = """sgugger/tiny-distilbert-classification"""
UpperCAmelCase : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , only_pretrain_model=A , )
UpperCAmelCase : List[str] = PyTorchBenchmark(A )
UpperCAmelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Tuple = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , torchscript=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : int = PyTorchBenchmark(A )
UpperCAmelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : List[str] = """sshleifer/tiny-gpt2"""
UpperCAmelCase : List[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , fpaa=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : List[str] = PyTorchBenchmark(A )
UpperCAmelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Dict = AutoConfig.from_pretrained(A )
# set architectures equal to `None`
UpperCAmelCase : Tuple = None
UpperCAmelCase : Optional[int] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Dict = PyTorchBenchmark(A , configs=[config] )
UpperCAmelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> str:
UpperCAmelCase : Any = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Optional[int] = PyTorchBenchmark(A )
UpperCAmelCase : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def _lowercase( self ) -> Any:
UpperCAmelCase : int = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , fpaa=A , multi_process=A , )
UpperCAmelCase : List[str] = PyTorchBenchmark(A )
UpperCAmelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = """sshleifer/tiny-gpt2"""
UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(A )
UpperCAmelCase : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Optional[Any] = PyTorchBenchmark(A , configs=[config] )
UpperCAmelCase : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> List[str]:
UpperCAmelCase : Optional[int] = """sshleifer/tinier_bart"""
UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained(A )
UpperCAmelCase : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Optional[Any] = PyTorchBenchmark(A , configs=[config] )
UpperCAmelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = """sshleifer/tiny-gpt2"""
UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(A )
UpperCAmelCase : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Optional[Any] = PyTorchBenchmark(A , configs=[config] )
UpperCAmelCase : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = """sshleifer/tinier_bart"""
UpperCAmelCase : int = AutoConfig.from_pretrained(A )
UpperCAmelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
UpperCAmelCase : Any = PyTorchBenchmark(A , configs=[config] )
UpperCAmelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : str = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Optional[int] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , save_to_csv=A , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(A , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(A , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(A , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(A , """train_time.csv""" ) , env_info_csv_file=os.path.join(A , """env.csv""" ) , multi_process=A , )
UpperCAmelCase : Dict = PyTorchBenchmark(A )
benchmark.run()
self.assertTrue(Path(os.path.join(A , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(A , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(A , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(A , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(A , """env.csv""" ) ).exists() )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Any = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(A ):
self.assertTrue(hasattr(A , """sequential""" ) )
self.assertTrue(hasattr(A , """cumulative""" ) )
self.assertTrue(hasattr(A , """current""" ) )
self.assertTrue(hasattr(A , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(A , """log.txt""" ) , log_print=A , trace_memory_line_by_line=A , multi_process=A , )
UpperCAmelCase : str = PyTorchBenchmark(A )
UpperCAmelCase : Union[str, Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(A , """log.txt""" ) ).exists() )
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
a : Any = logging.get_logger(__name__)
a : Union[str, Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
a : int = {
"""vocab_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
a : Any = {
"""vocab_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
a : Dict = {
"""vocab_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
),
},
}
a : Any = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
"""facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
a : List[Any] = {
"""facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
"""facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
a : Dict = {
"""facebook/dpr-reader-single-nq-base""": 5_1_2,
"""facebook/dpr-reader-multiset-base""": 5_1_2,
}
a : int = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
a : Union[str, Any] = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
a : Dict = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowercase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowercase = DPRContextEncoderTokenizer
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowercase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowercase = DPRQuestionEncoderTokenizer
a : Union[str, Any] = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
a : str = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
a : Optional[Any] = R"""
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Return:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(__magic_name__ )
class UpperCamelCase_ :
def __call__( self , A , A = None , A = None , A = False , A = False , A = None , A = None , A = None , **A , ) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
elif titles is None or texts is None:
UpperCAmelCase : Optional[int] = titles if texts is None else texts
return super().__call__(
A , A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
UpperCAmelCase : List[Any] = titles if not isinstance(A , A ) else [titles]
UpperCAmelCase : Union[str, Any] = texts if not isinstance(A , A ) else [texts]
UpperCAmelCase : Optional[Any] = len(A )
UpperCAmelCase : Tuple = questions if not isinstance(A , A ) else [questions] * n_passages
assert len(A ) == len(
A ), f'''There should be as many titles than texts but got {len(A )} titles and {len(A )} texts.'''
UpperCAmelCase : str = super().__call__(A , A , padding=A , truncation=A )["""input_ids"""]
UpperCAmelCase : Optional[int] = super().__call__(A , add_special_tokens=A , padding=A , truncation=A )["""input_ids"""]
UpperCAmelCase : int = {
"""input_ids""": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(A , A )
]
}
if return_attention_mask is not False:
UpperCAmelCase : Dict = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
UpperCAmelCase : int = attention_mask
return self.pad(A , padding=A , max_length=A , return_tensors=A )
def _lowercase( self , A , A , A = 16 , A = 64 , A = 4 , ) -> List[DPRSpanPrediction]:
UpperCAmelCase : Tuple = reader_input["""input_ids"""]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = reader_output[:3]
UpperCAmelCase : Optional[int] = len(A )
UpperCAmelCase : Optional[Any] = sorted(range(A ) , reverse=A , key=relevance_logits.__getitem__ )
UpperCAmelCase : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
UpperCAmelCase : Optional[int] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
UpperCAmelCase : List[Any] = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
UpperCAmelCase : Optional[Any] = sequence_ids.index(self.pad_token_id )
else:
UpperCAmelCase : Union[str, Any] = len(A )
UpperCAmelCase : Optional[Any] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=A , top_spans=A , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=A , start_index=A , end_index=A , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(A ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _lowercase( self , A , A , A , A , ) -> List[DPRSpanPrediction]:
UpperCAmelCase : Any = []
for start_index, start_score in enumerate(A ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
UpperCAmelCase : List[str] = sorted(A , key=lambda A : x[1] , reverse=A )
UpperCAmelCase : List[Any] = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
UpperCAmelCase : List[Any] = end_index - start_index + 1
assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(A ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__magic_name__ )
class UpperCamelCase_ ( __magic_name__ , __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = READER_PRETRAINED_VOCAB_FILES_MAP
lowercase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = READER_PRETRAINED_INIT_CONFIGURATION
lowercase = ['input_ids', 'attention_mask']
lowercase = DPRReaderTokenizer
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
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