code
stringlengths 86
54.5k
| code_codestyle
int64 0
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| style_context
stringlengths 87
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| style_context_codestyle
int64 0
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| label
int64 0
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|---|---|---|---|---|
"""simple docstring"""
def __magic_name__ ( lowercase = 1000 ):
return sum(e for e in range(3 , lowercase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 173
|
def lowerCamelCase__ ( a ) -> bool:
_A: Dict = [int(a ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(a ) == 4 and all(0 <= int(a ) <= 2_54 for octet in octets )
if __name__ == "__main__":
UpperCAmelCase__ : str = input().strip()
UpperCAmelCase__ : Any = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 121
| 0
|
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class SCREAMING_SNAKE_CASE__ :
def __init__( self , A_ , A_=13 , A_=2 , A_=24 , A_=16 , A_=True , A_=True , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=10 , A_=0.02 , A_=None , A_=2 , A_=2 , )-> int:
'''simple docstring'''
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = patch_size
UpperCamelCase = max_length
UpperCamelCase = num_mel_bins
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = scope
UpperCamelCase = frequency_stride
UpperCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCamelCase = frequency_out_dimension * time_out_dimension
UpperCamelCase = num_patches + 2
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
UpperCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, input_values, labels
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A_ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def UpperCAmelCase_ ( self , A_ , A_ , A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = ASTModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_values': input_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( snake_case_ , snake_case_ , unittest.TestCase):
lowerCAmelCase_ = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def UpperCAmelCase_ ( self , A_ , A_ , A_ , A_ , A_ )-> Dict:
'''simple docstring'''
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
UpperCamelCase = ASTModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='AST does not use inputs_embeds' )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , nn.Linear ) )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['input_values']
self.assertListEqual(arg_names[:1] , A_ )
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
@slow
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = ASTModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def A_( ):
UpperCamelCase = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset')
UpperCamelCase , UpperCamelCase = torchaudio.load(A)
return audio, sampling_rate
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
@cached_property
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
return (
ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' )
if is_torchaudio_available()
else None
)
@slow
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
UpperCamelCase = self.default_feature_extractor
UpperCamelCase = ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' ).to(A_ )
UpperCamelCase = self.default_feature_extractor
UpperCamelCase , UpperCamelCase = prepare_audio()
UpperCamelCase = audio.squeeze().numpy()
UpperCamelCase = feature_extractor(A_ , sampling_rate=A_ , return_tensors='pt' ).to(A_ )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**A_ )
# verify the logits
UpperCamelCase = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape , A_ )
UpperCamelCase = torch.tensor([-0.8_760, -7.0_042, -8.6_602] ).to(A_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A_ , atol=1e-4 ) )
| 251
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 251
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 178
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 178
| 1
|
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowerCamelCase = 50_000
lowerCamelCase = 5_000
lowerCamelCase , lowerCamelCase = os.path.split(__file__)
lowerCamelCase = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
for i in range(lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
for i in range(0 , len(lowerCAmelCase__ ) , lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i : i + batch_size]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
with dataset.formatted_as(type=lowerCAmelCase__ ):
for i in range(lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
with dataset.formatted_as(type=lowerCAmelCase__ ):
for i in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i : i + batch_size]
def a__ ( ):
UpperCAmelCase_ = {"num examples": SPEED_TEST_N_EXAMPLES}
UpperCAmelCase_ = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted, {"type": "pandas", "length": SMALL_TEST}),
(read_formatted, {"type": "torch", "length": SMALL_TEST}),
(read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
UpperCAmelCase_ = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset" )
UpperCAmelCase_ = datasets.Features(
{"list": datasets.Sequence(datasets.Value("float32" ) ), "numbers": datasets.Value("float32" )} )
UpperCAmelCase_ = generate_example_dataset(
os.path.join(lowerCAmelCase__ , "dataset.arrow" ) , lowerCAmelCase__ , num_examples=lowerCAmelCase__ , seq_shapes={"list": (100,)} , )
print("first set of iterations" )
for func, kwargs in functions:
print(func.__name__ , str(lowerCAmelCase__ ) )
UpperCAmelCase_ = func(lowerCAmelCase__ , **lowerCAmelCase__ )
print("shuffling dataset" )
UpperCAmelCase_ = dataset.shuffle()
print("Second set of iterations (after shuffling" )
for func, kwargs in functions_shuffled:
print("shuffled " , func.__name__ , str(lowerCAmelCase__ ) )
UpperCAmelCase_ = func(
lowerCAmelCase__ , **lowerCAmelCase__ )
with open(lowerCAmelCase__ , "wb" ) as f:
f.write(json.dumps(lowerCAmelCase__ ).encode("utf-8" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 241
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 241
| 1
|
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {}
__magic_name__ = {}
__magic_name__ = {}
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , ):
__SCREAMING_SNAKE_CASE = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})" )
__SCREAMING_SNAKE_CASE = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})" )
__SCREAMING_SNAKE_CASE = format_type
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None ):
__SCREAMING_SNAKE_CASE = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
__SCREAMING_SNAKE_CASE = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
__magic_name__ = ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
__magic_name__ = ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, "jax", aliases=[])
else:
__magic_name__ = ValueError("JAX needs to be installed to be able to return JAX arrays.")
_register_unavailable_formatter(_jax_error, "jax", aliases=[])
def _lowerCAmelCase ( UpperCamelCase_ ):
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def _lowerCAmelCase ( UpperCamelCase_ , **UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = get_format_type_from_alias(UpperCamelCase_ )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**UpperCamelCase_ )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got '{format_type}'" )
| 100
|
"""simple docstring"""
__magic_name__ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 100
| 1
|
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( __UpperCamelCase ):
A__ : str =(DDPMParallelScheduler,)
def A_ ( self : List[Any] , **UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = {
'num_train_timesteps': 1000,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**UpperCAmelCase_ )
return config
def A_ ( self : List[str] ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_ )
def A_ ( self : str ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
self.check_over_configs(thresholding=UpperCAmelCase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCAmelCase_ , prediction_type=UpperCAmelCase_ , sample_max_value=UpperCAmelCase_ , )
def A_ ( self : Tuple ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase_ )
def A_ ( self : List[Any] ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=UpperCAmelCase_ )
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter + 0.1
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter - 0.1
SCREAMING_SNAKE_CASE__ = samplea.shape[0]
SCREAMING_SNAKE_CASE__ = torch.stack([samplea, samplea, samplea] , dim=0 )
SCREAMING_SNAKE_CASE__ = torch.arange(UpperCAmelCase_ )[0:3, None].repeat(1 , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
SCREAMING_SNAKE_CASE__ = scheduler.batch_step_no_noise(UpperCAmelCase_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 1153.1833 ) < 1e-2
assert abs(result_mean.item() - 0.5_005 ) < 1e-3
def A_ ( self : List[str] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase_ ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
SCREAMING_SNAKE_CASE__ = pred_prev_sample
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3_372 ) < 1e-3
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config(prediction_type='v_prediction' )
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase_ ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
SCREAMING_SNAKE_CASE__ = pred_prev_sample
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2_631 ) < 1e-3
def A_ ( self : Dict ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = scheduler.timesteps
for i, timestep in enumerate(UpperCAmelCase_ ):
if i == len(UpperCAmelCase_ ) - 1:
SCREAMING_SNAKE_CASE__ = -1
else:
SCREAMING_SNAKE_CASE__ = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ = scheduler.previous_timestep(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = prev_t.item()
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [100, 87, 50, 51, 0]
with self.assertRaises(UpperCAmelCase_ , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
def A_ ( self : str ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ )
with self.assertRaises(UpperCAmelCase_ , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_ )
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase_ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
| 351
|
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )
if tokenizer_name is None:
SCREAMING_SNAKE_CASE__ = TOKENIZER_CLASSES
else:
SCREAMING_SNAKE_CASE__ = {tokenizer_name: getattr(UpperCamelCase_ , tokenizer_name + 'Fast' )}
logger.info(F'Loading tokenizer classes: {tokenizer_names}' )
for tokenizer_name in tokenizer_names:
SCREAMING_SNAKE_CASE__ = TOKENIZER_CLASSES[tokenizer_name]
SCREAMING_SNAKE_CASE__ = True
if checkpoint_name is None:
SCREAMING_SNAKE_CASE__ = list(tokenizer_class.max_model_input_sizes.keys() )
else:
SCREAMING_SNAKE_CASE__ = [checkpoint_name]
logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )
for checkpoint in checkpoint_names:
logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )
# Load tokenizer
SCREAMING_SNAKE_CASE__ = tokenizer_class.from_pretrained(UpperCamelCase_ , force_download=UpperCamelCase_ )
# Save fast tokenizer
logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )
# For organization names we create sub-directories
if "/" in checkpoint:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = checkpoint.split('/' )
SCREAMING_SNAKE_CASE__ = os.path.join(UpperCamelCase_ , UpperCamelCase_ )
elif add_prefix:
SCREAMING_SNAKE_CASE__ = checkpoint
SCREAMING_SNAKE_CASE__ = dump_path
else:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = dump_path
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
SCREAMING_SNAKE_CASE__ = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
SCREAMING_SNAKE_CASE__ = file_path.split(UpperCamelCase_ )[-1][0]
if next_char == "/":
SCREAMING_SNAKE_CASE__ = os.path.join(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = None
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
SCREAMING_SNAKE_CASE__ = tokenizer.save_pretrained(
UpperCamelCase_ , legacy_format=UpperCamelCase_ , filename_prefix=UpperCamelCase_ )
logger.info(F'=> File names {file_names}' )
for file_name in file_names:
if not file_name.endswith('tokenizer.json' ):
os.remove(UpperCamelCase_ )
logger.info(F'=> removing {file_name}' )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
)
parser.add_argument(
"""--tokenizer_name""",
default=None,
type=str,
help=(
F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
"""download and convert all the checkpoints from AWS."""
),
)
parser.add_argument(
"""--checkpoint_name""",
default=None,
type=str,
help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
)
parser.add_argument(
"""--force_download""",
action="""store_true""",
help="""Re-download checkpoints.""",
)
__snake_case = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 169
| 0
|
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _snake_case ( _snake_case : Union[str, Any] ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def _snake_case ( _snake_case : Optional[int] , _snake_case : Union[str, Any] ):
lowerCAmelCase : Optional[Any] = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
lowerCAmelCase : Dict = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
lowerCAmelCase : List[Any] = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
lowerCAmelCase : List[Any] = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
lowerCAmelCase : List[str] = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
lowerCAmelCase : Optional[Any] = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
lowerCAmelCase : Tuple = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
lowerCAmelCase : int = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
lowerCAmelCase : List[Any] = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
lowerCAmelCase : List[str] = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
lowerCAmelCase : Dict = key.replace('''image_encoder.module''' , '''flava.image_model''' )
lowerCAmelCase : Optional[int] = key.replace('''text_encoder.module''' , '''flava.text_model''' )
lowerCAmelCase : Optional[int] = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
lowerCAmelCase : Tuple = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
lowerCAmelCase : Tuple = key.replace('''text_projection''' , '''flava.text_projection''' )
lowerCAmelCase : Dict = key.replace('''image_projection''' , '''flava.image_projection''' )
lowerCAmelCase : Union[str, Any] = value.float()
for key, value in codebook_state_dict.items():
lowerCAmelCase : Dict = value
return upgrade
@torch.no_grad()
def _snake_case ( _snake_case : Tuple , _snake_case : Dict , _snake_case : Optional[int] , _snake_case : int=None ):
if config_path is not None:
lowerCAmelCase : Optional[int] = FlavaConfig.from_pretrained(_snake_case )
else:
lowerCAmelCase : Optional[Any] = FlavaConfig()
lowerCAmelCase : List[Any] = FlavaForPreTraining(_snake_case ).eval()
lowerCAmelCase : int = convert_dalle_checkpoint(_snake_case , _snake_case , save_checkpoint=_snake_case )
if os.path.exists(_snake_case ):
lowerCAmelCase : Tuple = torch.load(_snake_case , map_location='''cpu''' )
else:
lowerCAmelCase : str = torch.hub.load_state_dict_from_url(_snake_case , map_location='''cpu''' )
lowerCAmelCase : str = upgrade_state_dict(_snake_case , _snake_case )
hf_model.load_state_dict(_snake_case )
lowerCAmelCase : Optional[int] = hf_model.state_dict()
lowerCAmelCase : Any = count_parameters(_snake_case )
lowerCAmelCase : List[Any] = count_parameters(_snake_case ) + count_parameters(_snake_case )
assert torch.allclose(_snake_case , _snake_case , atol=1E-3 )
hf_model.save_pretrained(_snake_case )
if __name__ == "__main__":
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
snake_case__ : Any = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 60
|
def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int ,__lowercase : int ,__lowercase : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : list[int] ,__lowercase : int ):
'''simple docstring'''
if curr_ind == len(__lowercase ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 ,len(__lowercase ) ):
if valid_connection(__lowercase ,__lowercase ,__lowercase ,__lowercase ):
# Insert current vertex into path as next transition
A_ : Tuple = next_ver
# Validate created path
if util_hamilton_cycle(__lowercase ,__lowercase ,curr_ind + 1 ):
return True
# Backtrack
A_ : Tuple = -1
return False
def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int = 0 ):
'''simple docstring'''
A_ : Any = [-1] * (len(__lowercase ) + 1)
# initialize start and end of path with starting index
A_ : Dict = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__lowercase ,__lowercase ,1 ) else []
| 140
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|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase ( a_ ):
"""simple docstring"""
A : UNetaDModel
A : ScoreSdeVeScheduler
def __init__( self : List[str] , UpperCAmelCase_ : UNetaDModel , UpperCAmelCase_ : ScoreSdeVeScheduler):
"""simple docstring"""
super().__init__()
self.register_modules(unet=UpperCAmelCase_ , scheduler=UpperCAmelCase_)
@torch.no_grad()
def __call__( self : Any , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 2_0_0_0 , UpperCAmelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCAmelCase_ : Optional[str] = "pil" , UpperCAmelCase_ : bool = True , **UpperCAmelCase_ : Dict , ):
"""simple docstring"""
a : str = self.unet.config.sample_size
a : str = (batch_size, 3, img_size, img_size)
a : List[Any] = self.unet
a : Optional[int] = randn_tensor(UpperCAmelCase_ , generator=UpperCAmelCase_) * self.scheduler.init_noise_sigma
a : Union[str, Any] = sample.to(self.device)
self.scheduler.set_timesteps(UpperCAmelCase_)
self.scheduler.set_sigmas(UpperCAmelCase_)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
a : str = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
a : Tuple = self.unet(UpperCAmelCase_ , UpperCAmelCase_).sample
a : int = self.scheduler.step_correct(UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample
# prediction step
a : Dict = model(UpperCAmelCase_ , UpperCAmelCase_).sample
a : Optional[Any] = self.scheduler.step_pred(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_)
a : int = output.prev_sample, output.prev_sample_mean
a : str = sample_mean.clamp(0 , 1)
a : Optional[int] = sample.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a : Any = self.numpy_to_pil(UpperCAmelCase_)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=UpperCAmelCase_)
| 367
|
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class UpperCamelCase :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase_ : Tuple):
"""simple docstring"""
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
a : Dict = deepcopy(UpperCAmelCase_)
elif os.path.exists(UpperCAmelCase_):
with io.open(UpperCAmelCase_ , 'r' , encoding='utf-8') as f:
a : Union[str, Any] = json.load(UpperCAmelCase_)
else:
try:
a : Union[str, Any] = baseaa.urlsafe_baadecode(UpperCAmelCase_).decode('utf-8')
a : List[str] = json.loads(UpperCAmelCase_)
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""")
a : Optional[int] = config
self.set_stage_and_offload()
def SCREAMING_SNAKE_CASE_ ( self : List[Any]):
"""simple docstring"""
a : str = self.get_value('zero_optimization.stage' , -1)
# offload
a : Any = False
if self.is_zeroa() or self.is_zeroa():
a : Tuple = set(['cpu', 'nvme'])
a : int = set(
[
self.get_value('zero_optimization.offload_optimizer.device'),
self.get_value('zero_optimization.offload_param.device'),
])
if len(offload_devices & offload_devices_valid) > 0:
a : List[str] = True
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCAmelCase_ : Dict):
"""simple docstring"""
a : List[str] = self.config
# find the config node of interest if it exists
a : int = ds_key_long.split('.')
a : Union[str, Any] = nodes.pop()
for node in nodes:
a : Union[str, Any] = config.get(UpperCAmelCase_)
if config is None:
return None, ds_key
return config, ds_key
def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int=None):
"""simple docstring"""
a , a : int = self.find_config_node(UpperCAmelCase_)
if config is None:
return default
return config.get(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any=False):
"""simple docstring"""
a : Any = self.config
# find the config node of interest if it exists
a : Optional[Any] = ds_key_long.split('.')
for node in nodes:
a : List[str] = config
a : int = config.get(UpperCAmelCase_)
if config is None:
if must_exist:
raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""")
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCAmelCase_ : str):
"""simple docstring"""
a : List[str] = self.get_value(UpperCAmelCase_)
return False if value is None else bool(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCAmelCase_ : Union[str, Any]):
"""simple docstring"""
a : List[Any] = self.get_value(UpperCAmelCase_)
return False if value is None else not bool(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
return self._stage == 2
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
return self._stage == 3
def SCREAMING_SNAKE_CASE_ ( self : Dict):
"""simple docstring"""
return self._offload
class UpperCamelCase :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase_ : int):
"""simple docstring"""
a : Union[str, Any] = engine
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any]):
"""simple docstring"""
self.engine.backward(UpperCAmelCase_ , **UpperCAmelCase_)
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class UpperCamelCase ( a_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any):
"""simple docstring"""
super().__init__(UpperCAmelCase_ , device_placement=UpperCAmelCase_ , scaler=UpperCAmelCase_)
a : List[str] = hasattr(self.optimizer , 'overflow')
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCAmelCase_ : Dict=None):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def SCREAMING_SNAKE_CASE_ ( self : Tuple):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict):
"""simple docstring"""
if self.__has_overflow__:
return self.optimizer.overflow
return False
class UpperCamelCase ( a_ ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any]):
"""simple docstring"""
super().__init__(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int]=0.0_01 , UpperCAmelCase_ : List[Any]=0 , **UpperCAmelCase_ : Union[str, Any]):
"""simple docstring"""
a : int = params
a : str = lr
a : Tuple = weight_decay
a : Dict = kwargs
class UpperCamelCase :
"""simple docstring"""
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Union[str, Any]=0 , **UpperCAmelCase_ : List[Any]):
"""simple docstring"""
a : str = optimizer
a : Tuple = total_num_steps
a : Optional[Any] = warmup_num_steps
a : List[str] = kwargs
| 345
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|
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase ={
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
_lowerCamelCase =logging.get_logger(__name__)
class a_ ( __A ):
"""simple docstring"""
__UpperCAmelCase = 'mask2former'
__UpperCAmelCase = ['swin']
__UpperCAmelCase = {'hidden_size': 'hidden_dim'}
def __init__( self : Union[str, Any] ,snake_case : Dict = None ,snake_case : List[str] = 256 ,snake_case : Optional[int] = 256 ,snake_case : Any = 256 ,snake_case : int = 1024 ,snake_case : Dict = "relu" ,snake_case : Dict = 6 ,snake_case : Dict = 10 ,snake_case : Optional[int] = 8 ,snake_case : List[str] = 0.0 ,snake_case : Union[str, Any] = 2048 ,snake_case : List[str] = False ,snake_case : str = False ,snake_case : str = 4 ,snake_case : Dict = 255 ,snake_case : Dict = 100 ,snake_case : Optional[int] = 0.1 ,snake_case : Optional[int] = 2.0 ,snake_case : List[str] = 5.0 ,snake_case : Tuple = 5.0 ,snake_case : int = 12544 ,snake_case : int = 3.0 ,snake_case : List[Any] = 0.75 ,snake_case : Union[str, Any] = 0.02 ,snake_case : Tuple = 1.0 ,snake_case : List[str] = True ,snake_case : int = [4, 8, 16, 32] ,snake_case : List[Any] = None ,**snake_case : List[str] ,):
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
SCREAMING_SNAKE_CASE =CONFIG_MAPPING["""swin"""](
image_size=224 ,in_channels=3 ,patch_size=4 ,embed_dim=96 ,depths=[2, 2, 18, 2] ,num_heads=[3, 6, 12, 24] ,window_size=7 ,drop_path_rate=0.3 ,use_absolute_embeddings=_lowerCamelCase ,out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,)
if isinstance(_lowerCamelCase ,_lowerCamelCase ):
SCREAMING_SNAKE_CASE =backbone_config.pop('model_type' )
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE =config_class.from_dict(_lowerCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
f'Supported model types: {",".join(self.backbones_supported )}' )
SCREAMING_SNAKE_CASE =backbone_config
SCREAMING_SNAKE_CASE =feature_size
SCREAMING_SNAKE_CASE =mask_feature_size
SCREAMING_SNAKE_CASE =hidden_dim
SCREAMING_SNAKE_CASE =encoder_feedforward_dim
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =dim_feedforward
SCREAMING_SNAKE_CASE =pre_norm
SCREAMING_SNAKE_CASE =enforce_input_projection
SCREAMING_SNAKE_CASE =common_stride
SCREAMING_SNAKE_CASE =ignore_value
SCREAMING_SNAKE_CASE =num_queries
SCREAMING_SNAKE_CASE =no_object_weight
SCREAMING_SNAKE_CASE =class_weight
SCREAMING_SNAKE_CASE =mask_weight
SCREAMING_SNAKE_CASE =dice_weight
SCREAMING_SNAKE_CASE =train_num_points
SCREAMING_SNAKE_CASE =oversample_ratio
SCREAMING_SNAKE_CASE =importance_sample_ratio
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =init_xavier_std
SCREAMING_SNAKE_CASE =use_auxiliary_loss
SCREAMING_SNAKE_CASE =feature_strides
SCREAMING_SNAKE_CASE =output_auxiliary_logits
SCREAMING_SNAKE_CASE =decoder_layers
super().__init__(**_lowerCamelCase )
@classmethod
def _lowerCAmelCase ( cls : Any ,snake_case : Union[str, Any] ,**snake_case : List[str] ):
return cls(
backbone_config=_lowerCamelCase ,**_lowerCamelCase ,)
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
),
'distilbert-base-uncased-finetuned-sst-2-english': (
'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
),
}
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''distilbert'''
lowerCamelCase = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , _lowerCamelCase=3_0522 , _lowerCamelCase=512 , _lowerCamelCase=False , _lowerCamelCase=6 , _lowerCamelCase=12 , _lowerCamelCase=768 , _lowerCamelCase=4 * 768 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase="gelu" , _lowerCamelCase=0.02 , _lowerCamelCase=0.1 , _lowerCamelCase=0.2 , _lowerCamelCase=0 , **_lowerCamelCase , ) -> Optional[Any]:
A_ : Tuple = vocab_size
A_ : List[Any] = max_position_embeddings
A_ : int = sinusoidal_pos_embds
A_ : int = n_layers
A_ : str = n_heads
A_ : Optional[int] = dim
A_ : int = hidden_dim
A_ : Tuple = dropout
A_ : List[Any] = attention_dropout
A_ : int = activation
A_ : Dict = initializer_range
A_ : List[Any] = qa_dropout
A_ : int = seq_classif_dropout
super().__init__(**_lowerCamelCase , pad_token_id=_lowerCamelCase )
class _lowerCAmelCase ( __A ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
A_ : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 344
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|
'''simple docstring'''
def A (__lowerCamelCase :list ):
_lowerCAmelCase = len(__lowerCamelCase )
for i in range(1 , __lowerCamelCase ):
_lowerCAmelCase = collection[i]
_lowerCAmelCase = 0
_lowerCAmelCase = i - 1
while low <= high:
_lowerCAmelCase = (low + high) // 2
if val < collection[mid]:
_lowerCAmelCase = mid - 1
else:
_lowerCAmelCase = mid + 1
for j in range(__lowerCamelCase , __lowerCamelCase , -1 ):
_lowerCAmelCase = collection[j - 1]
_lowerCAmelCase = val
return collection
if __name__ == "__main__":
_lowercase = input("""Enter numbers separated by a comma:\n""").strip()
_lowercase = [int(item) for item in user_input.split(""",""")]
print(binary_insertion_sort(unsorted))
| 354
|
'''simple docstring'''
import os
def A ():
with open(os.path.dirname(__lowerCamelCase ) + """/grid.txt""" ) as f:
_lowerCAmelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(__lowerCamelCase ) for x in f.readline().split()] )
_lowerCAmelCase = 0
# right
for i in range(20 ):
for j in range(17 ):
_lowerCAmelCase = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
_lowerCAmelCase = temp
# down
for i in range(17 ):
for j in range(20 ):
_lowerCAmelCase = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
_lowerCAmelCase = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
_lowerCAmelCase = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
_lowerCAmelCase = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
_lowerCAmelCase = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
_lowerCAmelCase = temp
return maximum
if __name__ == "__main__":
print(solution())
| 229
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCamelCase (snake_case_ ):
'''simple docstring'''
_snake_case : Tuple = '''trocr'''
_snake_case : List[str] = ['''past_key_values''']
_snake_case : Optional[int] = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self , _UpperCamelCase=5_0_2_6_5 , _UpperCamelCase=1_0_2_4 , _UpperCamelCase=1_2 , _UpperCamelCase=1_6 , _UpperCamelCase=4_0_9_6 , _UpperCamelCase="gelu" , _UpperCamelCase=5_1_2 , _UpperCamelCase=0.1 , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=0.0 , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ) -> Tuple:
UpperCAmelCase_ : str = vocab_size
UpperCAmelCase_ : str = d_model
UpperCAmelCase_ : List[Any] = decoder_layers
UpperCAmelCase_ : Any = decoder_attention_heads
UpperCAmelCase_ : Tuple = decoder_ffn_dim
UpperCAmelCase_ : Optional[Any] = activation_function
UpperCAmelCase_ : List[str] = max_position_embeddings
UpperCAmelCase_ : Optional[int] = dropout
UpperCAmelCase_ : Optional[Any] = attention_dropout
UpperCAmelCase_ : Dict = activation_dropout
UpperCAmelCase_ : str = init_std
UpperCAmelCase_ : Dict = decoder_layerdrop
UpperCAmelCase_ : List[str] = use_cache
UpperCAmelCase_ : str = scale_embedding
UpperCAmelCase_ : List[str] = use_learned_position_embeddings
UpperCAmelCase_ : Optional[Any] = layernorm_embedding
super().__init__(
pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , decoder_start_token_id=_UpperCamelCase , **_UpperCamelCase , )
| 29
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientFormerConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['EfficientFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientFormerForImageClassification',
'EfficientFormerForImageClassificationWithTeacher',
'EfficientFormerModel',
'EfficientFormerPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFEfficientFormerForImageClassification',
'TFEfficientFormerForImageClassificationWithTeacher',
'TFEfficientFormerModel',
'TFEfficientFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 175
| 0
|
'''simple docstring'''
import numpy as np
class lowercase_ :
"""simple docstring"""
def __init__( self : List[Any] ,lowercase__ : int=None ,lowercase__ : str=None ,lowercase__ : List[Any]=None ,lowercase__ : Dict=None ,lowercase__ : Tuple=None ):
self.set_matricies(red=lowercase__ ,green=lowercase__ ,blue=lowercase__ ,red_edge=lowercase__ ,nir=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : str ,lowercase__ : Dict=None ,lowercase__ : List[Any]=None ,lowercase__ : Union[str, Any]=None ,lowercase__ : Union[str, Any]=None ,lowercase__ : Union[str, Any]=None ):
if red is not None:
__lowercase = red
if green is not None:
__lowercase = green
if blue is not None:
__lowercase = blue
if red_edge is not None:
__lowercase = red_edge
if nir is not None:
__lowercase = nir
return True
def SCREAMING_SNAKE_CASE ( self : List[str] ,lowercase__ : int="" ,lowercase__ : Any=None ,lowercase__ : Union[str, Any]=None ,lowercase__ : int=None ,lowercase__ : Optional[int]=None ,lowercase__ : Tuple=None ):
self.set_matricies(red=lowercase__ ,green=lowercase__ ,blue=lowercase__ ,red_edge=lowercase__ ,nir=lowercase__ )
__lowercase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def SCREAMING_SNAKE_CASE ( self : Tuple ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def SCREAMING_SNAKE_CASE ( self : int ):
return self.nir * (self.red / (self.green**2))
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return (self.nir - self.red) / (self.nir + self.red)
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return (self.nir - self.blue) / (self.nir + self.blue)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return (self.nir - self.green) / (self.nir + self.green)
def SCREAMING_SNAKE_CASE ( self : Tuple ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def SCREAMING_SNAKE_CASE ( self : Dict ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def SCREAMING_SNAKE_CASE ( self : Dict ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : List[str]=0.0_8 ,lowercase__ : Any=1.2_2 ,lowercase__ : Dict=0.0_3 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
return (self.nir / self.green) - 1
def SCREAMING_SNAKE_CASE ( self : Tuple ):
return (self.nir / self.redEdge) - 1
def SCREAMING_SNAKE_CASE ( self : Any ):
return (self.red - self.blue) / self.red
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return self.nir - self.green
def SCREAMING_SNAKE_CASE ( self : Dict ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Dict=0.1_6 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def SCREAMING_SNAKE_CASE ( self : str ,lowercase__ : List[str]=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Dict=None ,lowercase__ : int=None ):
return (self.nir - b) / (a * self.red)
def SCREAMING_SNAKE_CASE ( self : Tuple ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return (self.red + self.green + self.blue) / 3_0.5
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return self.nir / self.red
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
return (self.rvi() - 1) / (self.rvi() + 1)
def SCREAMING_SNAKE_CASE ( self : int ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return self.green / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return self.nir / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE ( self : str ):
return self.red / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return (self.green - self.red) / (self.green + self.red)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return (self.red - self.green) / (self.red + self.green)
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
__lowercase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def SCREAMING_SNAKE_CASE ( self : Any ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return self.nir / self.red
def SCREAMING_SNAKE_CASE ( self : int ):
return (self.ndvi() + 0.5) ** (1 / 2)
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 368
|
'''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 lowercase_ :
"""simple docstring"""
def __init__( self : int ,lowercase__ : str ,lowercase__ : List[Any]=1_3 ,lowercase__ : Optional[int]=3_2 ,lowercase__ : Any=3 ,lowercase__ : int=4 ,lowercase__ : Optional[int]=[1_0, 2_0, 3_0, 4_0] ,lowercase__ : List[Any]=[2, 2, 3, 2] ,lowercase__ : List[Any]=True ,lowercase__ : Optional[Any]=True ,lowercase__ : int=3_7 ,lowercase__ : Union[str, Any]="gelu" ,lowercase__ : Tuple=1_0 ,lowercase__ : int=0.0_2 ,lowercase__ : Any=["stage2", "stage3", "stage4"] ,lowercase__ : Optional[Any]=3 ,lowercase__ : Tuple=None ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = out_features
__lowercase = num_labels
__lowercase = scope
__lowercase = num_stages
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : str ):
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 SCREAMING_SNAKE_CASE ( self : List[Any] ):
return UperNetConfig(
backbone_config=self.get_backbone_config() ,hidden_size=5_1_2 ,pool_scales=[1, 2, 3, 6] ,use_auxiliary_head=lowercase__ ,auxiliary_loss_weight=0.4 ,auxiliary_in_channels=4_0 ,auxiliary_channels=2_5_6 ,auxiliary_num_convs=1 ,auxiliary_concat_input=lowercase__ ,loss_ignore_index=2_5_5 ,num_labels=self.num_labels ,)
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[str] ,lowercase__ : Any ,lowercase__ : Any ):
__lowercase = UperNetForSemanticSegmentation(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size, self.image_size) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Tuple = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : List[Any] = False
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = UperNetModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
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 SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__ )
@unittest.skip(reason='''UperNet does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE ( self : str ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def SCREAMING_SNAKE_CASE ( self : Union[str, 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 SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE ( self : Any ):
def check_hidden_states_output(lowercase__ : List[str] ,lowercase__ : int ,lowercase__ : List[str] ):
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(lowercase__ ) ,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] ,)
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = _config_zero_init(lowercase__ )
__lowercase = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
__lowercase = model_class(config=lowercase__ )
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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = UperNetForSemanticSegmentation.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''' , repo_type='''dataset''' , filename='''ADE_val_00000001.jpg''' )
__lowercase = Image.open(A__ ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''' )
__lowercase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''' ).to(lowercase__ )
__lowercase = prepare_img()
__lowercase = processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
with torch.no_grad():
__lowercase = model(**lowercase__ )
__lowercase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = 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(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,lowercase__ ,atol=1e-4 ) )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''' )
__lowercase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''' ).to(lowercase__ )
__lowercase = prepare_img()
__lowercase = processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
with torch.no_grad():
__lowercase = model(**lowercase__ )
__lowercase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = 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(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,lowercase__ ,atol=1e-4 ) )
| 52
| 0
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowercase = '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
'''
_lowercase = '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''
_lowercase = '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: "c" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric(\'mauve\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Optional[Any] ,A_ : Union[str, Any] ,A_ : Dict=None ,A_ : Union[str, Any]=None ,A_ : List[Any]=None ,A_ : List[Any]=None ,A_ : Optional[Any]="auto" ,A_ : Optional[int]=-1 ,A_ : Dict=0.9 ,A_ : Tuple=5 ,A_ : int=500 ,A_ : List[Any]="gpt2-large" ,A_ : Dict=-1 ,A_ : int=1024 ,A_ : Optional[Any]=25 ,A_ : str=5 ,A_ : Dict=True ,A_ : Optional[int]=25 ,) -> str:
A = compute_mauve(
p_text=A_ ,q_text=A_ ,p_features=A_ ,q_features=A_ ,p_tokens=A_ ,q_tokens=A_ ,num_buckets=A_ ,pca_max_data=A_ ,kmeans_explained_var=A_ ,kmeans_num_redo=A_ ,kmeans_max_iter=A_ ,featurize_model_name=A_ ,device_id=A_ ,max_text_length=A_ ,divergence_curve_discretization_size=A_ ,mauve_scaling_factor=A_ ,verbose=A_ ,seed=A_ ,)
return out
| 74
|
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_lowercase = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
_lowercase = '''sshleifer/student_marian_en_ro_6_1'''
_lowercase = '''sshleifer/tiny-mbart'''
@require_torch
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : Union[str, Any]=False ,A_ : Optional[int]=None ,A_ : List[str]=True ,A_ : Tuple=True ,A_ : Union[str, Any]=True ,A_ : List[str]=True ,) -> Tuple:
A = self.run_trainer(
eval_steps=1 ,max_len=12 ,model_name=A_ ,num_train_epochs=1 ,distributed=A_ ,extra_args_str=A_ ,predict_with_generate=A_ ,do_train=A_ ,do_eval=A_ ,do_predict=A_ ,)
A = TrainerState.load_from_json(os.path.join(A_ ,'trainer_state.json' ) ).log_history
if not do_eval:
return
A = [log for log in logs if 'eval_loss' in log.keys()]
A = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
A = eval_metrics[-1]
assert isinstance(last_step_stats['eval_bleu'] ,A_ )
assert not math.isnan(float(last_step_stats['eval_loss'] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : str ) -> Dict:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : int ) -> int:
self.run_seqaseq_quick(distributed=A_ )
@require_torch_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
self.run_seqaseq_quick(distributed=A_ )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
self.run_seqaseq_quick(distributed=A_ ,extra_args_str='--sharded_ddp simple' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def _SCREAMING_SNAKE_CASE ( self : Any ) -> int:
self.run_seqaseq_quick(distributed=A_ ,extra_args_str='--sharded_ddp simple --fp16' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]:
self.run_seqaseq_quick(distributed=A_ ,extra_args_str='--sharded_ddp zero_dp_2' ,predict_with_generate=A_ )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
self.run_seqaseq_quick(
distributed=A_ ,extra_args_str='--sharded_ddp zero_dp_2 --fp16' ,predict_with_generate=A_ )
@require_apex
@require_torch_gpu
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A_ ,extra_args_str='--fp16 --fp16_backend=apex' )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A_ ,extra_args_str='--fp16 --fp16_backend=apex' )
@parameterized.expand(['base', 'low', 'high', 'mixed'] )
@require_torch_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : Dict ) -> List[str]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
A = {
# test with the default log_level - should be info and thus log info once
'base': {'extra_args_str': '', 'n_matches': 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
# test with high log_level and log_level_replica - should be quiet on all processes
'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
}
A = experiments[experiment_id]
A = {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
A = 'Running training'
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A_ ,extra_args_str=data['extra_args_str'] )
A = len(re.findall(A_ ,cl.err ) )
self.assertEqual(A_ ,data['n_matches'] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
A = self.run_trainer(
eval_steps=2 ,max_len=128 ,model_name=A_ ,learning_rate=3e-4 ,num_train_epochs=10 ,distributed=A_ ,)
# Check metrics
A = TrainerState.load_from_json(os.path.join(A_ ,'trainer_state.json' ) ).log_history
A = [log for log in logs if 'eval_loss' in log.keys()]
A = eval_metrics[0]
A = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats['eval_bleu'] ,A_ )
# test if do_predict saves generations and metrics
A = os.listdir(A_ )
A = {os.path.basename(A_ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A_ : str ) -> Tuple[int, float]:
A = '--skip_memory_metrics 0'
A = self.run_trainer(
max_len=128 ,model_name=A_ ,learning_rate=3e-4 ,num_train_epochs=1 ,optim=A_ ,distributed=A_ ,extra_args_str=A_ ,do_eval=A_ ,do_predict=A_ ,n_gpus_to_use=1 ,)
# Check metrics
A = TrainerState.load_from_json(Path(A_ ,'trainer_state.json' ) ).log_history
A = int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
A = int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )
A = logs[0]['train_loss']
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
A , A , A = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
A , A , A = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
A = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
A = gpu_peak_mem_orig + gpu_alloc_mem_orig
A = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
A = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
A = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A_ ,A_ ,'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
F' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
F' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB' ,)
self.assertGreater(
A_ ,A_ ,'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
F' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
F' gpu_total_mem_bnb={gpu_total_mem_bnb}MB' ,)
self.assertEqual(
A_ ,A_ ,F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}' )
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : int ,A_ : str ,A_ : int ,A_ : float = 3e-3 ,A_ : str = "adafactor" ,A_ : bool = False ,A_ : str = None ,A_ : int = 0 ,A_ : bool = True ,A_ : bool = True ,A_ : bool = True ,A_ : bool = True ,A_ : int = None ,) -> Dict:
A = self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
A = self.get_auto_remove_tmp_dir()
A = F'\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A_ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A_ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n '.split()
A = F'\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A_ )}\n '.split()
A = '\n --do_predict\n '.split()
A = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F'--optim {optim}'.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
A = get_gpu_count()
A = get_torch_dist_unique_port()
A = F'\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n '.split()
A = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A_ ,env=self.get_env() )
else:
A = ['run_translation.py'] + args
with patch.object(A_ ,'argv' ,A_ ):
main()
return output_dir
| 74
| 1
|
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
UpperCamelCase_ = (
"https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
UpperCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
def A ( ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = '''https://pypi.org/pypi/diffusers/json'''
UpperCAmelCase_ = json.loads(request.urlopen(__UpperCAmelCase ).read() )['''releases'''].keys()
return sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase : version.Version(__UpperCAmelCase ) )
def A ( ) -> Any:
'''simple docstring'''
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__UpperCAmelCase )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
UpperCAmelCase_ = Path(__UpperCAmelCase ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def A ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
init_hf_modules()
UpperCAmelCase_ = Path(__UpperCAmelCase ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
UpperCAmelCase_ = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def A ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
with open(__UpperCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
UpperCAmelCase_ = f.read()
# Imports of the form `import .xxx`
UpperCAmelCase_ = re.findall('''^\s*import\s+\.(\S+)\s*$''' , __UpperCAmelCase , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __UpperCAmelCase , flags=re.MULTILINE )
# Unique-ify
return list(set(__UpperCAmelCase ) )
def A ( __UpperCAmelCase ) -> str:
'''simple docstring'''
UpperCAmelCase_ = False
UpperCAmelCase_ = [module_file]
UpperCAmelCase_ = []
# Let's recurse through all relative imports
while not no_change:
UpperCAmelCase_ = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__UpperCAmelCase ) )
UpperCAmelCase_ = Path(__UpperCAmelCase ).parent
UpperCAmelCase_ = [str(module_path / m ) for m in new_imports]
UpperCAmelCase_ = [f for f in new_import_files if f not in all_relative_imports]
UpperCAmelCase_ = [f"{f}.py" for f in new_import_files]
UpperCAmelCase_ = len(__UpperCAmelCase ) == 0
all_relative_imports.extend(__UpperCAmelCase )
return all_relative_imports
def A ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
with open(__UpperCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
UpperCAmelCase_ = f.read()
# Imports of the form `import xxx`
UpperCAmelCase_ = re.findall('''^\s*import\s+(\S+)\s*$''' , __UpperCAmelCase , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __UpperCAmelCase , flags=re.MULTILINE )
# Only keep the top-level module
UpperCAmelCase_ = [imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
UpperCAmelCase_ = list(set(__UpperCAmelCase ) )
UpperCAmelCase_ = []
for imp in imports:
try:
importlib.import_module(__UpperCAmelCase )
except ImportError:
missing_packages.append(__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
f"{', '.join(__UpperCAmelCase )}. Run `pip install {' '.join(__UpperCAmelCase )}`" )
return get_relative_imports(__UpperCAmelCase )
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = module_path.replace(os.path.sep , '''.''' )
UpperCAmelCase_ = importlib.import_module(__UpperCAmelCase )
if class_name is None:
return find_pipeline_class(__UpperCAmelCase )
return getattr(__UpperCAmelCase , __UpperCAmelCase )
def A ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from ..pipelines import DiffusionPipeline
UpperCAmelCase_ = dict(inspect.getmembers(__UpperCAmelCase , inspect.isclass ) )
UpperCAmelCase_ = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __UpperCAmelCase )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
f" {loaded_module}." )
UpperCAmelCase_ = cls
return pipeline_class
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = str(__UpperCAmelCase )
UpperCAmelCase_ = os.path.join(__UpperCAmelCase , __UpperCAmelCase )
if os.path.isfile(__UpperCAmelCase ):
UpperCAmelCase_ = module_file_or_url
UpperCAmelCase_ = '''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
UpperCAmelCase_ = get_diffusers_versions()
# cut ".dev0"
UpperCAmelCase_ = '''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
UpperCAmelCase_ = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(f"Defaulting to latest_version: {revision}." )
elif revision in available_versions:
UpperCAmelCase_ = f"v{revision}"
elif revision == "main":
UpperCAmelCase_ = revision
else:
raise ValueError(
f"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
f" {', '.join(available_versions + ['main'] )}." )
# community pipeline on GitHub
UpperCAmelCase_ = COMMUNITY_PIPELINES_URL.format(revision=__UpperCAmelCase , pipeline=__UpperCAmelCase )
try:
UpperCAmelCase_ = cached_download(
__UpperCAmelCase , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , proxies=__UpperCAmelCase , resume_download=__UpperCAmelCase , local_files_only=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , )
UpperCAmelCase_ = '''git'''
UpperCAmelCase_ = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}." )
raise
else:
try:
# Load from URL or cache if already cached
UpperCAmelCase_ = hf_hub_download(
__UpperCAmelCase , __UpperCAmelCase , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , proxies=__UpperCAmelCase , resume_download=__UpperCAmelCase , local_files_only=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , )
UpperCAmelCase_ = os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}." )
raise
# Check we have all the requirements in our environment
UpperCAmelCase_ = check_imports(__UpperCAmelCase )
# Now we move the module inside our cached dynamic modules.
UpperCAmelCase_ = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__UpperCAmelCase )
UpperCAmelCase_ = Path(__UpperCAmelCase ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__UpperCAmelCase , submodule_path / module_file )
for module_needed in modules_needed:
UpperCAmelCase_ = f"{module_needed}.py"
shutil.copy(os.path.join(__UpperCAmelCase , __UpperCAmelCase ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase_ = use_auth_token
elif use_auth_token is True:
UpperCAmelCase_ = HfFolder.get_token()
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = model_info(__UpperCAmelCase , revision=__UpperCAmelCase , token=__UpperCAmelCase ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
UpperCAmelCase_ = submodule_path / commit_hash
UpperCAmelCase_ = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__UpperCAmelCase )
if not (submodule_path / module_file).exists():
shutil.copy(__UpperCAmelCase , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__UpperCAmelCase , f"{module_needed}.py" , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , resume_download=__UpperCAmelCase , proxies=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , revision=__UpperCAmelCase , local_files_only=__UpperCAmelCase , )
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , **__UpperCAmelCase , ) -> int:
'''simple docstring'''
UpperCAmelCase_ = get_cached_module_file(
__UpperCAmelCase , __UpperCAmelCase , cache_dir=__UpperCAmelCase , force_download=__UpperCAmelCase , resume_download=__UpperCAmelCase , proxies=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , revision=__UpperCAmelCase , local_files_only=__UpperCAmelCase , )
return get_class_in_module(__UpperCAmelCase , final_module.replace('''.py''' , '''''' ) )
| 344
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class a_ ( _snake_case ):
UpperCamelCase__ : Dict ="open-llama"
def __init__( self :Union[str, Any] , _lowercase :List[Any]=100000 , _lowercase :Dict=4096 , _lowercase :List[Any]=11008 , _lowercase :Optional[int]=32 , _lowercase :Union[str, Any]=32 , _lowercase :List[str]="silu" , _lowercase :Union[str, Any]=2048 , _lowercase :Any=0.02 , _lowercase :Optional[Any]=1E-6 , _lowercase :str=True , _lowercase :str=0 , _lowercase :Any=1 , _lowercase :Optional[Any]=2 , _lowercase :str=False , _lowercase :Dict=True , _lowercase :Optional[Any]=0.1 , _lowercase :Tuple=0.1 , _lowercase :Dict=True , _lowercase :List[Any]=True , _lowercase :Dict=None , **_lowercase :Optional[int] , ) -> List[Any]:
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = rms_norm_eps
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = kwargs.pop(
'''use_memorry_efficient_attention''' , _lowercase)
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_dropout_prob
UpperCAmelCase_ = use_stable_embedding
UpperCAmelCase_ = shared_input_output_embedding
UpperCAmelCase_ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , tie_word_embeddings=_lowercase , **_lowercase , )
def __a ( self :int) -> str:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _lowercase) or len(self.rope_scaling) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
f"got {self.rope_scaling}")
UpperCAmelCase_ = self.rope_scaling.get('''type''' , _lowercase)
UpperCAmelCase_ = self.rope_scaling.get('''factor''' , _lowercase)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(_lowercase , _lowercase) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 344
| 1
|
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCAmelCase__ : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( _snake_case ):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(_snake_case ):
return ext
raise Exception(
f'''Unable to determine file format from file extension {path}. '''
f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}''' )
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : int = pipeline(
task=args.task ,model=args.model if args.model else None ,config=args.config ,tokenizer=args.tokenizer ,device=args.device ,)
SCREAMING_SNAKE_CASE__ : Tuple = try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
SCREAMING_SNAKE_CASE__ : Tuple = PipelineDataFormat.from_str(
format=_snake_case ,output_path=args.output ,input_path=args.input ,column=args.column if args.column else nlp.default_input_names ,overwrite=args.overwrite ,)
return RunCommand(_snake_case ,_snake_case )
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = nlp
SCREAMING_SNAKE_CASE__ : Tuple = reader
@staticmethod
def __magic_name__ (SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parser.add_parser("""run""" , help="""Run a pipeline through the CLI""" )
run_parser.add_argument("""--task""" , choices=get_supported_tasks() , help="""Task to run""" )
run_parser.add_argument("""--input""" , type=SCREAMING_SNAKE_CASE__ , help="""Path to the file to use for inference""" )
run_parser.add_argument("""--output""" , type=SCREAMING_SNAKE_CASE__ , help="""Path to the file that will be used post to write results.""" )
run_parser.add_argument("""--model""" , type=SCREAMING_SNAKE_CASE__ , help="""Name or path to the model to instantiate.""" )
run_parser.add_argument("""--config""" , type=SCREAMING_SNAKE_CASE__ , help="""Name or path to the model's config to instantiate.""" )
run_parser.add_argument(
"""--tokenizer""" , type=SCREAMING_SNAKE_CASE__ , help="""Name of the tokenizer to use. (default: same as the model name)""" )
run_parser.add_argument(
"""--column""" , type=SCREAMING_SNAKE_CASE__ , help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" , )
run_parser.add_argument(
"""--format""" , type=SCREAMING_SNAKE_CASE__ , default="""infer""" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="""Input format to read from""" , )
run_parser.add_argument(
"""--device""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" , )
run_parser.add_argument("""--overwrite""" , action="""store_true""" , help="""Allow overwriting the output file.""" )
run_parser.set_defaults(func=SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self._nlp, []
for entry in self._reader:
SCREAMING_SNAKE_CASE__ : List[Any] = nlp(**SCREAMING_SNAKE_CASE__ ) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
outputs.append(SCREAMING_SNAKE_CASE__ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
SCREAMING_SNAKE_CASE__ : Tuple = self._reader.save_binary(SCREAMING_SNAKE_CASE__ )
logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}''' )
else:
self._reader.save(SCREAMING_SNAKE_CASE__ )
| 25
|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowercase = parser.add_subparsers(help='diffusers-cli command helpers' )
# Register commands
EnvironmentCommand.register_subcommand(__snake_case )
# Let's go
lowercase = parser.parse_args()
if not hasattr(__snake_case , 'func' ):
parser.print_help()
exit(1 )
# Run
lowercase = args.func(__snake_case )
service.run()
if __name__ == "__main__":
main()
| 220
| 0
|
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
A_ : Optional[Any] = [8, 5, 9, 7]
A_ : Dict = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A_ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
lowerCamelCase__ : Tuple = claim_vector
lowerCamelCase__ : Optional[int] = allocated_resources_table
lowerCamelCase__ : Dict = maximum_claim_table
def a__ (self ):
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def a__ (self ):
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def a__ (self ):
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_A ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def a__ (self ):
'''simple docstring'''
return {self.__need().index(_A ): i for i in self.__need()}
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.__need()
lowerCamelCase__ : Any = self.__allocated_resources_table
lowerCamelCase__ : List[str] = self.__available_resources()
lowerCamelCase__ : Any = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 5_0 + '\n' )
while need_list:
lowerCamelCase__ : Union[str, Any] = False
for each_need in need_list:
lowerCamelCase__ : str = True
for index, need in enumerate(_A ):
if need > available_resources[index]:
lowerCamelCase__ : Any = False
break
if execution:
lowerCamelCase__ : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowerCamelCase__ : int = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_A )
# update available/freed resources stack
lowerCamelCase__ : str = np.array(_A ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(_A ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def a__ (self ):
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_A ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_A ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(_A ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(_A ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364
|
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
A_ : Optional[Any] = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
A_ : List[Any] = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
A_ : str = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
A_ : str = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
A_ : Optional[Any] = "allenai"
def lowerCamelCase_ ( _lowerCamelCase ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
lowerCamelCase__ : List[Any] = dict((re.sub(r'@@$' , '' , _lowerCamelCase ), v) if k.endswith('@@' ) else (re.sub(r'$' , '</w>' , _lowerCamelCase ), v) for k, v in d.items() )
lowerCamelCase__ : int = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[f'''{k}</w>''']
lowerCamelCase__ : List[str] = d[k] # restore
return da
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
# prep
assert os.path.exists(_lowerCamelCase )
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
print(f'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
lowerCamelCase__ : Optional[int] = basename(_lowerCamelCase )
lowerCamelCase__ : str = dirname(_lowerCamelCase )
lowerCamelCase__ : Any = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
lowerCamelCase__ : int = cls.hub_models()
lowerCamelCase__ : str = {'bpe': 'fastbpe', 'tokenizer': 'moses'}
lowerCamelCase__ : Optional[Any] = '.'
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(f'''using checkpoint {checkpoint_file}''' )
lowerCamelCase__ : Any = hub_utils.from_pretrained(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , archive_map=_lowerCamelCase , **_lowerCamelCase )
lowerCamelCase__ : List[str] = vars(chkpt['args']['model'] )
lowerCamelCase__ : Optional[Any] = args['source_lang']
lowerCamelCase__ : List[str] = args['target_lang']
lowerCamelCase__ : List[str] = dirname(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = basename(_lowerCamelCase )
# dicts
lowerCamelCase__ : Optional[Any] = os.path.join(_lowerCamelCase , f'''dict.{src_lang}.txt''' )
lowerCamelCase__ : Optional[Any] = os.path.join(_lowerCamelCase , f'''dict.{tgt_lang}.txt''' )
lowerCamelCase__ : Dict = Dictionary.load(_lowerCamelCase )
lowerCamelCase__ : List[Any] = rewrite_dict_keys(src_dict.indices )
lowerCamelCase__ : int = len(_lowerCamelCase )
lowerCamelCase__ : List[Any] = os.path.join(_lowerCamelCase , 'vocab-src.json' )
print(f'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_lowerCamelCase , ensure_ascii=_lowerCamelCase , indent=_lowerCamelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
lowerCamelCase__ : Optional[int] = True
for k in src_vocab.keys():
if not k.islower():
lowerCamelCase__ : int = False
break
lowerCamelCase__ : str = Dictionary.load(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
lowerCamelCase__ : Optional[Any] = len(_lowerCamelCase )
lowerCamelCase__ : Dict = os.path.join(_lowerCamelCase , 'vocab-tgt.json' )
print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_lowerCamelCase , ensure_ascii=_lowerCamelCase , indent=_lowerCamelCase ) )
# merges_file (bpecodes)
lowerCamelCase__ : List[Any] = os.path.join(_lowerCamelCase , VOCAB_FILES_NAMES['merges_file'] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
lowerCamelCase__ : Optional[int] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.exists(_lowerCamelCase ):
break
with open(_lowerCamelCase , encoding='utf-8' ) as fin:
lowerCamelCase__ : Union[str, Any] = fin.read()
lowerCamelCase__ : Any = re.sub(r' \d+$' , '' , _lowerCamelCase , 0 , re.M ) # remove frequency number
print(f'''Generating {merges_file}''' )
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as fout:
fout.write(_lowerCamelCase )
# model config
lowerCamelCase__ : Dict = os.path.join(_lowerCamelCase , 'config.json' )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", f'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", f'''need to extend tokenizer to support bpe={args['tokenizer']}'''
lowerCamelCase__ : Optional[int] = {
'architectures': ['FSMTForConditionalGeneration'],
'model_type': 'fsmt',
'activation_dropout': args['activation_dropout'],
'activation_function': 'relu',
'attention_dropout': args['attention_dropout'],
'd_model': args['decoder_embed_dim'],
'dropout': args['dropout'],
'init_std': 0.02,
'max_position_embeddings': args['max_source_positions'],
'num_hidden_layers': args['encoder_layers'],
'src_vocab_size': src_vocab_size,
'tgt_vocab_size': tgt_vocab_size,
'langs': [src_lang, tgt_lang],
'encoder_attention_heads': args['encoder_attention_heads'],
'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
'encoder_layerdrop': args['encoder_layerdrop'],
'encoder_layers': args['encoder_layers'],
'decoder_attention_heads': args['decoder_attention_heads'],
'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
'decoder_layerdrop': args['decoder_layerdrop'],
'decoder_layers': args['decoder_layers'],
'bos_token_id': 0,
'pad_token_id': 1,
'eos_token_id': 2,
'is_encoder_decoder': True,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_all_embeddings'],
}
# good hparam defaults to start with
lowerCamelCase__ : str = 5
lowerCamelCase__ : Tuple = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
lowerCamelCase__ : List[str] = best_score_hparams[model_dir]['length_penalty']
else:
lowerCamelCase__ : List[Any] = 1.0
print(f'''Generating {fsmt_model_config_file}''' )
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_lowerCamelCase , ensure_ascii=_lowerCamelCase , indent=_lowerCamelCase ) )
# tokenizer config
lowerCamelCase__ : Dict = os.path.join(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : int = {
'langs': [src_lang, tgt_lang],
'model_max_length': 1024,
'do_lower_case': do_lower_case,
}
print(f'''Generating {fsmt_tokenizer_config_file}''' )
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_lowerCamelCase , ensure_ascii=_lowerCamelCase , indent=_lowerCamelCase ) )
# model
lowerCamelCase__ : List[str] = chkpt['models'][0]
lowerCamelCase__ : Optional[Any] = model.state_dict()
# rename keys to start with 'model.'
lowerCamelCase__ : str = OrderedDict(('model.' + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
lowerCamelCase__ : int = [
'model.model',
'model.encoder.version',
'model.decoder.version',
'model.encoder_embed_tokens.weight',
'model.decoder_embed_tokens.weight',
'model.encoder.embed_positions._float_tensor',
'model.decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
model_state_dict.pop(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = FSMTConfig.from_pretrained(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = FSMTForConditionalGeneration(_lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(_lowerCamelCase , strict=_lowerCamelCase )
# save
lowerCamelCase__ : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
print(f'''Generating {pytorch_weights_dump_path}''' )
torch.save(_lowerCamelCase , _lowerCamelCase )
print('Conversion is done!' )
print('\nLast step is to upload the files to s3' )
print(f'''cd {data_root}''' )
print(f'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
A_ : Dict = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 316
| 0
|
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ : Optional[int] = """true"""
def __snake_case ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str=82 , UpperCAmelCase_ : int=16 ):
set_seed(42 )
lowerCamelCase_ = RegressionModel()
lowerCamelCase_ = deepcopy(UpperCAmelCase_ )
lowerCamelCase_ = RegressionDataset(length=UpperCAmelCase_ )
lowerCamelCase_ = DataLoader(UpperCAmelCase_ , batch_size=UpperCAmelCase_ )
model.to(accelerator.device )
lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(UpperCAmelCase_ , UpperCAmelCase_ )
return model, ddp_model, dataloader
def __snake_case ( UpperCAmelCase_ : Accelerator , UpperCAmelCase_ : str=False ):
lowerCamelCase_ = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
lowerCamelCase_ = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCAmelCase_ : Union[str, Any] ):
lowerCamelCase_ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCAmelCase_ , max_length=UpperCAmelCase_ )
return outputs
with accelerator.main_process_first():
lowerCamelCase_ = dataset.map(
UpperCAmelCase_ , batched=UpperCAmelCase_ , remove_columns=["idx", "sentence1", "sentence2"] , )
lowerCamelCase_ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCAmelCase_ : int ):
if use_longest:
return tokenizer.pad(UpperCAmelCase_ , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCAmelCase_ , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCAmelCase_ , shuffle=UpperCAmelCase_ , collate_fn=UpperCAmelCase_ , batch_size=16 )
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : Any ):
lowerCamelCase_ = Accelerator(dispatch_batches=UpperCAmelCase_ , split_batches=UpperCAmelCase_ )
lowerCamelCase_ = get_dataloader(UpperCAmelCase_ , not dispatch_batches )
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCAmelCase_ )
lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(UpperCAmelCase_ , UpperCAmelCase_ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __snake_case ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ):
lowerCamelCase_ = []
for batch in dataloader:
lowerCamelCase_ ,lowerCamelCase_ = batch.values()
with torch.no_grad():
lowerCamelCase_ = model(UpperCAmelCase_ )
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowerCamelCase_ ,lowerCamelCase_ = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCAmelCase_ )
targs.append(UpperCAmelCase_ )
lowerCamelCase_ ,lowerCamelCase_ = torch.cat(UpperCAmelCase_ ), torch.cat(UpperCAmelCase_ )
return logits, targs
def __snake_case ( UpperCAmelCase_ : Accelerator , UpperCAmelCase_ : Optional[Any]=82 , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : str=False , UpperCAmelCase_ : Union[str, Any]=16 ):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = get_basic_setup(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
lowerCamelCase_ ,lowerCamelCase_ = generate_predictions(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
assert (
len(UpperCAmelCase_ ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCAmelCase_ )}'''
def __snake_case ( UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False ):
lowerCamelCase_ = evaluate.load("glue" , "mrpc" )
lowerCamelCase_ ,lowerCamelCase_ = get_mrpc_setup(UpperCAmelCase_ , UpperCAmelCase_ )
# First do baseline
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = setup["no"]
model.to(UpperCAmelCase_ )
model.eval()
for batch in dataloader:
batch.to(UpperCAmelCase_ )
with torch.inference_mode():
lowerCamelCase_ = model(**UpperCAmelCase_ )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCAmelCase_ , references=batch["labels"] )
lowerCamelCase_ = metric.compute()
# Then do distributed
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowerCamelCase_ = model(**UpperCAmelCase_ )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
lowerCamelCase_ = batch["labels"]
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCAmelCase_ , references=UpperCAmelCase_ )
lowerCamelCase_ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __snake_case ( ):
lowerCamelCase_ = Accelerator(split_batches=UpperCAmelCase_ , dispatch_batches=UpperCAmelCase_ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(UpperCAmelCase_ , UpperCAmelCase_ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowerCamelCase_ = Accelerator(split_batches=UpperCAmelCase_ , dispatch_batches=UpperCAmelCase_ )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(UpperCAmelCase_ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
lowerCamelCase_ = Accelerator()
test_torch_metrics(UpperCAmelCase_ , 512 )
accelerator.state._reset_state()
def __snake_case ( UpperCAmelCase_ : int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 55
|
'''simple docstring'''
a_ : Any = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 55
| 1
|
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class UpperCamelCase :
def __init__(self : Union[str, Any] , _A : List[str] , ) -> Union[str, Any]:
__snake_case : List[Any] = parent
__snake_case : Any = 13
__snake_case : Any = 7
__snake_case : List[Any] = 30
__snake_case : Union[str, Any] = self.seq_length + self.mem_len
__snake_case : Optional[Any] = 15
__snake_case : int = True
__snake_case : Any = True
__snake_case : List[Any] = 99
__snake_case : Optional[int] = [10, 50, 80]
__snake_case : List[Any] = 32
__snake_case : List[str] = 32
__snake_case : Optional[int] = 4
__snake_case : Any = 8
__snake_case : Optional[int] = 1_28
__snake_case : List[str] = 2
__snake_case : List[Any] = 2
__snake_case : int = None
__snake_case : Optional[int] = 1
__snake_case : str = 0
__snake_case : Optional[int] = 3
__snake_case : Any = self.vocab_size - 1
__snake_case : Tuple = 0.01
def _lowercase (self : List[str]) -> Dict:
__snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__snake_case : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__snake_case : Dict = None
if self.use_labels:
__snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__snake_case : Tuple = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def _lowercase (self : Optional[int]) -> List[str]:
random.seed(self.seed)
tf.random.set_seed(self.seed)
def _lowercase (self : List[Any] , _A : Dict , _A : List[str] , _A : List[str] , _A : str) -> str:
__snake_case : Optional[int] = TFTransfoXLModel(_A)
__snake_case , __snake_case : Optional[Any] = model(_A).to_tuple()
__snake_case : str = {'input_ids': input_ids_a, 'mems': mems_a}
__snake_case , __snake_case : Union[str, Any] = model(_A).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _lowercase (self : Optional[Any] , _A : Dict , _A : List[Any] , _A : Union[str, Any] , _A : Tuple) -> int:
__snake_case : Union[str, Any] = TFTransfoXLLMHeadModel(_A)
__snake_case , __snake_case : Tuple = model(_A).to_tuple()
__snake_case : Tuple = {'input_ids': input_ids_a, 'labels': lm_labels}
__snake_case , __snake_case : Optional[Any] = model(_A).to_tuple()
__snake_case , __snake_case : Union[str, Any] = model([input_ids_a, mems_a]).to_tuple()
__snake_case : str = {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}
__snake_case , __snake_case : Optional[int] = model(_A).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _lowercase (self : Tuple , _A : Union[str, Any] , _A : Dict , _A : Optional[Any] , _A : Optional[int]) -> Union[str, Any]:
__snake_case : Optional[int] = TFTransfoXLForSequenceClassification(_A)
__snake_case : Tuple = model(_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowercase (self : int) -> Optional[Any]:
__snake_case : List[str] = self.prepare_config_and_inputs()
((__snake_case) , (__snake_case) , (__snake_case) , (__snake_case)) : int = config_and_inputs
__snake_case : Dict = {'input_ids': input_ids_a}
return config, inputs_dict
@require_tf
class UpperCamelCase ( lowercase , lowercase , unittest.TestCase ):
UpperCAmelCase : Dict = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
UpperCAmelCase : Union[str, Any] = () if is_tf_available() else ()
UpperCAmelCase : Tuple = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Optional[Any] = False
UpperCAmelCase : Tuple = False
UpperCAmelCase : Optional[int] = False
def _lowercase (self : str , _A : Union[str, Any] , _A : List[str] , _A : Optional[int] , _A : str , _A : Union[str, Any]) -> Tuple:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def _lowercase (self : Optional[int]) -> int:
__snake_case : Optional[int] = TFTransfoXLModelTester(self)
__snake_case : Union[str, Any] = ConfigTester(self , config_class=_A , d_embed=37)
def _lowercase (self : Union[str, Any]) -> Optional[Any]:
self.config_tester.run_common_tests()
def _lowercase (self : str) -> Dict:
self.model_tester.set_seed()
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*_A)
def _lowercase (self : Any) -> Union[str, Any]:
self.model_tester.set_seed()
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*_A)
def _lowercase (self : Any) -> Optional[int]:
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_A)
def _lowercase (self : List[str]) -> List[Any]:
__snake_case , __snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : int = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
__snake_case : Optional[int] = model_class(_A)
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer)
if model_class in list_other_models_with_output_ebd:
__snake_case : Tuple = model.get_output_embeddings()
assert isinstance(_A , tf.keras.layers.Layer)
__snake_case : Union[str, Any] = model.get_bias()
assert name is None
else:
__snake_case : Optional[int] = model.get_output_embeddings()
assert x is None
__snake_case : Union[str, Any] = model.get_bias()
assert name is None
def _lowercase (self : Any) -> Dict:
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def _lowercase (self : int) -> Optional[int]:
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Any = TFTransfoXLModel.from_pretrained(_A)
self.assertIsNotNone(_A)
@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.')
def _lowercase (self : List[Any]) -> List[Any]:
pass
@require_tf
class UpperCamelCase ( unittest.TestCase ):
@unittest.skip('Skip test until #12651 is resolved.')
@slow
def _lowercase (self : int) -> Optional[int]:
__snake_case : Dict = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
# fmt: off
__snake_case : Union[str, Any] = tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] , dtype=tf.intaa) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
__snake_case : List[str] = [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
__snake_case : Dict = model.generate(_A , max_length=2_00 , do_sample=_A)
self.assertListEqual(output_ids[0].numpy().tolist() , _A)
| 95
|
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_a : int= NewType("DataClass", Any)
_a : Dict= NewType("DataClassType", Any)
def __UpperCAmelCase ( UpperCAmelCase_ : Any ) -> Optional[Any]:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)." )
def __UpperCAmelCase ( UpperCAmelCase_ : list ) -> Callable[[str], Any]:
'''simple docstring'''
__snake_case : str = {str(UpperCAmelCase_ ): choice for choice in choices}
return lambda UpperCAmelCase_ : str_to_choice.get(UpperCAmelCase_ , UpperCAmelCase_ )
def __UpperCAmelCase ( *,
UpperCAmelCase_ : Union[str, List[str]] = None , UpperCAmelCase_ : str = None , UpperCAmelCase_ : Any = dataclasses.MISSING , UpperCAmelCase_ : Callable[[], Any] = dataclasses.MISSING , UpperCAmelCase_ : dict = None , **UpperCAmelCase_ : str , ) -> dataclasses.Field:
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__snake_case : Optional[Any] = {}
if aliases is not None:
__snake_case : Optional[int] = aliases
if help is not None:
__snake_case : Optional[int] = help
return dataclasses.field(metadata=UpperCAmelCase_ , default=UpperCAmelCase_ , default_factory=UpperCAmelCase_ , **UpperCAmelCase_ )
class UpperCamelCase ( lowercase ):
UpperCAmelCase : Iterable[DataClassType]
def __init__(self : Tuple , _A : Union[DataClassType, Iterable[DataClassType]] , **_A : int) -> int:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__snake_case : Union[str, Any] = ArgumentDefaultsHelpFormatter
super().__init__(**_A)
if dataclasses.is_dataclass(_A):
__snake_case : Optional[int] = [dataclass_types]
__snake_case : Dict = list(_A)
for dtype in self.dataclass_types:
self._add_dataclass_arguments(_A)
@staticmethod
def _lowercase (_A : ArgumentParser , _A : dataclasses.Field) -> Tuple:
__snake_case : Union[str, Any] = f"--{field.name}"
__snake_case : Optional[int] = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , _A):
raise RuntimeError(
'Unresolved type detected, which should have been done with the help of '
'`typing.get_type_hints` method by default')
__snake_case : Any = kwargs.pop('aliases' , [])
if isinstance(_A , _A):
__snake_case : Optional[Any] = [aliases]
__snake_case : Tuple = getattr(field.type , '__origin__' , field.type)
if origin_type is Union or (hasattr(_A , 'UnionType') and isinstance(_A , types.UnionType)):
if str not in field.type.__args__ and (
len(field.type.__args__) != 2 or type(_A) not in field.type.__args__
):
raise ValueError(
'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
' the argument parser only supports one type per argument.'
f" Problem encountered in field '{field.name}'.")
if type(_A) not in field.type.__args__:
# filter `str` in Union
__snake_case : Tuple = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__snake_case : Optional[int] = getattr(field.type , '__origin__' , field.type)
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__snake_case : Optional[Any] = (
field.type.__args__[0] if isinstance(_A , field.type.__args__[1]) else field.type.__args__[1]
)
__snake_case : Tuple = getattr(field.type , '__origin__' , field.type)
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__snake_case : Optional[int] = {}
if origin_type is Literal or (isinstance(field.type , _A) and issubclass(field.type , _A)):
if origin_type is Literal:
__snake_case : Tuple = field.type.__args__
else:
__snake_case : Dict = [x.value for x in field.type]
__snake_case : Dict = make_choice_type_function(kwargs['choices'])
if field.default is not dataclasses.MISSING:
__snake_case : Dict = field.default
else:
__snake_case : Union[str, Any] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__snake_case : Tuple = copy(_A)
# Hack because type=bool in argparse does not behave as we want.
__snake_case : Dict = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__snake_case : str = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__snake_case : Any = default
# This tells argparse we accept 0 or 1 value after --field_name
__snake_case : Dict = '?'
# This is the value that will get picked if we do --field_name (without value)
__snake_case : List[str] = True
elif isclass(_A) and issubclass(_A , _A):
__snake_case : str = field.type.__args__[0]
__snake_case : Any = '+'
if field.default_factory is not dataclasses.MISSING:
__snake_case : List[str] = field.default_factory()
elif field.default is dataclasses.MISSING:
__snake_case : Any = True
else:
__snake_case : Tuple = field.type
if field.default is not dataclasses.MISSING:
__snake_case : Optional[int] = field.default
elif field.default_factory is not dataclasses.MISSING:
__snake_case : List[Any] = field.default_factory()
else:
__snake_case : Union[str, Any] = True
parser.add_argument(_A , *_A , **_A)
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__snake_case : List[str] = False
parser.add_argument(f"--no_{field.name}" , action='store_false' , dest=field.name , **_A)
def _lowercase (self : List[Any] , _A : DataClassType) -> Optional[int]:
if hasattr(_A , '_argument_group_name'):
__snake_case : Union[str, Any] = self.add_argument_group(dtype._argument_group_name)
else:
__snake_case : int = self
try:
__snake_case : Dict[str, type] = get_type_hints(_A)
except NameError:
raise RuntimeError(
f"Type resolution failed for {dtype}. Try declaring the class in global scope or "
'removing line of `from __future__ import annotations` which opts in Postponed '
'Evaluation of Annotations (PEP 563)')
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_A):
__snake_case : Union[str, Any] = '.'.join(map(_A , sys.version_info[:3]))
raise RuntimeError(
f"Type resolution failed for {dtype} on Python {python_version}. Try removing "
'line of `from __future__ import annotations` which opts in union types as '
'`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
'support Python versions that lower than 3.10, you need to use '
'`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
'`X | None`.') from ex
raise
for field in dataclasses.fields(_A):
if not field.init:
continue
__snake_case : Optional[Any] = type_hints[field.name]
self._parse_dataclass_field(_A , _A)
def _lowercase (self : Union[str, Any] , _A : List[Any]=None , _A : Optional[Any]=False , _A : int=True , _A : List[Any]=None , _A : str=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
__snake_case : Any = []
if args_filename:
args_files.append(Path(_A))
elif look_for_args_file and len(sys.argv):
args_files.append(Path(sys.argv[0]).with_suffix('.args'))
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__snake_case : int = ArgumentParser()
args_file_parser.add_argument(_A , type=_A , action='append')
# Use only remaining args for further parsing (remove the args_file_flag)
__snake_case , __snake_case : int = args_file_parser.parse_known_args(args=_A)
__snake_case : int = vars(_A).get(args_file_flag.lstrip('-') , _A)
if cmd_args_file_paths:
args_files.extend([Path(_A) for p in cmd_args_file_paths])
__snake_case : Optional[int] = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__snake_case : List[str] = file_args + args if args is not None else file_args + sys.argv[1:]
__snake_case , __snake_case : Tuple = self.parse_known_args(args=_A)
__snake_case : Dict = []
for dtype in self.dataclass_types:
__snake_case : List[Any] = {f.name for f in dataclasses.fields(_A) if f.init}
__snake_case : List[str] = {k: v for k, v in vars(_A).items() if k in keys}
for k in keys:
delattr(_A , _A)
__snake_case : List[str] = dtype(**_A)
outputs.append(_A)
if len(namespace.__dict__) > 0:
# additional namespace.
outputs.append(_A)
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
return (*outputs,)
def _lowercase (self : Tuple , _A : Dict[str, Any] , _A : bool = False) -> Tuple[DataClass, ...]:
__snake_case : List[Any] = set(args.keys())
__snake_case : Dict = []
for dtype in self.dataclass_types:
__snake_case : List[str] = {f.name for f in dataclasses.fields(_A) if f.init}
__snake_case : Union[str, Any] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys())
__snake_case : List[str] = dtype(**_A)
outputs.append(_A)
if not allow_extra_keys and unused_keys:
raise ValueError(f"Some keys are not used by the HfArgumentParser: {sorted(_A)}")
return tuple(_A)
def _lowercase (self : int , _A : str , _A : bool = False) -> Tuple[DataClass, ...]:
with open(Path(_A) , encoding='utf-8') as open_json_file:
__snake_case : int = json.loads(open_json_file.read())
__snake_case : Optional[int] = self.parse_dict(_A , allow_extra_keys=_A)
return tuple(_A)
def _lowercase (self : List[str] , _A : str , _A : bool = False) -> Tuple[DataClass, ...]:
__snake_case : Dict = self.parse_dict(yaml.safe_load(Path(_A).read_text()) , allow_extra_keys=_A)
return tuple(_A)
| 95
| 1
|
import argparse
from collections import defaultdict
import yaml
A_ :Tuple = '''docs/source/en/_toctree.yml'''
def A ( a_ ) -> Tuple:
__UpperCamelCase : Tuple =defaultdict(a_ )
for doc in model_doc:
counts[doc["local"]] += 1
__UpperCamelCase : int =[key for key, value in counts.items() if value > 1]
__UpperCamelCase : Any =[]
for duplicate_key in duplicates:
__UpperCamelCase : Union[str, Any] =list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(a_ ) > 1:
raise ValueError(
F'{duplicate_key} is present several times in the documentation table of content at '
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(a_ ,key=lambda a_ : s["title"].lower() )
def A ( a_=False ) -> Union[str, Any]:
with open(a_ ,encoding='utf-8' ) as f:
__UpperCamelCase : Any =yaml.safe_load(f.read() )
# Get to the API doc
__UpperCamelCase : List[str] =0
while content[api_idx]["title"] != "API":
api_idx += 1
__UpperCamelCase : Any =content[api_idx]['sections']
# Then to the model doc
__UpperCamelCase : List[str] =0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
__UpperCamelCase : str =api_doc[model_idx]['sections']
__UpperCamelCase : int =[(idx, section) for idx, section in enumerate(a_ ) if 'sections' in section]
__UpperCamelCase : Any =False
for idx, modality_doc in modalities_docs:
__UpperCamelCase : Optional[Any] =modality_doc['sections']
__UpperCamelCase : str =clean_model_doc_toc(a_ )
if old_modality_doc != new_modality_doc:
__UpperCamelCase : str =True
if overwrite:
__UpperCamelCase : List[Any] =new_modality_doc
if diff:
if overwrite:
__UpperCamelCase : Union[str, Any] =model_doc
__UpperCamelCase : int =api_doc
with open(a_ ,'w' ,encoding='utf-8' ) as f:
f.write(yaml.dump(a_ ,allow_unicode=a_ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
A_ :Dict = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A_ :Any = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 71
|
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=32 , lowerCamelCase__=2 , lowerCamelCase__=3 , lowerCamelCase__=16 , lowerCamelCase__=[32, 64, 128] , lowerCamelCase__=[1, 2, 1] , lowerCamelCase__=[2, 2, 4] , lowerCamelCase__=2 , lowerCamelCase__=2.0 , lowerCamelCase__=True , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__="gelu" , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=0.02 , lowerCamelCase__=1e-5 , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=10 , lowerCamelCase__=8 , lowerCamelCase__=["stage1", "stage2"] , lowerCamelCase__=[1, 2] , ) -> int:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = embed_dim
__lowerCamelCase = hidden_sizes
__lowerCamelCase = depths
__lowerCamelCase = num_heads
__lowerCamelCase = window_size
__lowerCamelCase = mlp_ratio
__lowerCamelCase = qkv_bias
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_absolute_embeddings
__lowerCamelCase = patch_norm
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = is_training
__lowerCamelCase = scope
__lowerCamelCase = use_labels
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = encoder_stride
__lowerCamelCase = out_features
__lowerCamelCase = out_indices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = FocalNetModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowerCamelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = FocalNetBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
__lowerCamelCase = None
__lowerCamelCase = FocalNetBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = FocalNetForMaskedImageModeling(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowerCamelCase = 1
__lowerCamelCase = FocalNetForMaskedImageModeling(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = self.type_sequence_label_size
__lowerCamelCase = FocalNetForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCamelCase = 1
__lowerCamelCase = FocalNetForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
snake_case_ = (
{'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FocalNetModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , embed_dim=37 , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase_ ( self ) -> str:
'''simple docstring'''
return
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__ )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@unittest.skip(reason='FocalNet does not use inputs_embeds' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='FocalNet does not use feedforward chunking' )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
# FocalNet has a different seq_length
__lowerCamelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowerCamelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__lowerCamelCase = outputs.reshaped_hidden_states
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = reshaped_hidden_states[0].shape
__lowerCamelCase = (
reshaped_hidden_states[0].view(lowerCamelCase__ , lowerCamelCase__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
__lowerCamelCase = True
self.check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
self.check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowerCamelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowerCamelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowerCamelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
__lowerCamelCase = True
self.check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
self.check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , (padded_height, padded_width) )
@slow
def lowercase_ ( self ) -> str:
'''simple docstring'''
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = FocalNetModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = _config_zero_init(lowerCamelCase__ )
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
# TODO update organization
return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny' ) if is_vision_available() else None
@slow
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny' ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
__lowerCamelCase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
__lowerCamelCase = torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 281 )
@require_torch
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (FocalNetBackbone,) if is_torch_available() else ()
snake_case_ = FocalNetConfig
snake_case_ = False
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = FocalNetModelTester(self )
| 90
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|
'''simple docstring'''
import string
from math import logaa
def _lowerCamelCase ( lowercase : str , lowercase : str ) -> int:
_a = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_a = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _lowerCamelCase ( lowercase : str , lowercase : str ) -> tuple[int, int]:
_a = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_a = corpus_without_punctuation.split("\n" )
_a = term.lower()
return (len([doc for doc in docs if term in doc] ), len(lowercase ))
def _lowerCamelCase ( lowercase : int , lowercase : int , lowercase : Tuple=False ) -> float:
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> float:
return round(tf * idf , 3 )
| 370
|
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamelCase ( lowercase : Any ) -> Any:
_a = filter(lambda lowercase : p.requires_grad , model.parameters() )
_a = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase_ : List[str] = logging.getLogger(__name__)
def _lowerCamelCase ( lowercase : List[str] , lowercase : Dict ) -> Union[str, Any]:
if metric == "rouge2":
_a = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
_a = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
_a = "{val_avg_em:.4f}-{step_count}"
elif metric == "loss":
_a = "{val_avg_loss:.4f}-{step_count}"
else:
raise NotImplementedError(
F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
" function." )
_a = ModelCheckpoint(
dirpath=lowercase , filename=lowercase , monitor=F'val_{metric}' , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def _lowerCamelCase ( lowercase : Dict , lowercase : Dict ) -> str:
return EarlyStopping(
monitor=F'val_{metric}' , mode="min" if "loss" in metric else "max" , patience=lowercase , verbose=lowercase , )
class __SCREAMING_SNAKE_CASE (pl.Callback ):
"""simple docstring"""
def UpperCamelCase__ ( self : Tuple , __a : Optional[int] , __a : Any ):
_a = {f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__a )
@rank_zero_only
def UpperCamelCase__ ( self : Tuple , __a : pl.Trainer , __a : pl.LightningModule , __a : str , __a : Dict=True ):
logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
_a = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
_a = Path(pl_module.hparams.output_dir )
if type_path == "test":
_a = od / "test_results.txt"
_a = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_a = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
_a = od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=__a )
generations_file.parent.mkdir(exist_ok=__a )
with open(__a , "a+" ) as writer:
for key in sorted(__a ):
if key in ["log", "progress_bar", "preds"]:
continue
_a = metrics[key]
if isinstance(__a , torch.Tensor ):
_a = val.item()
_a = f'{key}: {val:.6f}\n'
writer.write(__a )
if not save_generations:
return
if "preds" in metrics:
_a = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(__a )
@rank_zero_only
def UpperCamelCase__ ( self : Any , __a : List[Any] , __a : Dict ):
try:
_a = pl_module.model.model.num_parameters()
except AttributeError:
_a = pl_module.model.num_parameters()
_a = count_trainable_parameters(__a )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )
@rank_zero_only
def UpperCamelCase__ ( self : Union[str, Any] , __a : pl.Trainer , __a : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__a , __a , "test" )
@rank_zero_only
def UpperCamelCase__ ( self : Optional[int] , __a : pl.Trainer , __a : str ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 346
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|
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[int] , *__UpperCamelCase : List[Any] , **__UpperCamelCase : str )->int:
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = {}
def lowercase__ ( self : int , __UpperCamelCase : Any , *__UpperCamelCase : List[str] , **__UpperCamelCase : Tuple )->str:
_UpperCAmelCase = super().add_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
''' `placeholder_token` that is not already in the tokenizer.''' )
def lowercase__ ( self : List[Any] , __UpperCamelCase : Union[str, Any] , *__UpperCamelCase : Any , __UpperCamelCase : Dict=1 , **__UpperCamelCase : str )->Dict:
_UpperCAmelCase = []
if num_vec_per_token == 1:
self.try_adding_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
output.append(__UpperCamelCase )
else:
_UpperCAmelCase = []
for i in range(__UpperCamelCase ):
_UpperCAmelCase = placeholder_token + F'_{i}'
self.try_adding_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
output.append(__UpperCamelCase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
_UpperCAmelCase = output
def lowercase__ ( self : List[str] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str]=False , __UpperCamelCase : Union[str, Any]=1.0 )->List[Any]:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCAmelCase = []
for i in range(len(__UpperCamelCase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=__UpperCamelCase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
_UpperCAmelCase = self.token_map[placeholder_token]
_UpperCAmelCase = tokens[: 1 + int(len(__UpperCamelCase ) * prop_tokens_to_load )]
if vector_shuffle:
_UpperCAmelCase = copy.copy(__UpperCamelCase )
random.shuffle(__UpperCamelCase )
_UpperCAmelCase = text.replace(__UpperCamelCase , ''' '''.join(__UpperCamelCase ) )
return text
def __call__( self : List[Any] , __UpperCamelCase : List[Any] , *__UpperCamelCase : Any , __UpperCamelCase : Dict=False , __UpperCamelCase : Tuple=1.0 , **__UpperCamelCase : Tuple )->Any:
return super().__call__(
self.replace_placeholder_tokens_in_text(
__UpperCamelCase , vector_shuffle=__UpperCamelCase , prop_tokens_to_load=__UpperCamelCase ) , *__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : List[str] , __UpperCamelCase : List[Any] , *__UpperCamelCase : str , __UpperCamelCase : Tuple=False , __UpperCamelCase : Union[str, Any]=1.0 , **__UpperCamelCase : Any )->Optional[Any]:
return super().encode(
self.replace_placeholder_tokens_in_text(
__UpperCamelCase , vector_shuffle=__UpperCamelCase , prop_tokens_to_load=__UpperCamelCase ) , *__UpperCamelCase , **__UpperCamelCase , )
| 260
|
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260
| 1
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case_ = get_tests_dir('fixtures')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def a (self : Any ):
"""simple docstring"""
__snake_case = mock.Mock()
__snake_case = 500
__snake_case = {}
__snake_case = HTTPError
__snake_case = {}
# Download this model to make sure it's in the cache.
__snake_case = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=a__ ) as mock_head:
__snake_case = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def a (self : Dict ):
"""simple docstring"""
__snake_case = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def a (self : List[str] ):
"""simple docstring"""
with self.assertRaises(a__ ):
# config is in subfolder, the following should not work without specifying the subfolder
__snake_case = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
__snake_case = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(a__ )
@is_staging_test
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@classmethod
def a (cls : Optional[Any] ):
"""simple docstring"""
__snake_case = TOKEN
HfFolder.save_token(a__ )
@classmethod
def a (cls : Optional[int] ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def a (self : int ):
"""simple docstring"""
__snake_case = ViTImageProcessor.from_pretrained(a__ )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
__snake_case = ViTImageProcessor.from_pretrained(f"""{USER}/test-image-processor""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a__ , getattr(a__ , a__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
a__ , repo_id='''test-image-processor''' , push_to_hub=a__ , use_auth_token=self._token )
__snake_case = ViTImageProcessor.from_pretrained(f"""{USER}/test-image-processor""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(a__ , getattr(a__ , a__ ) )
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = ViTImageProcessor.from_pretrained(a__ )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
__snake_case = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(a__ , getattr(a__ , a__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
a__ , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=a__ , use_auth_token=self._token )
__snake_case = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(a__ , getattr(a__ , a__ ) )
def a (self : List[str] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
__snake_case = CustomImageProcessor.from_pretrained(a__ )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
__snake_case = AutoImageProcessor.from_pretrained(
f"""{USER}/test-dynamic-image-processor""" , trust_remote_code=a__ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 356
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# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
__snake_case = min(snake_case_ ) # min() finds the minimum value
__snake_case = max(snake_case_ ) # max() finds the maximum value
__snake_case = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__snake_case = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(snake_case_ , snake_case_ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__snake_case = 0
for count in range(snake_case_ ):
while holes[count] > 0:
holes[count] -= 1
__snake_case = count + min_val
i += 1
def lowerCamelCase__ ( ) -> Union[str, Any]:
__snake_case = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(snake_case_ )
print('''Sorted order is:''' , ''' '''.join(snake_case_ ) )
if __name__ == "__main__":
main()
| 238
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|
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
_snake_case = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A , __A=16 , __A=13 , __A=7 , __A=14 , __A=10 , __A=19 , __A=5 , __A=4 , __A=True , __A=16 , __A=2 , __A=4 , __A=4 , __A="gelu" , __A=0.1 , __A=0.1 , __A=[1, 2, 3, 4, 5] , __A=25 , __A=5 , ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = d_model
lowerCamelCase : Any = parent
lowerCamelCase : List[Any] = batch_size
lowerCamelCase : Union[str, Any] = prediction_length
lowerCamelCase : str = context_length
lowerCamelCase : List[Any] = cardinality
lowerCamelCase : Dict = num_time_features
lowerCamelCase : Any = lags_sequence
lowerCamelCase : Any = embedding_dimension
lowerCamelCase : Union[str, Any] = is_training
lowerCamelCase : List[str] = hidden_size
lowerCamelCase : int = num_hidden_layers
lowerCamelCase : List[str] = num_attention_heads
lowerCamelCase : Any = intermediate_size
lowerCamelCase : List[Any] = hidden_act
lowerCamelCase : List[Any] = hidden_dropout_prob
lowerCamelCase : Optional[int] = attention_probs_dropout_prob
lowerCamelCase : Any = context_length
lowerCamelCase : str = prediction_length + label_length
lowerCamelCase : Any = label_length
lowerCamelCase : Union[str, Any] = moving_average
lowerCamelCase : Dict = autocorrelation_factor
def _snake_case ( self ):
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Any = config.context_length + max(config.lags_sequence )
lowerCamelCase : Any = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
lowerCamelCase : int = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCamelCase : List[Any] = floats_tensor([self.batch_size, _past_length] )
lowerCamelCase : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCamelCase : Optional[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCamelCase : List[str] = floats_tensor([self.batch_size, config.prediction_length] )
lowerCamelCase : Dict = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.get_config()
lowerCamelCase : Any = self.prepare_autoformer_inputs_dict(lowerCamelCase_ )
return config, inputs_dict
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _snake_case ( self , __A , __A ):
"""simple docstring"""
lowerCamelCase : str = AutoformerModel(config=lowerCamelCase_ ).to(lowerCamelCase_ ).eval()
lowerCamelCase : List[str] = model(**lowerCamelCase_ )
lowerCamelCase : List[Any] = outputs.encoder_last_hidden_state
lowerCamelCase : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCamelCase_ )
lowerCamelCase : Any = AutoformerEncoder.from_pretrained(lowerCamelCase_ ).to(lowerCamelCase_ )
lowerCamelCase : Tuple = model.create_network_inputs(**lowerCamelCase_ )
lowerCamelCase : Tuple = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCamelCase : Dict = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
lowerCamelCase : Tuple = encoder(inputs_embeds=lowerCamelCase_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
lowerCamelCase : str = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
lowerCamelCase : int = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
lowerCamelCase : List[str] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
lowerCamelCase : List[str] = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase : Optional[Any] = model.get_decoder()
decoder.save_pretrained(lowerCamelCase_ )
lowerCamelCase : List[str] = AutoformerDecoder.from_pretrained(lowerCamelCase_ ).to(lowerCamelCase_ )
lowerCamelCase : List[Any] = decoder(
trend=lowerCamelCase_ , inputs_embeds=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class UpperCAmelCase_ ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
__A : Union[str, Any] = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__A : List[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__A : List[Any] = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
__A : Union[str, Any] = False
__A : int = False
__A : Optional[Any] = False
__A : Tuple = False
__A : Optional[int] = False
__A : Any = False
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = AutoformerModelTester(self )
lowerCamelCase : int = ConfigTester(self , config_class=lowerCamelCase_ , has_text_modality=lowerCamelCase_ )
def _snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase : Dict = model_class(lowerCamelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCamelCase_ )
lowerCamelCase : Tuple = model_class.from_pretrained(lowerCamelCase_ , output_loading_info=lowerCamelCase_ )
self.assertEqual(info["missing_keys"] , [] )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase_ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _snake_case ( self ):
"""simple docstring"""
pass
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = inspect.signature(getattr(lowerCamelCase_ , "forward" ) )
# The main input is the name of the argument after `self`
lowerCamelCase : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCamelCase_ )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase : List[str] = model_class(lowerCamelCase_ )
lowerCamelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase : Optional[int] = [*signature.parameters.keys()]
lowerCamelCase : Tuple = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(lowerCamelCase_ )] , lowerCamelCase_ )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase : Optional[int] = True
lowerCamelCase : Union[str, Any] = getattr(self.model_tester , "seq_length" , lowerCamelCase_ )
lowerCamelCase : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCamelCase_ )
lowerCamelCase : Dict = getattr(self.model_tester , "encoder_seq_length" , lowerCamelCase_ )
lowerCamelCase : Tuple = getattr(self.model_tester , "d_model" , lowerCamelCase_ )
lowerCamelCase : Optional[int] = getattr(self.model_tester , "num_attention_heads" , lowerCamelCase_ )
lowerCamelCase : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCamelCase : Optional[int] = True
lowerCamelCase : str = False
lowerCamelCase : str = True
lowerCamelCase : str = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
lowerCamelCase : Union[str, Any] = model(**self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ ) )
lowerCamelCase : Dict = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase : Tuple = True
lowerCamelCase : Dict = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
lowerCamelCase : Optional[int] = model(**self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ ) )
lowerCamelCase : Union[str, Any] = outputs.encoder_attentions
self.assertEqual(len(lowerCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
lowerCamelCase : Any = len(lowerCamelCase_ )
lowerCamelCase : Dict = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
# decoder attentions
lowerCamelCase : List[Any] = outputs.decoder_attentions
self.assertIsInstance(lowerCamelCase_ , (list, tuple) )
self.assertEqual(len(lowerCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
lowerCamelCase : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCamelCase_ , (list, tuple) )
self.assertEqual(len(lowerCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
lowerCamelCase : Tuple = True
lowerCamelCase : Tuple = True
lowerCamelCase : List[Any] = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
lowerCamelCase : Dict = model(**self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(out_len + 2 , len(lowerCamelCase_ ) )
lowerCamelCase : Any = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def _snake_case ( self ):
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowercase_( SCREAMING_SNAKE_CASE_="train-batch.pt" ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=_a , repo_type="dataset" )
lowerCamelCase : Any = torch.load(_a , map_location=_a )
return batch
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCamelCase_ )
lowerCamelCase : Optional[int] = prepare_batch()
with torch.no_grad():
lowerCamelCase : str = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
lowerCamelCase : Optional[Any] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCamelCase_ )
lowerCamelCase : Dict = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=lowerCamelCase_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCamelCase_ , atol=lowerCamelCase_ ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCamelCase_ )
lowerCamelCase : List[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase : List[Any] = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
lowerCamelCase : Optional[int] = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCamelCase_ )
lowerCamelCase : Tuple = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=lowerCamelCase_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCamelCase_ , atol=lowerCamelCase_ ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCamelCase_ )
lowerCamelCase : Any = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase : List[str] = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
lowerCamelCase : Optional[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCamelCase_ )
lowerCamelCase : List[Any] = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=lowerCamelCase_ )
lowerCamelCase : Union[str, Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCamelCase_ , rtol=1e-1 ) )
| 283
|
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class _snake_case ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
A__ : Dict = AutoencoderKL
A__ : Optional[int] = "sample"
A__ : Tuple = 1E-2
@property
def A__ ( self: List[Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = 4
UpperCAmelCase_ : str = 3
UpperCAmelCase_ : Any = (32, 32)
UpperCAmelCase_ : Optional[int] = floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase_ )
return {"sample": image}
@property
def A__ ( self: List[str] ) -> Tuple:
return (3, 32, 32)
@property
def A__ ( self: Optional[Any] ) -> Any:
return (3, 32, 32)
def A__ ( self: Any ) -> Tuple:
UpperCAmelCase_ : List[Any] = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
UpperCAmelCase_ : int = self.dummy_input
return init_dict, inputs_dict
def A__ ( self: Optional[Any] ) -> int:
pass
def A__ ( self: str ) -> Any:
pass
@unittest.skipIf(torch_device == """mps""" ,"""Gradient checkpointing skipped on MPS""" )
def A__ ( self: Union[str, Any] ) -> Dict:
# enable deterministic behavior for gradient checkpointing
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = self.prepare_init_args_and_inputs_for_common()
UpperCAmelCase_ : List[Any] = self.model_class(**lowerCamelCase_ )
model.to(lowerCamelCase_ )
assert not model.is_gradient_checkpointing and model.training
UpperCAmelCase_ : Optional[Any] = model(**lowerCamelCase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
UpperCAmelCase_ : Any = torch.randn_like(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
UpperCAmelCase_ : str = self.model_class(**lowerCamelCase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowerCamelCase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
UpperCAmelCase_ : Optional[int] = model_a(**lowerCamelCase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
UpperCAmelCase_ : Dict = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
UpperCAmelCase_ : Dict = dict(model.named_parameters() )
UpperCAmelCase_ : Union[str, Any] = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data ,named_params_a[name].grad.data ,atol=5e-5 ) )
def A__ ( self: Optional[Any] ) -> str:
UpperCAmelCase_ , UpperCAmelCase_ : int = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy""" ,output_loading_info=lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
self.assertEqual(len(loading_info["""missing_keys"""] ) ,0 )
model.to(lowerCamelCase_ )
UpperCAmelCase_ : Dict = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A__ ( self: Optional[int] ) -> int:
UpperCAmelCase_ : Dict = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy""" )
UpperCAmelCase_ : Tuple = model.to(lowerCamelCase_ )
model.eval()
if torch_device == "mps":
UpperCAmelCase_ : Tuple = torch.manual_seed(0 )
else:
UpperCAmelCase_ : Optional[int] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
UpperCAmelCase_ : str = torch.randn(
1 ,model.config.in_channels ,model.config.sample_size ,model.config.sample_size ,generator=torch.manual_seed(0 ) ,)
UpperCAmelCase_ : int = image.to(lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : Dict = model(lowerCamelCase_ ,sample_posterior=lowerCamelCase_ ,generator=lowerCamelCase_ ).sample
UpperCAmelCase_ : Optional[int] = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
UpperCAmelCase_ : Tuple = torch.tensor(
[
-4.0078e-01,
-3.8323e-04,
-1.2681e-01,
-1.1462e-01,
2.0095e-01,
1.0893e-01,
-8.8247e-02,
-3.0361e-01,
-9.8644e-03,
] )
elif torch_device == "cpu":
UpperCAmelCase_ : List[str] = torch.tensor(
[-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6] )
else:
UpperCAmelCase_ : List[str] = torch.tensor(
[-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5] )
self.assertTrue(torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,rtol=1e-2 ) )
@slow
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self: Any ,lowerCamelCase_: List[Any] ,lowerCamelCase_: Any ) -> Optional[Any]:
return F'''gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase_ ) for s in shape] )}.npy'''
def A__ ( self: Union[str, Any] ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self: List[str] ,lowerCamelCase_: Optional[int]=0 ,lowerCamelCase_: List[Any]=(4, 3, 512, 512) ,lowerCamelCase_: Optional[Any]=False ) -> Optional[int]:
UpperCAmelCase_ : Tuple = torch.floataa if fpaa else torch.floataa
UpperCAmelCase_ : Tuple = torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_ ,lowerCamelCase_ ) ) ).to(lowerCamelCase_ ).to(lowerCamelCase_ )
return image
def A__ ( self: List[Any] ,lowerCamelCase_: List[str]="CompVis/stable-diffusion-v1-4" ,lowerCamelCase_: Union[str, Any]=False ) -> Any:
UpperCAmelCase_ : Optional[Any] = """fp16""" if fpaa else None
UpperCAmelCase_ : str = torch.floataa if fpaa else torch.floataa
UpperCAmelCase_ : int = AutoencoderKL.from_pretrained(
lowerCamelCase_ ,subfolder="""vae""" ,torch_dtype=lowerCamelCase_ ,revision=lowerCamelCase_ ,)
model.to(lowerCamelCase_ ).eval()
return model
def A__ ( self: Dict ,lowerCamelCase_: Union[str, Any]=0 ) -> Optional[int]:
if torch_device == "mps":
return torch.manual_seed(lowerCamelCase_ )
return torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
[47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
# fmt: on
] )
def A__ ( self: List[Any] ,lowerCamelCase_: Optional[Any] ,lowerCamelCase_: str ,lowerCamelCase_: Dict ) -> Tuple:
UpperCAmelCase_ : List[Any] = self.get_sd_vae_model()
UpperCAmelCase_ : int = self.get_sd_image(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = self.get_generator(lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : Union[str, Any] = model(lowerCamelCase_ ,generator=lowerCamelCase_ ,sample_posterior=lowerCamelCase_ ).sample
assert sample.shape == image.shape
UpperCAmelCase_ : Optional[Any] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
UpperCAmelCase_ : Tuple = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice )
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
[47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
# fmt: on
] )
@require_torch_gpu
def A__ ( self: Union[str, Any] ,lowerCamelCase_: Any ,lowerCamelCase_: List[str] ) -> Tuple:
UpperCAmelCase_ : List[str] = self.get_sd_vae_model(fpaa=lowerCamelCase_ )
UpperCAmelCase_ : Any = self.get_sd_image(lowerCamelCase_ ,fpaa=lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = self.get_generator(lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : Union[str, Any] = model(lowerCamelCase_ ,generator=lowerCamelCase_ ,sample_posterior=lowerCamelCase_ ).sample
assert sample.shape == image.shape
UpperCAmelCase_ : Tuple = sample[-1, -2:, :2, -2:].flatten().float().cpu()
UpperCAmelCase_ : Optional[int] = torch.tensor(lowerCamelCase_ )
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
[47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
# fmt: on
] )
def A__ ( self: Tuple ,lowerCamelCase_: List[Any] ,lowerCamelCase_: Optional[int] ,lowerCamelCase_: List[str] ) -> Dict:
UpperCAmelCase_ : Optional[int] = self.get_sd_vae_model()
UpperCAmelCase_ : Dict = self.get_sd_image(lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : str = model(lowerCamelCase_ ).sample
assert sample.shape == image.shape
UpperCAmelCase_ : List[Any] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
UpperCAmelCase_ : Any = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice )
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
[37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
# fmt: on
] )
@require_torch_gpu
def A__ ( self: Optional[Any] ,lowerCamelCase_: Tuple ,lowerCamelCase_: str ) -> Optional[Any]:
UpperCAmelCase_ : List[str] = self.get_sd_vae_model()
UpperCAmelCase_ : Optional[int] = self.get_sd_image(lowerCamelCase_ ,shape=(3, 4, 64, 64) )
with torch.no_grad():
UpperCAmelCase_ : str = model.decode(lowerCamelCase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
UpperCAmelCase_ : Any = sample[-1, -2:, :2, -2:].flatten().cpu()
UpperCAmelCase_ : Union[str, Any] = torch.tensor(lowerCamelCase_ )
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
[16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
# fmt: on
] )
@require_torch_gpu
def A__ ( self: str ,lowerCamelCase_: List[Any] ,lowerCamelCase_: Any ) -> Optional[Any]:
UpperCAmelCase_ : Dict = self.get_sd_vae_model(fpaa=lowerCamelCase_ )
UpperCAmelCase_ : List[Any] = self.get_sd_image(lowerCamelCase_ ,shape=(3, 4, 64, 64) ,fpaa=lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : List[str] = model.decode(lowerCamelCase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
UpperCAmelCase_ : str = sample[-1, -2:, :2, -2:].flatten().float().cpu()
UpperCAmelCase_ : str = torch.tensor(lowerCamelCase_ )
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() ,reason="""xformers is not required when using PyTorch 2.0.""" )
def A__ ( self: List[Any] ,lowerCamelCase_: Union[str, Any] ) -> int:
UpperCAmelCase_ : Optional[Any] = self.get_sd_vae_model(fpaa=lowerCamelCase_ )
UpperCAmelCase_ : List[str] = self.get_sd_image(lowerCamelCase_ ,shape=(3, 4, 64, 64) ,fpaa=lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : Optional[Any] = model.decode(lowerCamelCase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
UpperCAmelCase_ : List[str] = model.decode(lowerCamelCase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() ,reason="""xformers is not required when using PyTorch 2.0.""" )
def A__ ( self: Optional[Any] ,lowerCamelCase_: Dict ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = self.get_sd_vae_model()
UpperCAmelCase_ : Any = self.get_sd_image(lowerCamelCase_ ,shape=(3, 4, 64, 64) )
with torch.no_grad():
UpperCAmelCase_ : Union[str, Any] = model.decode(lowerCamelCase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
UpperCAmelCase_ : Optional[Any] = model.decode(lowerCamelCase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
[47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
# fmt: on
] )
def A__ ( self: Union[str, Any] ,lowerCamelCase_: Any ,lowerCamelCase_: Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Dict = self.get_sd_vae_model()
UpperCAmelCase_ : Optional[Any] = self.get_sd_image(lowerCamelCase_ )
UpperCAmelCase_ : str = self.get_generator(lowerCamelCase_ )
with torch.no_grad():
UpperCAmelCase_ : int = model.encode(lowerCamelCase_ ).latent_dist
UpperCAmelCase_ : Optional[Any] = dist.sample(generator=lowerCamelCase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
UpperCAmelCase_ : Tuple = sample[0, -1, -3:, -3:].flatten().cpu()
UpperCAmelCase_ : Optional[Any] = torch.tensor(lowerCamelCase_ )
UpperCAmelCase_ : List[Any] = 3e-3 if torch_device != """mps""" else 1e-2
assert torch_all_close(lowerCamelCase_ ,lowerCamelCase_ ,atol=lowerCamelCase_ )
| 345
| 0
|
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCAmelCase :Tuple = pd.read_csv('''sample_data.csv''', header=None)
lowerCAmelCase :List[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
lowerCAmelCase :List[str] = df.iloc[:, 1:2]
lowerCAmelCase :Optional[int] = actual_data.values.reshape(len_data, 1)
lowerCAmelCase :Any = MinMaxScaler().fit_transform(actual_data)
lowerCAmelCase :Any = 1_0
lowerCAmelCase :List[Any] = 5
lowerCAmelCase :str = 2_0
lowerCAmelCase :List[str] = len_data - periods * look_back
lowerCAmelCase :List[str] = actual_data[:division]
lowerCAmelCase :int = actual_data[division - look_back :]
lowerCAmelCase :Optional[int] = [], []
lowerCAmelCase :Union[str, Any] = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
lowerCAmelCase :Union[str, Any] = np.array(train_x)
lowerCAmelCase :Tuple = np.array(test_x)
lowerCAmelCase :Optional[int] = np.array([list(i.ravel()) for i in train_y])
lowerCAmelCase :Dict = np.array([list(i.ravel()) for i in test_y])
lowerCAmelCase :Dict = Sequential()
model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
lowerCAmelCase :Optional[Any] = model.fit(
x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
)
lowerCAmelCase :List[Any] = model.predict(x_test)
| 359
|
'''simple docstring'''
import math
def lowerCamelCase ( lowerCAmelCase : float , lowerCAmelCase : float ):
"""simple docstring"""
return math.pow(lowerCAmelCase , 2 ) - a
def lowerCamelCase ( lowerCAmelCase : float ):
"""simple docstring"""
return 2 * x
def lowerCamelCase ( lowerCAmelCase : float ):
"""simple docstring"""
__magic_name__ : List[Any] = 2.0
while start <= a:
__magic_name__ : List[str] = math.pow(lowerCAmelCase , 2 )
return start
def lowerCamelCase ( lowerCAmelCase : float , lowerCAmelCase : int = 9999 , lowerCAmelCase : float = 0.00_0000_0000_0001 ):
"""simple docstring"""
if a < 0:
raise ValueError('math domain error' )
__magic_name__ : Any = get_initial_point(lowerCAmelCase )
for _ in range(lowerCAmelCase ):
__magic_name__ : List[str] = value
__magic_name__ : Optional[int] = value - fx(lowerCAmelCase , lowerCAmelCase ) / fx_derivative(lowerCAmelCase )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 275
| 0
|
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_snake_case = "."
if __name__ == "__main__":
_snake_case = os.path.join(REPO_PATH, "utils/documentation_tests.txt")
_snake_case = []
_snake_case = []
with open(doctest_file_path) as fp:
for line in fp:
_snake_case = line.strip()
_snake_case = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
_snake_case = "\n".join(non_existent_paths)
raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
if all_paths != sorted(all_paths):
raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")
| 36
|
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_A : Optional[Any] = 16
_A : Union[str, Any] = 32
def UpperCamelCase_ ( snake_case_ : List[str] ) -> str:
'''simple docstring'''
return int(x / 2**20 )
class _lowercase :
'''simple docstring'''
def __enter__( self : List[Any] ) -> int:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
__lowerCAmelCase = torch.cuda.memory_allocated()
return self
def __exit__( self : Tuple , *SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[Any]:
gc.collect()
torch.cuda.empty_cache()
__lowerCAmelCase = torch.cuda.memory_allocated()
__lowerCAmelCase = torch.cuda.max_memory_allocated()
__lowerCAmelCase = bamb(self.end - self.begin )
__lowerCAmelCase = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def UpperCamelCase_ ( snake_case_ : Accelerator , snake_case_ : int = 16 , snake_case_ : str = "bert-base-cased" , snake_case_ : int = 3_20 , snake_case_ : int = 1_60 , ) -> Optional[int]:
'''simple docstring'''
__lowerCAmelCase = AutoTokenizer.from_pretrained(snake_case_ )
__lowerCAmelCase = load_dataset(
"""glue""" , """mrpc""" , split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""} )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case_ , max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowerCAmelCase = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=snake_case_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(snake_case_ : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case_ , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(snake_case_ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
__lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
def UpperCamelCase_ ( snake_case_ : List[Any] , snake_case_ : Tuple ) -> Optional[int]:
'''simple docstring'''
__lowerCAmelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase = config["""lr"""]
__lowerCAmelCase = int(config["""num_epochs"""] )
__lowerCAmelCase = int(config["""seed"""] )
__lowerCAmelCase = int(config["""batch_size"""] )
__lowerCAmelCase = args.model_name_or_path
set_seed(snake_case_ )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(snake_case_ , snake_case_ , snake_case_ , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(snake_case_ , return_dict=snake_case_ )
# Instantiate optimizer
__lowerCAmelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowerCAmelCase = optimizer_cls(params=model.parameters() , lr=snake_case_ )
if accelerator.state.deepspeed_plugin is not None:
__lowerCAmelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
__lowerCAmelCase = 1
__lowerCAmelCase = (len(snake_case_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=0 , num_training_steps=snake_case_ , )
else:
__lowerCAmelCase = DummyScheduler(snake_case_ , total_num_steps=snake_case_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# We need to keep track of how many total steps we have iterated over
__lowerCAmelCase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowerCAmelCase = 0
# Now we train the model
__lowerCAmelCase = {}
for epoch in range(snake_case_ , snake_case_ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(snake_case_ ):
__lowerCAmelCase = model(**snake_case_ )
__lowerCAmelCase = outputs.loss
__lowerCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(snake_case_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
__lowerCAmelCase = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """peak_memory_utilization.json""" ) , """w""" ) as f:
json.dump(snake_case_ , snake_case_ )
def UpperCamelCase_ ( ) -> Any:
'''simple docstring'''
__lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=snake_case_ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=snake_case_ , )
parser.add_argument(
"""--output_dir""" , type=snake_case_ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--peak_memory_upper_bound""" , type=snake_case_ , default=snake_case_ , help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" , )
parser.add_argument(
"""--n_train""" , type=snake_case_ , default=3_20 , help="""Number of training examples to use.""" , )
parser.add_argument(
"""--n_val""" , type=snake_case_ , default=1_60 , help="""Number of validation examples to use.""" , )
parser.add_argument(
"""--num_epochs""" , type=snake_case_ , default=1 , help="""Number of train epochs.""" , )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 229
| 0
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCamelCase__ = (3, 9, -11, 0, 7, 5, 1, -1)
lowerCamelCase__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
lowercase = 42
lowercase = 42
class A__ :
def __init__( self : Dict , a : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : int = None
for i in sorted(__lowercase , reverse=__lowercase ):
lowerCAmelCase__ : List[Any] = Node(__lowercase , self.head )
def __iter__( self : str ):
'''simple docstring'''
lowerCAmelCase__ : Dict = self.head
while node:
yield node.data
lowerCAmelCase__ : Any = node.next_node
def __len__( self : int ):
'''simple docstring'''
return sum(1 for _ in self )
def __str__( self : Optional[Any] ):
'''simple docstring'''
return " -> ".join([str(__lowercase ) for node in self] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
return SortedLinkedList(list(SCREAMING_SNAKE_CASE_ ) + list(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 359
|
import os
import string
import sys
lowerCamelCase__ = 1 << 8
lowerCamelCase__ = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
lowerCamelCase__ = KEYMAP["""up"""]
lowerCamelCase__ = KEYMAP["""left"""]
if sys.platform == "win32":
lowerCamelCase__ = []
lowerCamelCase__ = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
lowerCamelCase__ = ord(str(i))
def lowerCAmelCase__ ( ) -> Dict:
if os.name == "nt":
import msvcrt
lowerCAmelCase__ : Dict = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(SCREAMING_SNAKE_CASE_ ) == 0:
# Read the keystroke
lowerCAmelCase__ : Optional[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCAmelCase__ : Dict = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCAmelCase__ : Dict = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_ )
if ord(SCREAMING_SNAKE_CASE_ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
lowerCAmelCase__ : Dict = chr(KEYMAP['esc'] )
except KeyError:
lowerCAmelCase__ : Dict = cha[1]
else:
lowerCAmelCase__ : List[Any] = ch.decode(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Tuple = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCAmelCase__ : Tuple = sys.stdin.fileno()
lowerCAmelCase__ : Any = termios.tcgetattr(SCREAMING_SNAKE_CASE_ )
try:
tty.setraw(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = sys.stdin.read(1 )
finally:
termios.tcsetattr(SCREAMING_SNAKE_CASE_ , termios.TCSADRAIN , SCREAMING_SNAKE_CASE_ )
return ch
def lowerCAmelCase__ ( ) -> Union[str, Any]:
lowerCAmelCase__ : Any = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["esc"]:
lowerCAmelCase__ : Union[str, Any] = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["mod_int"]:
lowerCAmelCase__ : str = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(SCREAMING_SNAKE_CASE_ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(SCREAMING_SNAKE_CASE_ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 307
| 0
|
"""simple docstring"""
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
snake_case = (boundary[1] - boundary[0]) / steps
snake_case = boundary[0]
snake_case = boundary[1]
snake_case = make_points(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
snake_case = 0.0
y += (h / 2.0) * f(_lowerCAmelCase )
for i in x_i:
# print(i)
y += h * f(_lowerCAmelCase )
y += (h / 2.0) * f(_lowerCAmelCase )
return y
def lowerCAmelCase__ ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : List[str] ) -> Tuple:
"""simple docstring"""
snake_case = a + h
while x < (b - h):
yield x
snake_case = x + h
def lowerCAmelCase__ ( _UpperCamelCase : Dict ) -> Optional[Any]: # enter your function here
"""simple docstring"""
snake_case = (x - 0) * (x - 0)
return y
def lowerCAmelCase__ ( ) -> Dict:
"""simple docstring"""
snake_case = 0.0 # Lower bound of integration
snake_case = 1.0 # Upper bound of integration
snake_case = 10.0 # define number of steps or resolution
snake_case = [a, b] # define boundary of integration
snake_case = method_a(_lowerCAmelCase , _lowerCAmelCase )
print(f"""y = {y}""" )
if __name__ == "__main__":
main()
| 150
|
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
UpperCamelCase : List[Any] = False
for divisor in range(2 , int(round(sqrt(_lowerCAmelCase ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
UpperCamelCase : Union[str, Any] = False
break
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'status' must been from type bool"
return status
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
UpperCamelCase : int = list(range(2 , n + 1 ) )
UpperCamelCase : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_lowerCAmelCase ) ):
for j in range(i + 1 , len(_lowerCAmelCase ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
UpperCamelCase : Tuple = 0
# filters actual prime numbers.
UpperCamelCase : str = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Optional[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
UpperCamelCase : str = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(_lowerCAmelCase ):
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and number >= 0, "'number' must been an int and >= 0"
UpperCamelCase : Optional[Any] = [] # this list will be returns of the function.
# potential prime number factors.
UpperCamelCase : Tuple = 2
UpperCamelCase : str = number
if number == 0 or number == 1:
ans.append(_lowerCAmelCase )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_lowerCAmelCase ):
while quotient != 1:
if is_prime(_lowerCAmelCase ) and (quotient % factor == 0):
ans.append(_lowerCAmelCase )
quotient /= factor
else:
factor += 1
else:
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
UpperCamelCase : List[Any] = 0
# prime factorization of 'number'
UpperCamelCase : Any = prime_factorization(_lowerCAmelCase )
UpperCamelCase : List[Any] = max(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase ) -> Union[str, Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
UpperCamelCase : List[Any] = 0
# prime factorization of 'number'
UpperCamelCase : Dict = prime_factorization(_lowerCAmelCase )
UpperCamelCase : List[Any] = min(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase ) -> Optional[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 == 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 == 0
def A_ ( _lowerCAmelCase ) -> List[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 != 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 != 0
def A_ ( _lowerCAmelCase ) -> Any:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (number > 2) and is_even(_lowerCAmelCase )
), "'number' must been an int, even and > 2"
UpperCamelCase : List[str] = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
UpperCamelCase : Dict = get_prime_numbers(_lowerCAmelCase )
UpperCamelCase : Tuple = len(_lowerCAmelCase )
# run variable for while-loops.
UpperCamelCase : Optional[int] = 0
UpperCamelCase : int = None
# exit variable. for break up the loops
UpperCamelCase : Union[str, Any] = True
while i < len_pn and loop:
UpperCamelCase : Tuple = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
UpperCamelCase : Any = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (len(_lowerCAmelCase ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
UpperCamelCase : Tuple = 0
while numbera != 0:
UpperCamelCase : Tuple = numbera % numbera
UpperCamelCase : Any = numbera
UpperCamelCase : Union[str, Any] = rest
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
UpperCamelCase : Optional[int] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
UpperCamelCase : List[Any] = prime_factorization(_lowerCAmelCase )
UpperCamelCase : Union[str, Any] = prime_factorization(_lowerCAmelCase )
elif numbera == 1 or numbera == 1:
UpperCamelCase : Optional[Any] = []
UpperCamelCase : int = []
UpperCamelCase : List[Any] = max(_lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase : Optional[int] = 0
UpperCamelCase : Tuple = 0
UpperCamelCase : List[str] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
UpperCamelCase : str = prime_fac_a.count(_lowerCAmelCase )
UpperCamelCase : Tuple = prime_fac_a.count(_lowerCAmelCase )
for _ in range(max(_lowerCAmelCase , _lowerCAmelCase ) ):
ans *= n
else:
UpperCamelCase : str = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
UpperCamelCase : Any = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def A_ ( _lowerCAmelCase ) -> Tuple:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'number' must been a positive int"
UpperCamelCase : int = 0
UpperCamelCase : int = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_lowerCAmelCase ):
ans += 1
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and is_prime(
_lowerCAmelCase ), "'ans' must been a prime number and from type int"
return ans
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
assert (
is_prime(_lowerCAmelCase ) and is_prime(_lowerCAmelCase ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
UpperCamelCase : str = p_number_a + 1 # jump to the next number
UpperCamelCase : Dict = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
while number < p_number_a:
ans.append(_lowerCAmelCase )
number += 1
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and ans[0] != p_number_a
and ans[len(_lowerCAmelCase ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def A_ ( _lowerCAmelCase ) -> List[str]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 1), "'n' must been int and >= 1"
UpperCamelCase : Dict = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(_lowerCAmelCase )
# precondition
assert ans[0] == 1 and ans[len(_lowerCAmelCase ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def A_ ( _lowerCAmelCase ) -> int:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number > 1
), "'number' must been an int and >= 1"
UpperCamelCase : int = get_divisors(_lowerCAmelCase )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (divisors[0] == 1)
and (divisors[len(_lowerCAmelCase ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
UpperCamelCase : List[str] = gcd(abs(_lowerCAmelCase ) , abs(_lowerCAmelCase ) )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def A_ ( _lowerCAmelCase ) -> Dict:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been a int and >= 0"
UpperCamelCase : str = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def A_ ( _lowerCAmelCase ) -> Tuple:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been an int and >= 0"
UpperCamelCase : Dict = 0
UpperCamelCase : Dict = 1
UpperCamelCase : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
UpperCamelCase : Any = ans
ans += fiba
UpperCamelCase : str = tmp
return ans
| 52
| 0
|
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase = 50 ):
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 37
|
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
lowerCAmelCase__ : str = logging.get_logger(__name__)
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = UNetaDModel
SCREAMING_SNAKE_CASE = '''sample'''
@property
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
__UpperCAmelCase : Dict = 4
__UpperCAmelCase : Dict = 3
__UpperCAmelCase : Dict = (32, 32)
__UpperCAmelCase : Any = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase_ )
__UpperCAmelCase : str = torch.tensor([10] ).to(UpperCAmelCase_ )
return {"sample": noise, "timestep": time_step}
@property
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
return (3, 32, 32)
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return (3, 32, 32)
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = {
"block_out_channels": (32, 64),
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
"attention_head_dim": 3,
"out_channels": 3,
"in_channels": 3,
"layers_per_block": 2,
"sample_size": 32,
}
__UpperCAmelCase : List[str] = self.dummy_input
return init_dict, inputs_dict
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = UNetaDModel
SCREAMING_SNAKE_CASE = '''sample'''
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : str = 4
__UpperCAmelCase : Dict = 4
__UpperCAmelCase : Optional[int] = (32, 32)
__UpperCAmelCase : Optional[int] = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = torch.tensor([10] ).to(UpperCAmelCase_ )
return {"sample": noise, "timestep": time_step}
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return (4, 32, 32)
@property
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
return (4, 32, 32)
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Dict = {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
"layers_per_block": 2,
"block_out_channels": (32, 64),
"attention_head_dim": 32,
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
__UpperCAmelCase : List[Any] = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : str = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(UpperCAmelCase_ )
__UpperCAmelCase : List[str] = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : List[str] = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
__UpperCAmelCase , __UpperCAmelCase : str = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase_ )
model_accelerate.to(UpperCAmelCase_ )
model_accelerate.eval()
__UpperCAmelCase : Optional[Any] = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
__UpperCAmelCase : int = noise.to(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = torch.tensor([10] * noise.shape[0] ).to(UpperCAmelCase_ )
__UpperCAmelCase : Optional[Any] = model_accelerate(UpperCAmelCase_ , UpperCAmelCase_ )["sample"]
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
__UpperCAmelCase , __UpperCAmelCase : str = UNetaDModel.from_pretrained(
"fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase_ , low_cpu_mem_usage=UpperCAmelCase_ )
model_normal_load.to(UpperCAmelCase_ )
model_normal_load.eval()
__UpperCAmelCase : Optional[Any] = model_normal_load(UpperCAmelCase_ , UpperCAmelCase_ )["sample"]
assert torch_all_close(UpperCAmelCase_ , UpperCAmelCase_ , rtol=1e-3 )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
__UpperCAmelCase : Tuple = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
model.eval()
model.to(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
__UpperCAmelCase : Optional[int] = noise.to(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = torch.tensor([10] * noise.shape[0] ).to(UpperCAmelCase_ )
with torch.no_grad():
__UpperCAmelCase : Dict = model(UpperCAmelCase_ , UpperCAmelCase_ ).sample
__UpperCAmelCase : Optional[int] = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
__UpperCAmelCase : int = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase_ , UpperCAmelCase_ , rtol=1e-3 ) )
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = UNetaDModel
SCREAMING_SNAKE_CASE = '''sample'''
@property
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase_ : List[str]=(32, 32) ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = 4
__UpperCAmelCase : Tuple = 3
__UpperCAmelCase : List[Any] = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=UpperCAmelCase_ )
return {"sample": noise, "timestep": time_step}
@property
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
return (3, 32, 32)
@property
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
return (3, 32, 32)
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
"out_channels": 3,
"time_embedding_type": "fourier",
"norm_eps": 1e-6,
"mid_block_scale_factor": math.sqrt(2.0 ),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
}
__UpperCAmelCase : int = self.dummy_input
return init_dict, inputs_dict
@slow
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : int = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(UpperCAmelCase_ )
__UpperCAmelCase : Any = self.dummy_input
__UpperCAmelCase : int = floats_tensor((4, 3) + (256, 256) ).to(UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = noise
__UpperCAmelCase : Optional[Any] = model(**UpperCAmelCase_ )
assert image is not None, "Make sure output is not None"
@slow
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
__UpperCAmelCase : Any = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
model.to(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = 4
__UpperCAmelCase : Optional[int] = 3
__UpperCAmelCase : int = (256, 256)
__UpperCAmelCase : Optional[Any] = torch.ones((batch_size, num_channels) + sizes ).to(UpperCAmelCase_ )
__UpperCAmelCase : Dict = torch.tensor(batch_size * [1e-4] ).to(UpperCAmelCase_ )
with torch.no_grad():
__UpperCAmelCase : Tuple = model(UpperCAmelCase_ , UpperCAmelCase_ ).sample
__UpperCAmelCase : List[str] = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
__UpperCAmelCase : Tuple = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase_ , UpperCAmelCase_ , rtol=1e-2 ) )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
__UpperCAmelCase : Dict = UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
model.to(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Union[str, Any] = 3
__UpperCAmelCase : Union[str, Any] = (32, 32)
__UpperCAmelCase : Optional[Any] = torch.ones((batch_size, num_channels) + sizes ).to(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = torch.tensor(batch_size * [1e-4] ).to(UpperCAmelCase_ )
with torch.no_grad():
__UpperCAmelCase : str = model(UpperCAmelCase_ , UpperCAmelCase_ ).sample
__UpperCAmelCase : Dict = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
__UpperCAmelCase : Any = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase_ , UpperCAmelCase_ , rtol=1e-2 ) )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
# not required for this model
pass
| 37
| 1
|
'''simple docstring'''
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple , _lowerCAmelCase : Any=None):
'''simple docstring'''
__lowercase =data
__lowercase =None
def __repr__( self : Tuple):
'''simple docstring'''
__lowercase =[]
__lowercase =self
while temp:
string_rep.append(f"""{temp.data}""")
__lowercase =temp.next
return "->".join(_lowerCAmelCase)
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if not elements_list:
raise Exception('The Elements List is empty' )
__lowercase =__lowercase =Node(elements_list[0] )
for i in range(1 , len(_lowerCAmelCase ) ):
__lowercase =Node(elements_list[i] )
__lowercase =current.next
return head
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if head_node is not None and isinstance(_lowerCAmelCase , _lowerCAmelCase ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
"""simple docstring"""
from doctest import testmod
testmod()
__lowercase =make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(_lowerCAmelCase )
print('Elements in Reverse:' )
print_reverse(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 166
|
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCamelCase = (
"""This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
warnings.warn(_lowerCAmelCase , _lowerCAmelCase )
requires_backends(_lowerCAmelCase , 'sklearn' )
return (preds == labels).mean()
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
warnings.warn(_lowerCAmelCase , _lowerCAmelCase )
requires_backends(_lowerCAmelCase , 'sklearn' )
__lowercase =simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )
__lowercase =fa_score(y_true=_lowerCAmelCase , y_pred=_lowerCAmelCase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
warnings.warn(_lowerCAmelCase , _lowerCAmelCase )
requires_backends(_lowerCAmelCase , 'sklearn' )
__lowercase =pearsonr(_lowerCAmelCase , _lowerCAmelCase )[0]
__lowercase =spearmanr(_lowerCAmelCase , _lowerCAmelCase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
warnings.warn(_lowerCAmelCase , _lowerCAmelCase )
requires_backends(_lowerCAmelCase , 'sklearn' )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase ), f"""Predictions and labels have mismatched lengths {len(_lowerCAmelCase )} and {len(_lowerCAmelCase )}"""
if task_name == "cola":
return {"mcc": matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "mrpc":
return acc_and_fa(_lowerCAmelCase , _lowerCAmelCase )
elif task_name == "sts-b":
return pearson_and_spearman(_lowerCAmelCase , _lowerCAmelCase )
elif task_name == "qqp":
return acc_and_fa(_lowerCAmelCase , _lowerCAmelCase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "rte":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
elif task_name == "hans":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
else:
raise KeyError(_lowerCAmelCase )
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
warnings.warn(_lowerCAmelCase , _lowerCAmelCase )
requires_backends(_lowerCAmelCase , 'sklearn' )
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError(f"""Predictions and labels have mismatched lengths {len(_lowerCAmelCase )} and {len(_lowerCAmelCase )}""" )
if task_name == "xnli":
return {"acc": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
else:
raise KeyError(_lowerCAmelCase )
| 166
| 1
|
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class _a :
'''simple docstring'''
UpperCAmelCase__: List[Any] = XGLMConfig
UpperCAmelCase__: Any = {}
UpperCAmelCase__: str = '''gelu'''
def __init__( self , A__ , A__=14 , A__=7 , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=512 , A__=0.0_2 , ):
A__ : str = parent
A__ : Any = batch_size
A__ : Optional[Any] = seq_length
A__ : Optional[int] = is_training
A__ : Tuple = use_input_mask
A__ : Tuple = use_labels
A__ : List[Any] = vocab_size
A__ : Optional[int] = d_model
A__ : Optional[Any] = num_hidden_layers
A__ : Union[str, Any] = num_attention_heads
A__ : List[Any] = ffn_dim
A__ : Dict = activation_function
A__ : Optional[Any] = activation_dropout
A__ : str = attention_dropout
A__ : List[Any] = max_position_embeddings
A__ : Tuple = initializer_range
A__ : Any = None
A__ : Any = 0
A__ : Dict = 2
A__ : Dict = 1
def __A ( self ):
return XGLMConfig.from_pretrained("""facebook/xglm-564M""" )
def __A ( self ):
A__ : Optional[int] = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
A__ : List[str] = None
if self.use_input_mask:
A__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
A__ : Union[str, Any] = self.get_config()
A__ : str = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def __A ( self ):
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=A__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=A__ , )
def __A ( self ):
A__ : List[str] = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) : Union[str, Any] = config_and_inputs
A__ : Tuple = {
"""input_ids""": input_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_tf
class _a (__magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__: Optional[int] = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCAmelCase__: int = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCAmelCase__: Any = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCAmelCase__: Tuple = False
UpperCAmelCase__: int = False
UpperCAmelCase__: Dict = False
def __A ( self ):
A__ : Tuple = TFXGLMModelTester(self )
A__ : List[Any] = ConfigTester(self , config_class=A__ , n_embd=37 )
def __A ( self ):
self.config_tester.run_common_tests()
@slow
def __A ( self ):
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : int = TFXGLMModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""" )
def __A ( self ):
super().test_resize_token_embeddings()
@require_tf
class _a (unittest.TestCase ):
'''simple docstring'''
@slow
def __A ( self , A__=True ):
A__ : Tuple = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
A__ : Any = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
A__ : Tuple = [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
# fmt: on
A__ : Dict = model.generate(A__ , do_sample=A__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , A__ )
@slow
def __A ( self ):
A__ : List[str] = XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
A__ : List[Any] = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
tf.random.set_seed(0 )
A__ : str = tokenizer("""Today is a nice day and""" , return_tensors="""tf""" )
A__ : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(""":/CPU:0""" ):
A__ : List[str] = model.generate(A__ , do_sample=A__ , seed=[7, 0] )
A__ : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=A__ )
A__ : List[Any] = (
"""Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
)
self.assertEqual(A__ , A__ )
@slow
def __A ( self ):
A__ : Optional[int] = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
A__ : Tuple = XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
A__ : List[str] = """left"""
# use different length sentences to test batching
A__ : Union[str, Any] = [
"""This is an extremelly long sentence that only exists to test the ability of the model to cope with """
"""left-padding, such as in batched generation. The output for the sequence below should be the same """
"""regardless of whether left padding is applied or not. When""",
"""Hello, my dog is a little""",
]
A__ : Union[str, Any] = tokenizer(A__ , return_tensors="""tf""" , padding=A__ )
A__ : int = inputs["""input_ids"""]
A__ : Dict = model.generate(input_ids=A__ , attention_mask=inputs["""attention_mask"""] , max_new_tokens=12 )
A__ : str = tokenizer(sentences[0] , return_tensors="""tf""" ).input_ids
A__ : Tuple = model.generate(input_ids=A__ , max_new_tokens=12 )
A__ : str = tokenizer(sentences[1] , return_tensors="""tf""" ).input_ids
A__ : int = model.generate(input_ids=A__ , max_new_tokens=12 )
A__ : Any = tokenizer.batch_decode(A__ , skip_special_tokens=A__ )
A__ : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A__ )
A__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=A__ )
A__ : Optional[Any] = [
"""This is an extremelly long sentence that only exists to test the ability of the model to cope with """
"""left-padding, such as in batched generation. The output for the sequence below should be the same """
"""regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
"""a single""",
"""Hello, my dog is a little bit of a shy one, but he is very friendly""",
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , [non_padded_sentence, padded_sentence] )
| 141
|
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class _a :
'''simple docstring'''
def __init__( self , A__ , A__=3 , A__=7 , A__=True , A__=True , A__=False , 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__=3 , A__=4 , A__=None , ):
A__ : List[Any] = parent
A__ : List[str] = batch_size
A__ : Optional[int] = seq_length
A__ : Optional[int] = is_training
A__ : Any = use_input_mask
A__ : Tuple = use_token_type_ids
A__ : str = use_labels
A__ : Tuple = vocab_size
A__ : Any = hidden_size
A__ : List[str] = num_hidden_layers
A__ : Optional[int] = num_attention_heads
A__ : Optional[Any] = intermediate_size
A__ : Optional[Any] = hidden_act
A__ : Tuple = hidden_dropout_prob
A__ : Union[str, Any] = attention_probs_dropout_prob
A__ : List[str] = max_position_embeddings
A__ : Union[str, Any] = type_vocab_size
A__ : str = type_sequence_label_size
A__ : Tuple = initializer_range
A__ : Tuple = num_labels
A__ : Dict = num_choices
A__ : List[str] = scope
def __A ( self ):
A__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Any = None
if self.use_input_mask:
A__ : int = random_attention_mask([self.batch_size, self.seq_length] )
A__ : str = None
A__ : Union[str, Any] = None
A__ : List[str] = None
A__ : Optional[Any] = None
if self.use_labels:
A__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A__ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
A__ : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self ):
return FalconConfig(
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 , pad_token_id=1 , new_decoder_architecture=A__ , )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ):
A__ : List[str] = FalconModel(config=A__ )
model.to(A__ )
model.eval()
A__ : int = model(A__ , attention_mask=A__ )
A__ : Union[str, Any] = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ):
A__ : Union[str, Any] = True
A__ : Union[str, Any] = FalconModel(A__ )
model.to(A__ )
model.eval()
A__ : Tuple = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , )
A__ : Union[str, Any] = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , )
A__ : List[str] = model(A__ , attention_mask=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ):
A__ : Any = FalconForCausalLM(config=A__ )
model.to(A__ )
model.eval()
A__ : Tuple = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ):
A__ : Optional[Any] = True
A__ : Union[str, Any] = True
A__ : int = FalconForCausalLM(config=A__ )
model.to(A__ )
model.eval()
# first forward pass
A__ : List[Any] = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , use_cache=A__ , )
A__ : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A__ : Tuple = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ : List[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
A__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
A__ : List[str] = torch.cat([input_mask, next_mask] , dim=-1 )
A__ : Optional[int] = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , output_hidden_states=A__ , )["""hidden_states"""][0]
A__ : Any = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , past_key_values=A__ , output_hidden_states=A__ , )["""hidden_states"""][0]
# select random slice
A__ : List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
A__ : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
A__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A__ , A__ , atol=1e-3 ) )
def __A ( self ):
A__ : List[str] = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) : Tuple = config_and_inputs
A__ : List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _a (__magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase__: Tuple = (FalconForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__: Optional[int] = (
{
'''feature-extraction''': FalconModel,
'''text-classification''': FalconForSequenceClassification,
'''text-generation''': FalconForCausalLM,
'''question-answering''': FalconForQuestionAnswering,
'''token-classification''': FalconForTokenClassification,
'''zero-shot''': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__: str = False
UpperCAmelCase__: int = False
def __A ( self ):
A__ : List[Any] = FalconModelTester(self )
A__ : Union[str, Any] = ConfigTester(self , config_class=A__ , hidden_size=37 )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
A__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def __A ( self ):
A__ , *A__ : List[Any] = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
A__ : Tuple = alibi
self.model_tester.create_and_check_model(A__ , *A__ )
def __A ( self ):
A__ , A__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
A__ : Optional[int] = 3
A__ : int = input_dict["""input_ids"""]
A__ : int = input_ids.ne(1 ).to(A__ )
A__ : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
A__ : Optional[int] = FalconForSequenceClassification(A__ )
model.to(A__ )
model.eval()
A__ : int = model(A__ , attention_mask=A__ , labels=A__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __A ( self ):
A__ , A__ : str = self.model_tester.prepare_config_and_inputs_for_common()
A__ : Dict = 3
A__ : Tuple = """single_label_classification"""
A__ : List[Any] = input_dict["""input_ids"""]
A__ : Dict = input_ids.ne(1 ).to(A__ )
A__ : Dict = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
A__ : Any = FalconForSequenceClassification(A__ )
model.to(A__ )
model.eval()
A__ : Any = model(A__ , attention_mask=A__ , labels=A__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __A ( self ):
A__ , A__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A__ : List[str] = input_dict["""input_ids"""]
A__ : List[str] = FalconForCausalLM(A__ )
model.to(A__ )
model.eval()
A__ : Any = model(A__ , use_cache=A__ )
A__ : Any = input_ids.shape[0]
A__ : Union[str, Any] = model._convert_to_rw_cache(result.past_key_values )
A__ : int = model._convert_cache_to_standard_format(A__ , A__ )
for layer in range(len(A__ ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def __A ( self ):
A__ , A__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
A__ : Optional[Any] = 3
A__ : List[Any] = """multi_label_classification"""
A__ : Tuple = input_dict["""input_ids"""]
A__ : List[Any] = input_ids.ne(1 ).to(A__ )
A__ : Optional[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
A__ : Optional[int] = FalconForSequenceClassification(A__ )
model.to(A__ )
model.eval()
A__ : List[Any] = model(A__ , attention_mask=A__ , labels=A__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __A ( self ):
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
A__ , A__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(A__ , """use_cache""" ):
return
A__ : Optional[Any] = model_class(A__ ).to(A__ )
if "use_cache" not in inputs:
A__ : Optional[int] = True
A__ : List[Any] = model(**A__ )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
A__ : str = (
getattr(A__ , """decoder_layers""" , A__ )
or getattr(A__ , """num_decoder_layers""" , A__ )
or config.num_hidden_layers
)
A__ : Dict = getattr(A__ , """num_kv_heads""" , config.num_attention_heads )
A__ : List[str] = getattr(A__ , """d_model""" , config.hidden_size )
A__ : Union[str, Any] = embed_dim // num_attention_heads
A__ : str = outputs["""past_key_values"""]
self.assertEqual(len(A__ ) , A__ )
A__ , A__ : int = inputs["""input_ids"""].shape
for i in range(A__ ):
if config.new_decoder_architecture:
A__ : Any = config.num_attention_heads
elif config.multi_query:
A__ : List[Any] = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class _a (unittest.TestCase ):
'''simple docstring'''
@slow
def __A ( self ):
A__ : Dict = AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
A__ : List[Any] = FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
model.eval()
model.to(A__ )
A__ : Optional[int] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(A__ )
A__ : Optional[Any] = (
"""My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
)
A__ : Any = model.generate(**A__ , do_sample=A__ , max_new_tokens=19 )
A__ : Optional[int] = tokenizer.batch_decode(A__ )[0]
self.assertEqual(A__ , A__ )
@slow
def __A ( self ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
A__ : Dict = AutoTokenizer.from_pretrained(A__ )
A__ : List[str] = FalconForCausalLM.from_pretrained(A__ )
model.eval()
model.to(A__ )
A__ : Union[str, Any] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(A__ )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**A__ , do_sample=A__ , max_new_tokens=4 )
model.generate(**A__ , do_sample=A__ , max_new_tokens=4 )
model.generate(**A__ , num_beams=2 , max_new_tokens=4 )
@slow
def __A ( self ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
A__ : Dict = AutoTokenizer.from_pretrained(A__ )
A__ : Any = FalconForCausalLM.from_pretrained(A__ )
model.eval()
model.to(device=A__ )
A__ : List[str] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(A__ )
# Test results are the same with and without cache
A__ : Tuple = model.generate(**A__ , do_sample=A__ , max_new_tokens=20 , use_cache=A__ )
A__ : Optional[Any] = model.generate(**A__ , do_sample=A__ , max_new_tokens=20 , use_cache=A__ )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 141
| 1
|
def __UpperCamelCase ( _A = 600851475143 ):
try:
lowerCAmelCase_ = int(_A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowerCAmelCase_ = 2
lowerCAmelCase_ = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowerCAmelCase_ = i
while n % i == 0:
lowerCAmelCase_ = n // i
i += 1
return int(_A )
if __name__ == "__main__":
print(f"{solution() = }")
| 278
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class A ( __UpperCAmelCase ):
__snake_case = 'vit'
def __init__( self, UpperCamelCase__=768, UpperCamelCase__=12, UpperCamelCase__=12, UpperCamelCase__=3072, UpperCamelCase__="gelu", UpperCamelCase__=0.0, UpperCamelCase__=0.0, UpperCamelCase__=0.02, UpperCamelCase__=1E-12, UpperCamelCase__=224, UpperCamelCase__=16, UpperCamelCase__=3, UpperCamelCase__=True, UpperCamelCase__=16, **UpperCamelCase__, ):
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = image_size
lowerCAmelCase_ = patch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = qkv_bias
lowerCAmelCase_ = encoder_stride
class A ( __UpperCAmelCase ):
__snake_case = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return 1E-4
| 278
| 1
|
"""simple docstring"""
import qiskit
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : Any =qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
lowerCamelCase__ : Optional[int] =qiskit.QuantumCircuit(__lowerCamelCase , __lowerCamelCase )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
lowerCamelCase__ : Optional[int] =qiskit.execute(__lowerCamelCase , __lowerCamelCase , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__lowerCamelCase )
if __name__ == "__main__":
_lowercase : int = single_qubit_measure(2, 2)
print(f'Total count for various states are: {counts}')
| 272
|
"""simple docstring"""
import numpy as np
from PIL import Image
def snake_case__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =np.array(__lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowerCamelCase__ : int =0
lowerCamelCase__ : int =0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : List[Any] =0
# compute the shape of the output matrix
lowerCamelCase__ : Union[str, Any] =(arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
lowerCamelCase__ : Union[str, Any] =np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
lowerCamelCase__ : str =np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : Optional[int] =0
return updated_arr
def snake_case__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =np.array(__lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowerCamelCase__ : str =0
lowerCamelCase__ : List[Any] =0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : List[Any] =0
# compute the shape of the output matrix
lowerCamelCase__ : Dict =(arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
lowerCamelCase__ : Optional[Any] =np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
lowerCamelCase__ : Optional[int] =int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : int =0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
_lowercase : int = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 272
| 1
|
from math import pi
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 95
|
def _A ( SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
a__ : Optional[Any] =len(SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
a__ : List[Any] =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
a__ : int =arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
# Reverse whole list
a__ : List[str] =arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
UpperCAmelCase : int = input("""Enter numbers separated by a comma:\n""").strip()
UpperCAmelCase : Optional[int] = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 95
| 1
|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = "▁"
UpperCamelCase_ = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
UpperCamelCase_ = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
UpperCamelCase_ = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
UpperCamelCase_ = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class a_ ( _snake_case ):
UpperCamelCase__ : str =VOCAB_FILES_NAMES
UpperCamelCase__ : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : Optional[int] =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : Optional[int] =["input_ids", "attention_mask"]
UpperCamelCase__ : List[int] =[]
UpperCamelCase__ : List[int] =[]
def __init__( self :Optional[Any] , _lowercase :Dict , _lowercase :List[str] , _lowercase :Dict=None , _lowercase :Union[str, Any]=None , _lowercase :Union[str, Any]="<s>" , _lowercase :Any="</s>" , _lowercase :List[Any]="</s>" , _lowercase :List[str]="<pad>" , _lowercase :Union[str, Any]="<unk>" , _lowercase :Tuple="m2m100" , _lowercase :Optional[Dict[str, Any]] = None , _lowercase :str=8 , **_lowercase :Any , ) -> None:
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = language_codes
UpperCAmelCase_ = FAIRSEQ_LANGUAGE_CODES[language_codes]
UpperCAmelCase_ = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
UpperCAmelCase_ = kwargs.get('''additional_special_tokens''' , [])
kwargs["additional_special_tokens"] += [
self.get_lang_token(_lowercase)
for lang_code in fairseq_language_code
if self.get_lang_token(_lowercase) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=_lowercase , tgt_lang=_lowercase , bos_token=_lowercase , eos_token=_lowercase , sep_token=_lowercase , unk_token=_lowercase , pad_token=_lowercase , language_codes=_lowercase , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=_lowercase , **_lowercase , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = load_json(_lowercase)
UpperCAmelCase_ = {v: k for k, v in self.encoder.items()}
UpperCAmelCase_ = spm_file
UpperCAmelCase_ = load_spm(_lowercase , self.sp_model_kwargs)
UpperCAmelCase_ = len(self.encoder)
UpperCAmelCase_ = {
self.get_lang_token(_lowercase): self.encoder_size + i for i, lang_code in enumerate(_lowercase)
}
UpperCAmelCase_ = {lang_code: self.encoder_size + i for i, lang_code in enumerate(_lowercase)}
UpperCAmelCase_ = {v: k for k, v in self.lang_token_to_id.items()}
UpperCAmelCase_ = src_lang if src_lang is not None else '''en'''
UpperCAmelCase_ = tgt_lang
UpperCAmelCase_ = self.get_lang_id(self._src_lang)
self.set_src_lang_special_tokens(self._src_lang)
UpperCAmelCase_ = num_madeup_words
@property
def __a ( self :Optional[Any]) -> int:
return len(self.encoder) + len(self.lang_token_to_id)
@property
def __a ( self :int) -> str:
return self._src_lang
@src_lang.setter
def __a ( self :Dict , _lowercase :str) -> None:
UpperCAmelCase_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def __a ( self :Optional[Any] , _lowercase :str) -> List[str]:
return self.sp_model.encode(_lowercase , out_type=_lowercase)
def __a ( self :Optional[int] , _lowercase :Tuple) -> List[str]:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(_lowercase , self.encoder[self.unk_token])
def __a ( self :int , _lowercase :int) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(_lowercase , self.unk_token)
def __a ( self :str , _lowercase :Any) -> Any:
UpperCAmelCase_ = []
UpperCAmelCase_ = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowercase) + token
UpperCAmelCase_ = []
else:
current_sub_tokens.append(_lowercase)
out_string += self.sp_model.decode(_lowercase)
return out_string.strip()
def __a ( self :Dict , _lowercase :List[int] , _lowercase :Optional[List[int]] = None , _lowercase :bool = False) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase)
UpperCAmelCase_ = [1] * len(self.prefix_tokens)
UpperCAmelCase_ = [1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowercase)) + suffix_ones
return prefix_ones + ([0] * len(_lowercase)) + ([0] * len(_lowercase)) + suffix_ones
def __a ( self :List[Any] , _lowercase :List[int] , _lowercase :Optional[List[int]] = None) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __a ( self :List[Any]) -> Dict:
UpperCAmelCase_ = {self.convert_ids_to_tokens(_lowercase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self :List[str]) -> Dict:
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self :str , _lowercase :Dict) -> None:
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
UpperCAmelCase_ = {}
UpperCAmelCase_ = load_spm(self.spm_file , self.sp_model_kwargs)
def __a ( self :Tuple , _lowercase :str , _lowercase :Optional[str] = None) -> Tuple[str]:
UpperCAmelCase_ = Path(_lowercase)
if not save_dir.is_dir():
raise OSError(f"{save_directory} should be a directory")
UpperCAmelCase_ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
UpperCAmelCase_ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowercase)
if os.path.abspath(self.spm_file) != os.path.abspath(_lowercase) and os.path.isfile(self.spm_file):
copyfile(self.spm_file , _lowercase)
elif not os.path.isfile(self.spm_file):
with open(_lowercase , '''wb''') as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(_lowercase)
return (str(_lowercase), str(_lowercase))
def __a ( self :Union[str, Any] , _lowercase :List[str] , _lowercase :str = "en" , _lowercase :Optional[List[str]] = None , _lowercase :str = "ro" , **_lowercase :Tuple , ) -> BatchEncoding:
UpperCAmelCase_ = src_lang
UpperCAmelCase_ = tgt_lang
self.set_src_lang_special_tokens(self.src_lang)
return super().prepare_seqaseq_batch(_lowercase , _lowercase , **_lowercase)
def __a ( self :int , _lowercase :Dict , _lowercase :Optional[str] , _lowercase :Optional[str] , **_lowercase :List[str]) -> Dict:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
UpperCAmelCase_ = src_lang
UpperCAmelCase_ = self(_lowercase , add_special_tokens=_lowercase , **_lowercase)
UpperCAmelCase_ = self.get_lang_id(_lowercase)
UpperCAmelCase_ = tgt_lang_id
return inputs
def __a ( self :Optional[Any]) -> Optional[int]:
self.set_src_lang_special_tokens(self.src_lang)
def __a ( self :List[str]) -> Tuple:
self.set_tgt_lang_special_tokens(self.tgt_lang)
def __a ( self :Optional[Any] , _lowercase :str) -> None:
UpperCAmelCase_ = self.get_lang_token(_lowercase)
UpperCAmelCase_ = self.lang_token_to_id[lang_token]
UpperCAmelCase_ = [self.cur_lang_id]
UpperCAmelCase_ = [self.eos_token_id]
def __a ( self :str , _lowercase :str) -> None:
UpperCAmelCase_ = self.get_lang_token(_lowercase)
UpperCAmelCase_ = self.lang_token_to_id[lang_token]
UpperCAmelCase_ = [self.cur_lang_id]
UpperCAmelCase_ = [self.eos_token_id]
def __a ( self :Dict , _lowercase :str) -> str:
return self.lang_code_to_token[lang]
def __a ( self :Union[str, Any] , _lowercase :str) -> int:
UpperCAmelCase_ = self.get_lang_token(_lowercase)
return self.lang_token_to_id[lang_token]
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> sentencepiece.SentencePieceProcessor:
'''simple docstring'''
UpperCAmelCase_ = sentencepiece.SentencePieceProcessor(**__UpperCAmelCase )
spm.Load(str(__UpperCAmelCase ) )
return spm
def A ( __UpperCAmelCase ) -> Union[Dict, List]:
'''simple docstring'''
with open(__UpperCAmelCase , '''r''' ) as f:
return json.load(__UpperCAmelCase )
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> None:
'''simple docstring'''
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase , indent=2 )
| 352
|
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = False, False, False
@dataclass
class a_ :
UpperCamelCase__ : Optional[int] =None
UpperCamelCase__ : bool =True
UpperCamelCase__ : bool =True
UpperCamelCase__ : Optional[str] =None
# Automatically constructed
UpperCamelCase__ : ClassVar[str] ="dict"
UpperCamelCase__ : ClassVar[Any] =pa.struct({"bytes": pa.binary(), "path": pa.string()} )
UpperCamelCase__ : str =field(default="Audio" , init=_snake_case , repr=_snake_case )
def __call__( self :List[Any]) -> List[Any]:
return self.pa_type
def __a ( self :Any , _lowercase :Union[str, bytes, dict]) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''') from err
if isinstance(_lowercase , _lowercase):
return {"bytes": None, "path": value}
elif isinstance(_lowercase , _lowercase):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
UpperCAmelCase_ = BytesIO()
sf.write(_lowercase , value['''array'''] , value['''sampling_rate'''] , format='''wav''')
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm'''):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''') is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''')
if value.get('''bytes'''):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
UpperCAmelCase_ = np.frombuffer(value['''bytes'''] , dtype=np.intaa).astype(np.floataa) / 32767
else:
UpperCAmelCase_ = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''').astype(np.floataa) / 32767
UpperCAmelCase_ = BytesIO(bytes())
sf.write(_lowercase , _lowercase , value['''sampling_rate'''] , format='''wav''')
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''')}
elif value.get('''bytes''') is not None or value.get('''path''') is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
else:
raise ValueError(
f"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.")
def __a ( self :Dict , _lowercase :dict , _lowercase :Optional[Dict[str, Union[str, bool, None]]] = None) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''')
UpperCAmelCase_ , UpperCAmelCase_ = (value['''path'''], BytesIO(value['''bytes'''])) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''') from err
UpperCAmelCase_ = xsplitext(_lowercase)[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''')
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''')
if file is None:
UpperCAmelCase_ = token_per_repo_id or {}
UpperCAmelCase_ = path.split('''::''')[-1]
try:
UpperCAmelCase_ = string_to_dict(_lowercase , config.HUB_DATASETS_URL)['''repo_id''']
UpperCAmelCase_ = token_per_repo_id[repo_id]
except (ValueError, KeyError):
UpperCAmelCase_ = None
with xopen(_lowercase , '''rb''' , use_auth_token=_lowercase) as f:
UpperCAmelCase_ , UpperCAmelCase_ = sf.read(_lowercase)
else:
UpperCAmelCase_ , UpperCAmelCase_ = sf.read(_lowercase)
UpperCAmelCase_ = array.T
if self.mono:
UpperCAmelCase_ = librosa.to_mono(_lowercase)
if self.sampling_rate and self.sampling_rate != sampling_rate:
UpperCAmelCase_ = librosa.resample(_lowercase , orig_sr=_lowercase , target_sr=self.sampling_rate)
UpperCAmelCase_ = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def __a ( self :Union[str, Any]) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''')
return {
"bytes": Value('''binary'''),
"path": Value('''string'''),
}
def __a ( self :int , _lowercase :Union[pa.StringArray, pa.StructArray]) -> pa.StructArray:
if pa.types.is_string(storage.type):
UpperCAmelCase_ = pa.array([None] * len(_lowercase) , type=pa.binary())
UpperCAmelCase_ = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null())
elif pa.types.is_binary(storage.type):
UpperCAmelCase_ = pa.array([None] * len(_lowercase) , type=pa.string())
UpperCAmelCase_ = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null())
elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices('''array'''):
UpperCAmelCase_ = pa.array([Audio().encode_example(_lowercase) if x is not None else None for x in storage.to_pylist()])
elif pa.types.is_struct(storage.type):
if storage.type.get_field_index('''bytes''') >= 0:
UpperCAmelCase_ = storage.field('''bytes''')
else:
UpperCAmelCase_ = pa.array([None] * len(_lowercase) , type=pa.binary())
if storage.type.get_field_index('''path''') >= 0:
UpperCAmelCase_ = storage.field('''path''')
else:
UpperCAmelCase_ = pa.array([None] * len(_lowercase) , type=pa.string())
UpperCAmelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null())
return array_cast(_lowercase , self.pa_type)
def __a ( self :Any , _lowercase :pa.StructArray) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(_lowercase :Tuple):
with xopen(_lowercase , '''rb''') as f:
UpperCAmelCase_ = f.read()
return bytes_
UpperCAmelCase_ = pa.array(
[
(path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
UpperCAmelCase_ = pa.array(
[os.path.basename(_lowercase) if path is not None else None for path in storage.field('''path''').to_pylist()] , type=pa.string() , )
UpperCAmelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null())
return array_cast(_lowercase , self.pa_type)
| 344
| 0
|
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def a ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_UpperCAmelCase , _UpperCAmelCase ) ) )
def a ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ):
'''simple docstring'''
if dataset.ndim != value_array.ndim:
__UpperCAmelCase : Union[str, Any] = (
'''Wrong input data\'s dimensions... '''
f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
)
raise ValueError(_UpperCAmelCase )
try:
if dataset.shape[1] != value_array.shape[1]:
__UpperCAmelCase : Optional[int] = (
'''Wrong input data\'s shape... '''
f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
)
raise ValueError(_UpperCAmelCase )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
__UpperCAmelCase : Dict = (
'''Input data have different datatype... '''
f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
)
raise TypeError(_UpperCAmelCase )
__UpperCAmelCase : Tuple = []
for value in value_array:
__UpperCAmelCase : List[Any] = euclidean(_UpperCAmelCase , dataset[0] )
__UpperCAmelCase : Union[str, Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
__UpperCAmelCase : List[Any] = euclidean(_UpperCAmelCase , _UpperCAmelCase )
if dist > temp_dist:
__UpperCAmelCase : Optional[int] = temp_dist
__UpperCAmelCase : Dict = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def a ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ):
'''simple docstring'''
return np.dot(_UpperCAmelCase , _UpperCAmelCase ) / (norm(_UpperCAmelCase ) * norm(_UpperCAmelCase ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 226
|
import qiskit
def a ( _UpperCAmelCase : int , _UpperCAmelCase : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCAmelCase : Optional[Any] = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
__UpperCAmelCase : str = qiskit.execute(_UpperCAmelCase , _UpperCAmelCase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(_UpperCAmelCase )
if __name__ == "__main__":
__A =half_adder(1, 1)
print(f'''Half Adder Output Qubit Counts: {counts}''')
| 226
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''FNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''FNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FNetForMaskedLM''',
'''FNetForMultipleChoice''',
'''FNetForNextSentencePrediction''',
'''FNetForPreTraining''',
'''FNetForQuestionAnswering''',
'''FNetForSequenceClassification''',
'''FNetForTokenClassification''',
'''FNetLayer''',
'''FNetModel''',
'''FNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 368
|
'''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 lowercase_ :
"""simple docstring"""
def __init__( self : int ,lowercase__ : str ,lowercase__ : List[Any]=1_3 ,lowercase__ : Optional[int]=3_2 ,lowercase__ : Any=3 ,lowercase__ : int=4 ,lowercase__ : Optional[int]=[1_0, 2_0, 3_0, 4_0] ,lowercase__ : List[Any]=[2, 2, 3, 2] ,lowercase__ : List[Any]=True ,lowercase__ : Optional[Any]=True ,lowercase__ : int=3_7 ,lowercase__ : Union[str, Any]="gelu" ,lowercase__ : Tuple=1_0 ,lowercase__ : int=0.0_2 ,lowercase__ : Any=["stage2", "stage3", "stage4"] ,lowercase__ : Optional[Any]=3 ,lowercase__ : Tuple=None ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = out_features
__lowercase = num_labels
__lowercase = scope
__lowercase = num_stages
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : str ):
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 SCREAMING_SNAKE_CASE ( self : List[Any] ):
return UperNetConfig(
backbone_config=self.get_backbone_config() ,hidden_size=5_1_2 ,pool_scales=[1, 2, 3, 6] ,use_auxiliary_head=lowercase__ ,auxiliary_loss_weight=0.4 ,auxiliary_in_channels=4_0 ,auxiliary_channels=2_5_6 ,auxiliary_num_convs=1 ,auxiliary_concat_input=lowercase__ ,loss_ignore_index=2_5_5 ,num_labels=self.num_labels ,)
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[str] ,lowercase__ : Any ,lowercase__ : Any ):
__lowercase = UperNetForSemanticSegmentation(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size, self.image_size) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Tuple = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : List[Any] = False
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = UperNetModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
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 SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__ )
@unittest.skip(reason='''UperNet does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE ( self : str ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def SCREAMING_SNAKE_CASE ( self : Union[str, 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 SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE ( self : Any ):
def check_hidden_states_output(lowercase__ : List[str] ,lowercase__ : int ,lowercase__ : List[str] ):
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(lowercase__ ) ,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] ,)
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = _config_zero_init(lowercase__ )
__lowercase = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
__lowercase = model_class(config=lowercase__ )
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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = UperNetForSemanticSegmentation.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''' , repo_type='''dataset''' , filename='''ADE_val_00000001.jpg''' )
__lowercase = Image.open(A__ ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''' )
__lowercase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''' ).to(lowercase__ )
__lowercase = prepare_img()
__lowercase = processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
with torch.no_grad():
__lowercase = model(**lowercase__ )
__lowercase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = 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(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,lowercase__ ,atol=1e-4 ) )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''' )
__lowercase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''' ).to(lowercase__ )
__lowercase = prepare_img()
__lowercase = processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
with torch.no_grad():
__lowercase = model(**lowercase__ )
__lowercase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = 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(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,lowercase__ ,atol=1e-4 ) )
| 52
| 0
|
'''simple docstring'''
from functools import reduce
_UpperCamelCase : List[str] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def __UpperCAmelCase ( A : str = N ) -> int:
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda A , A : str(int(A ) * int(A ) ) , n[i : i + 1_3] ) )
for i in range(len(A ) - 1_2 ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 304
|
'''simple docstring'''
_UpperCamelCase : Tuple = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_UpperCamelCase : Any = [{'type': 'code', 'content': INSTALL_CONTENT}]
_UpperCamelCase : Dict = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 304
| 1
|
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a_ ( __snake_case : Optional[int] ) -> int:
"""simple docstring"""
return 1 / (1 + np.exp(-z ))
def a_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Dict:
"""simple docstring"""
return (-y * np.log(__snake_case ) - (1 - y) * np.log(1 - h )).mean()
def a_ ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : str ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =np.dot(__snake_case , __snake_case )
return np.sum(y * scores - np.log(1 + np.exp(__snake_case ) ) )
def a_ ( __snake_case : Optional[Any] , __snake_case : Any , __snake_case : Optional[int] , __snake_case : Dict=7_0000 ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =np.zeros(x.shape[1] )
for iterations in range(__snake_case ):
lowerCamelCase_ =np.dot(__snake_case , __snake_case )
lowerCamelCase_ =sigmoid_function(__snake_case )
lowerCamelCase_ =np.dot(x.T , h - y ) / y.size
lowerCamelCase_ =theta - alpha * gradient # updating the weights
lowerCamelCase_ =np.dot(__snake_case , __snake_case )
lowerCamelCase_ =sigmoid_function(__snake_case )
lowerCamelCase_ =cost_function(__snake_case , __snake_case )
if iterations % 100 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
a_ : List[str] = datasets.load_iris()
a_ : int = iris.data[:, :2]
a_ : Tuple = (iris.target != 0) * 1
a_ : Union[str, Any] = 0.1
a_ : Dict = logistic_reg(alpha, x, y, max_iterations=7_00_00)
print("""theta: """, theta) # printing the theta i.e our weights vector
def a_ ( __snake_case : Optional[Any] ) -> Dict:
"""simple docstring"""
return sigmoid_function(
np.dot(__snake_case , __snake_case ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
((a_) , (a_)) : Dict = (x[:, 0].min(), x[:, 0].max())
((a_) , (a_)) : List[str] = (x[:, 1].min(), x[:, 1].max())
((a_) , (a_)) : Any = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
a_ : int = np.c_[xxa.ravel(), xxa.ravel()]
a_ : Optional[Any] = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")
plt.legend()
plt.show()
| 6
|
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =VQModel
lowercase : Union[str, Any] ='sample'
@property
def lowercase__ ( self, lowerCAmelCase=(32, 32) ):
"""simple docstring"""
lowerCamelCase_ =4
lowerCamelCase_ =3
lowerCamelCase_ =floats_tensor((batch_size, num_channels) + sizes ).to(lowerCAmelCase )
return {"sample": image}
@property
def lowercase__ ( self ):
"""simple docstring"""
return (3, 32, 32)
@property
def lowercase__ ( self ):
"""simple docstring"""
return (3, 32, 32)
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 3,
}
lowerCamelCase_ =self.dummy_input
return init_dict, inputs_dict
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =VQModel.from_pretrained('''fusing/vqgan-dummy''', output_loading_info=lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
self.assertEqual(len(loading_info['''missing_keys'''] ), 0 )
model.to(lowerCAmelCase )
lowerCamelCase_ =model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =VQModel.from_pretrained('''fusing/vqgan-dummy''' )
model.to(lowerCAmelCase ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
lowerCamelCase_ =torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size )
lowerCamelCase_ =image.to(lowerCAmelCase )
with torch.no_grad():
lowerCamelCase_ =model(lowerCAmelCase ).sample
lowerCamelCase_ =output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
lowerCamelCase_ =torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
| 6
| 1
|
from ...processing_utils import ProcessorMixin
class a ( lowercase__ ):
"""simple docstring"""
a : Dict = ['image_processor', 'feature_extractor']
a : Optional[int] = 'TvltImageProcessor'
a : int = 'TvltFeatureExtractor'
def __init__( self : int , __lowercase : Optional[Any] , __lowercase : Tuple ) -> Tuple:
super().__init__(image_processor=__lowercase , feature_extractor=__lowercase )
__UpperCAmelCase : Optional[int] = image_processor
__UpperCAmelCase : Tuple = feature_extractor
def __call__( self : Tuple , __lowercase : Optional[int]=None , __lowercase : List[str]=None , __lowercase : List[str]=None , __lowercase : int=None , __lowercase : List[str]=False , __lowercase : Union[str, Any]=False , *__lowercase : Optional[int] , **__lowercase : Optional[int] , ) -> Dict:
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
__UpperCAmelCase : Optional[int] = None
if images is not None:
__UpperCAmelCase : List[Any] = self.image_processor(__lowercase , mask_pixel=__lowercase , *__lowercase , **__lowercase )
if images_mixed is not None:
__UpperCAmelCase : Tuple = self.image_processor(__lowercase , is_mixed=__lowercase , *__lowercase , **__lowercase )
if audio is not None:
__UpperCAmelCase : Optional[Any] = self.feature_extractor(
__lowercase , *__lowercase , sampling_rate=__lowercase , mask_audio=__lowercase , **__lowercase )
__UpperCAmelCase : Tuple = {}
if audio is not None:
output_dict.update(__lowercase )
if images is not None:
output_dict.update(__lowercase )
if images_mixed_dict is not None:
output_dict.update(__lowercase )
return output_dict
@property
def UpperCAmelCase ( self : Tuple ) -> Any:
__UpperCAmelCase : Dict = self.image_processor.model_input_names
__UpperCAmelCase : Tuple = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 114
|
from __future__ import annotations
from math import pi
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 114
| 1
|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase__ :
def __init__( self : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple=3 , SCREAMING_SNAKE_CASE__ : List[str]=32 , SCREAMING_SNAKE_CASE__ : int=3 , SCREAMING_SNAKE_CASE__ : int=10 , SCREAMING_SNAKE_CASE__ : Any=[10, 20, 30, 40] , SCREAMING_SNAKE_CASE__ : str=[1, 1, 2, 1] , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Any="relu" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE__ : Tuple=None , ) -> int:
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = embeddings_size
__lowerCamelCase = hidden_sizes
__lowerCamelCase = depths
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_act
__lowerCamelCase = num_labels
__lowerCamelCase = scope
__lowerCamelCase = len(SCREAMING_SNAKE_CASE__ )
def __A ( self : Tuple ) -> List[Any]:
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def __A ( self : Union[str, Any] ) -> Tuple:
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[Any]:
__lowerCamelCase = TFResNetModel(config=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = model(SCREAMING_SNAKE_CASE__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __A ( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple ) -> List[Any]:
__lowerCamelCase = self.num_labels
__lowerCamelCase = TFResNetForImageClassification(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self : int ) -> Union[str, Any]:
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( __lowercase , __lowercase , unittest.TestCase ):
a__ : str = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a__ : Tuple = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a__ : List[str] = False
a__ : List[str] = False
a__ : Dict = False
a__ : Tuple = False
a__ : str = False
def __A ( self : Any ) -> int:
__lowerCamelCase = TFResNetModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , has_text_modality=SCREAMING_SNAKE_CASE__ )
def __A ( self : Tuple ) -> List[Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __A ( self : Union[str, Any] ) -> int:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def __A ( self : Tuple ) -> Union[str, Any]:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def __A ( self : Optional[int] ) -> Optional[int]:
pass
def __A ( self : List[str] ) -> Tuple:
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE__ )
def __A ( self : Tuple ) -> List[str]:
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __A ( self : Tuple ) -> int:
def check_hidden_states_output(SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
__lowerCamelCase = model_class(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
__lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , expected_num_stages + 1 )
# ResNet'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] , )
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__lowerCamelCase = layer_type
__lowerCamelCase = True
check_hidden_states_output(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __A ( self : Any ) -> Dict:
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __A ( self : Dict ) -> int:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TFResNetModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __magic_name__ ( ) -> Union[str, Any]:
__lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def __A ( self : int ) -> str:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __A ( self : str ) -> Optional[int]:
__lowerCamelCase = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='''tf''' )
# forward pass
__lowerCamelCase = model(**SCREAMING_SNAKE_CASE__ )
# verify the logits
__lowerCamelCase = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
| 339
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
__lowerCamelCase = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(__lowerCAmelCase ).content ).xpath(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : List[str] = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 339
| 1
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "philschmid/bart-large-cnn-samsum"
lowercase_ = (
"This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
"and returns a summary of the text."
)
lowercase_ = "summarizer"
lowercase_ = AutoTokenizer
lowercase_ = AutoModelForSeqaSeqLM
lowercase_ = ["text"]
lowercase_ = ["text"]
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : int) ->Dict:
'''simple docstring'''
return self.pre_processor(UpperCAmelCase_ , return_tensors="pt" , truncation=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : int) ->List[str]:
'''simple docstring'''
return self.model.generate(**UpperCAmelCase_)[0]
def SCREAMING_SNAKE_CASE_ (self : int , UpperCAmelCase_ : Tuple) ->Optional[int]:
'''simple docstring'''
return self.pre_processor.decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_)
| 10
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
),
}
class __SCREAMING_SNAKE_CASE( a_ ):
_UpperCAmelCase = "gptsan-japanese"
_UpperCAmelCase = [
"past_key_values",
]
_UpperCAmelCase = {
"hidden_size": "d_model",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self: Optional[Any] , UpperCamelCase: List[str]=3_60_00 , UpperCamelCase: List[str]=12_80 , UpperCamelCase: List[Any]=10_24 , UpperCamelCase: Any=81_92 , UpperCamelCase: Dict=40_96 , UpperCamelCase: Optional[int]=1_28 , UpperCamelCase: Any=10 , UpperCamelCase: List[Any]=0 , UpperCamelCase: Dict=16 , UpperCamelCase: Tuple=16 , UpperCamelCase: Union[str, Any]=1_28 , UpperCamelCase: List[Any]=0.0 , UpperCamelCase: Union[str, Any]=1e-5 , UpperCamelCase: int=False , UpperCamelCase: Optional[int]=0.0 , UpperCamelCase: Dict="float32" , UpperCamelCase: Any=False , UpperCamelCase: Dict=False , UpperCamelCase: List[str]=False , UpperCamelCase: Union[str, Any]=0.002 , UpperCamelCase: int=False , UpperCamelCase: str=True , UpperCamelCase: Dict=3_59_98 , UpperCamelCase: Optional[Any]=3_59_95 , UpperCamelCase: Optional[Any]=3_59_99 , **UpperCamelCase: Optional[int] , ) -> Optional[int]:
snake_case__ = vocab_size
snake_case__ = max_position_embeddings
snake_case__ = d_model
snake_case__ = d_ff
snake_case__ = d_ext
snake_case__ = d_spout
snake_case__ = num_switch_layers
snake_case__ = num_ext_layers
snake_case__ = num_switch_layers + num_ext_layers
snake_case__ = num_heads
snake_case__ = num_experts
snake_case__ = expert_capacity
snake_case__ = dropout_rate
snake_case__ = layer_norm_epsilon
snake_case__ = router_bias
snake_case__ = router_jitter_noise
snake_case__ = router_dtype
snake_case__ = router_ignore_padding_tokens
snake_case__ = output_hidden_states
snake_case__ = output_attentions
snake_case__ = initializer_factor
snake_case__ = output_router_logits
snake_case__ = use_cache
super().__init__(
separator_token_id=UpperCamelCase , pad_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase , )
| 307
| 0
|
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _lowercase):
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : str ) -> List[str]:
with open(__UpperCamelCase , encoding='''utf-8''' ) as input_file:
_UpperCamelCase = re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''' )
_UpperCamelCase = input_file.read()
_UpperCamelCase = regexp.search(__UpperCamelCase )
return match
def _UpperCamelCase ( self : Optional[int] , __UpperCamelCase : str ) -> Union[str, Any]:
with open(__UpperCamelCase , encoding='''utf-8''' ) as input_file:
_UpperCamelCase = re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''' , re.DOTALL )
_UpperCamelCase = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
_UpperCamelCase = regexp.finditer(__UpperCamelCase )
_UpperCamelCase = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _UpperCamelCase ( self : Optional[int] ) -> str:
_UpperCamelCase = Path('''./datasets''' )
_UpperCamelCase = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__UpperCamelCase ) ):
raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}''' )
def _UpperCamelCase ( self : Dict ) -> int:
_UpperCamelCase = Path('''./datasets''' )
_UpperCamelCase = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_print_statements(str(__UpperCamelCase ) ):
raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 54
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class UpperCAmelCase_ ( _lowercase):
snake_case__ = '''deta'''
snake_case__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : Dict , __UpperCamelCase : List[str]=None , __UpperCamelCase : Any=900 , __UpperCamelCase : Dict=2048 , __UpperCamelCase : Dict=6 , __UpperCamelCase : Union[str, Any]=2048 , __UpperCamelCase : str=8 , __UpperCamelCase : List[Any]=6 , __UpperCamelCase : Union[str, Any]=1024 , __UpperCamelCase : Optional[int]=8 , __UpperCamelCase : Optional[Any]=0.0 , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Any="relu" , __UpperCamelCase : Dict=256 , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : int=0.0 , __UpperCamelCase : Any=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1.0 , __UpperCamelCase : Dict=True , __UpperCamelCase : str=False , __UpperCamelCase : List[Any]="sine" , __UpperCamelCase : List[Any]=5 , __UpperCamelCase : Dict=4 , __UpperCamelCase : int=4 , __UpperCamelCase : Dict=True , __UpperCamelCase : List[Any]=300 , __UpperCamelCase : Any=True , __UpperCamelCase : List[str]=True , __UpperCamelCase : List[str]=1 , __UpperCamelCase : Optional[Any]=5 , __UpperCamelCase : List[Any]=2 , __UpperCamelCase : Tuple=1 , __UpperCamelCase : int=1 , __UpperCamelCase : str=5 , __UpperCamelCase : List[str]=2 , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : Tuple=0.2_5 , **__UpperCamelCase : Union[str, Any] , ) -> Optional[int]:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCamelCase = backbone_config.pop('''model_type''' )
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__UpperCamelCase )
_UpperCamelCase = backbone_config
_UpperCamelCase = num_queries
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
# deformable attributes
_UpperCamelCase = num_feature_levels
_UpperCamelCase = encoder_n_points
_UpperCamelCase = decoder_n_points
_UpperCamelCase = two_stage
_UpperCamelCase = two_stage_num_proposals
_UpperCamelCase = with_box_refine
_UpperCamelCase = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
_UpperCamelCase = focal_alpha
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def _UpperCamelCase ( self : Optional[Any] ) -> int:
return self.encoder_attention_heads
@property
def _UpperCamelCase ( self : List[str] ) -> int:
return self.d_model
def _UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
_UpperCamelCase = copy.deepcopy(self.__dict__ )
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
| 54
| 1
|
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
super().__init__()
self.register_modules(unet=__UpperCAmelCase ,scheduler=__UpperCAmelCase )
def __call__( self ) -> str:
lowerCAmelCase__ : Tuple = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,)
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : Union[str, Any] = self.unet(__UpperCAmelCase ,__UpperCAmelCase ).sample
lowerCAmelCase__ : List[Any] = self.scheduler.step(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ).prev_sample
lowerCAmelCase__ : Optional[int] = scheduler_output - scheduler_output + torch.ones_like(__UpperCAmelCase )
return result
| 37
|
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
try:
with open(UpperCamelCase , """rb""" ) as flax_state_f:
lowerCAmelCase__ : Union[str, Any] = from_bytes(UpperCamelCase , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(UpperCamelCase ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(UpperCamelCase , UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
lowerCAmelCase__ : str = flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase : x.dtype == jnp.bfloataa , UpperCamelCase ) ).values()
if any(UpperCamelCase ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
lowerCAmelCase__ : Dict = jax.tree_util.tree_map(
lambda UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , UpperCamelCase )
lowerCAmelCase__ : Any = """"""
lowerCAmelCase__ : Any = flatten_dict(UpperCamelCase , sep=""".""" )
lowerCAmelCase__ : Optional[int] = pt_model.state_dict()
# keep track of unexpected & missing keys
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : int = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowerCAmelCase__ : Union[str, Any] = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
lowerCAmelCase__ : Optional[int] = flax_key_tuple_array[:-1] + ["""weight"""]
lowerCAmelCase__ : Any = jnp.transpose(UpperCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
lowerCAmelCase__ : str = flax_key_tuple_array[:-1] + ["""weight"""]
lowerCAmelCase__ : Any = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
lowerCAmelCase__ : int = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(UpperCamelCase ):
lowerCAmelCase__ : List[str] = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
lowerCAmelCase__ : Union[str, Any] = """.""".join(UpperCamelCase )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowerCAmelCase__ : int = np.asarray(UpperCamelCase ) if not isinstance(UpperCamelCase , np.ndarray ) else flax_tensor
lowerCAmelCase__ : int = torch.from_numpy(UpperCamelCase )
# remove from missing keys
missing_keys.remove(UpperCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(UpperCamelCase )
pt_model.load_state_dict(UpperCamelCase )
# re-transform missing_keys to list
lowerCAmelCase__ : Optional[int] = list(UpperCamelCase )
if len(UpperCamelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
if len(UpperCamelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
""" use it for predictions and inference.""" )
return pt_model
| 37
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "microsoft/speecht5_tts"
__UpperCamelCase = (
"This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
"text to read (in English) and returns a waveform object containing the sound."
)
__UpperCamelCase = "text_reader"
__UpperCamelCase = SpeechTaProcessor
__UpperCamelCase = SpeechTaForTextToSpeech
__UpperCamelCase = SpeechTaHifiGan
__UpperCamelCase = ["text"]
__UpperCamelCase = ["audio"]
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
if self.post_processor is None:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '''microsoft/speecht5_hifigan'''
super().setup()
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict=None):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = self.pre_processor(text=lowercase_ , return_tensors='''pt''' , truncation=lowercase_)
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.''')
SCREAMING_SNAKE_CASE_ : int = load_dataset('''Matthijs/cmu-arctic-xvectors''' , split='''validation''')
SCREAMING_SNAKE_CASE_ : str = torch.tensor(embeddings_dataset[7305]['''xvector''']).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : Union[str, Any]):
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : List[str]):
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowercase_).cpu().detach()
| 318
|
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase_ : List[str] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase_ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _A (__a , __a , __a , __a ) -> bool:
"""simple docstring"""
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _A (__a ) -> tuple[int, int] | None:
"""simple docstring"""
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _A (__a ) -> Matrix | None:
"""simple docstring"""
if location := find_empty_location(__a ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(__a , __a , __a , __a ):
SCREAMING_SNAKE_CASE_ : Tuple = digit
if sudoku(__a ) is not None:
return grid
SCREAMING_SNAKE_CASE_ : Any = 0
return None
def _A (__a ) -> None:
"""simple docstring"""
for row in grid:
for cell in row:
print(__a , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("""\nExample grid:\n""" + """=""" * 20)
print_solution(example_grid)
print("""\nExample grid solution:""")
UpperCAmelCase_ : str = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("""Cannot find a solution.""")
| 318
| 1
|
'''simple docstring'''
def a_ ( __snake_case : str , __snake_case : Dict ) -> Dict:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def a_ ( __snake_case : Tuple , __snake_case : Union[str, Any]=0 ) -> List[str]:
"""simple docstring"""
return sorted(__snake_case , key=lambda __snake_case : x[column] )
def a_ ( __snake_case : Any , __snake_case : Dict , __snake_case : Tuple=float('''inf''' ) ) -> Union[str, Any]:
"""simple docstring"""
for i in range(points_counts - 1 ):
for j in range(i + 1 , __snake_case ):
lowerCamelCase_ =euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCamelCase_ =current_dis
return min_dis
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple , __snake_case : Optional[int]=float('''inf''' ) ) -> str:
"""simple docstring"""
for i in range(min(6 , points_counts - 1 ) , __snake_case ):
for j in range(max(0 , i - 6 ) , __snake_case ):
lowerCamelCase_ =euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCamelCase_ =current_dis
return min_dis
def a_ ( __snake_case : Any , __snake_case : Optional[int] , __snake_case : Dict ) -> Optional[Any]:
"""simple docstring"""
# base case
if points_counts <= 3:
return dis_between_closest_pair(__snake_case , __snake_case )
# recursion
lowerCamelCase_ =points_counts // 2
lowerCamelCase_ =closest_pair_of_points_sqr(
__snake_case , points_sorted_on_y[:mid] , __snake_case )
lowerCamelCase_ =closest_pair_of_points_sqr(
__snake_case , points_sorted_on_y[mid:] , points_counts - mid )
lowerCamelCase_ =min(__snake_case , __snake_case )
lowerCamelCase_ =[]
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(__snake_case )
lowerCamelCase_ =dis_between_closest_in_strip(
__snake_case , len(__snake_case ) , __snake_case )
return min(__snake_case , __snake_case )
def a_ ( __snake_case : Union[str, Any] , __snake_case : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =column_based_sort(__snake_case , column=0 )
lowerCamelCase_ =column_based_sort(__snake_case , column=1 )
return (
closest_pair_of_points_sqr(
__snake_case , __snake_case , __snake_case )
) ** 0.5
if __name__ == "__main__":
a_ : Optional[int] = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 75
|
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_A = '''\
'''
_A = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
'''
_A = '''
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to \'cuda\' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
>>> results = perplexity.compute(model_id=\'gpt2\',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
78.22
>>> print(round(results["perplexities"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = datasets.load_dataset("wikitext",
... "wikitext-2-raw-v1",
... split="test")["text"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!=\'\']
>>> results = perplexity.compute(model_id=\'gpt2\',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
60.35
>>> print(round(results["perplexities"][0], 2))
81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def lowerCamelCase_ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1_6 , __UpperCamelCase = True , __UpperCamelCase=None ):
"""simple docstring"""
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
UpperCamelCase_ = """cuda"""
else:
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
UpperCamelCase_ = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
UpperCamelCase_ = model.to(__UpperCamelCase )
UpperCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
UpperCamelCase_ = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
UpperCamelCase_ = model.config.max_length - 1
else:
UpperCamelCase_ = model.config.max_length
UpperCamelCase_ = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors="""pt""" , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
UpperCamelCase_ = encodings["""input_ids"""]
UpperCamelCase_ = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
UpperCamelCase_ = []
UpperCamelCase_ = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
UpperCamelCase_ = min(start_index + batch_size , len(__UpperCamelCase ) )
UpperCamelCase_ = encoded_texts[start_index:end_index]
UpperCamelCase_ = attn_masks[start_index:end_index]
if add_start_token:
UpperCamelCase_ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
UpperCamelCase_ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
UpperCamelCase_ = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
UpperCamelCase_ = encoded_batch
with torch.no_grad():
UpperCamelCase_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
UpperCamelCase_ = out_logits[..., :-1, :].contiguous()
UpperCamelCase_ = labels[..., 1:].contiguous()
UpperCamelCase_ = attn_mask[..., 1:].contiguous()
UpperCamelCase_ = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 122
| 0
|
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _UpperCAmelCase ( unittest.TestCase ):
a__ : Optional[int] = MODEL_FOR_MASKED_LM_MAPPING
a__ : Tuple = TF_MODEL_FOR_MASKED_LM_MAPPING
def a ( self : List[str] ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def a ( self : Tuple ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''tf''' )
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is grouped''', '''score''': 2.1E-05, '''token''': 3_80_15, '''token_str''': ''' grouped'''},
{'''sequence''': '''My name is accuser''', '''score''': 2.1E-05, '''token''': 2_55_06, '''token_str''': ''' accuser'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is grouped''',
'''score''': 2.1E-05,
'''token''': 3_80_15,
'''token_str''': ''' grouped''',
},
{
'''sequence''': '''The largest city in France is accuser''',
'''score''': 2.1E-05,
'''token''': 2_55_06,
'''token_str''': ''' accuser''',
},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Patrick''', '''score''': 2E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 1.9E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
] , )
@require_torch
def a ( self : Optional[int] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''pt''' )
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Maul''', '''score''': 2.2E-05, '''token''': 3_56_76, '''token_str''': ''' Maul'''},
{'''sequence''': '''My name isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is Maul''',
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
},
{'''sequence''': '''The largest city in France isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 2.1E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 2E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
{'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
] , )
__UpperCAmelCase = unmasker('''My name is <mask> <mask>''' , top_k=2 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
[
{
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is Maul<mask></s>''',
},
{'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
],
[
{
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is<mask> Maul</s>''',
},
{'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
],
] , )
@require_torch_gpu
def a ( self : Any ):
__UpperCAmelCase = pipeline('''fill-mask''' , model='''hf-internal-testing/tiny-random-distilbert''' , device=0 , framework='''pt''' )
# convert model to fp16
pipe.model.half()
__UpperCAmelCase = pipe('''Paris is the [MASK] of France.''' )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(_lowercase , _lowercase )
@slow
@require_torch
def a ( self : int ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''pt''' )
self.run_large_test(_lowercase )
@slow
@require_tf
def a ( self : Optional[Any] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''tf''' )
self.run_large_test(_lowercase )
def a ( self : Dict , _lowercase : str ):
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase ) , [
{'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_10, '''token_str''': ''' John'''},
{'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 15_73, '''token_str''': ''' Chris'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase ) , [
{
'''sequence''': '''The largest city in France is Paris''',
'''score''': 0.251,
'''token''': 22_01,
'''token_str''': ''' Paris''',
},
{
'''sequence''': '''The largest city in France is Lyon''',
'''score''': 0.214,
'''token''': 1_27_90,
'''token_str''': ''' Lyon''',
},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 29_41, '''token_str''': ''' Te'''},
] , )
@require_torch
def a ( self : List[Any] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''pt''' )
__UpperCAmelCase = None
__UpperCAmelCase = None
self.run_pipeline_test(_lowercase , [] )
@require_tf
def a ( self : str ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''tf''' )
__UpperCAmelCase = None
__UpperCAmelCase = None
self.run_pipeline_test(_lowercase , [] )
def a ( self : Optional[int] , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : Tuple ):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''' )
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = [
F'''This is another {tokenizer.mask_token} test''',
]
return fill_masker, examples
def a ( self : int , _lowercase : Tuple , _lowercase : Tuple ):
__UpperCAmelCase = fill_masker.tokenizer
__UpperCAmelCase = fill_masker.model
__UpperCAmelCase = fill_masker(
F'''This is a {tokenizer.mask_token}''' , )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = fill_masker([F'''This is a {tokenizer.mask_token}'''] )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.'''] )
self.assertEqual(
_lowercase , [
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
] , )
with self.assertRaises(_lowercase ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(_lowercase ):
fill_masker('''This is''' )
self.run_test_top_k(_lowercase , _lowercase )
self.run_test_targets(_lowercase , _lowercase )
self.run_test_top_k_targets(_lowercase , _lowercase )
self.fill_mask_with_duplicate_targets_and_top_k(_lowercase , _lowercase )
self.fill_mask_with_multiple_masks(_lowercase , _lowercase )
def a ( self : Optional[Any] , _lowercase : Optional[Any] , _lowercase : List[Any] ):
__UpperCAmelCase = tokenizer.get_vocab()
__UpperCAmelCase = sorted(vocab.keys() )[:2]
# Pipeline argument
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase , targets=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , _lowercase )
__UpperCAmelCase = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(_lowercase ) )
# Call argument
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , _lowercase )
__UpperCAmelCase = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(_lowercase ) )
# Score equivalence
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
__UpperCAmelCase = [top_mask['''token_str'''] for top_mask in outputs]
__UpperCAmelCase = [top_mask['''score'''] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_lowercase ) == set(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
__UpperCAmelCase = [top_mask['''score'''] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
# Raises with invalid
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=[''''''] )
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets='''''' )
def a ( self : List[Any] , _lowercase : Tuple , _lowercase : Optional[Any] ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase , top_k=2 )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
def a ( self : Optional[int] , _lowercase : int , _lowercase : Tuple ):
__UpperCAmelCase = tokenizer.get_vocab()
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
# top_k=2, ntargets=3
__UpperCAmelCase = sorted(vocab.keys() )[:3]
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=2 , targets=_lowercase )
# If we use the most probably targets, and filter differently, we should still
# have the same results
__UpperCAmelCase = [el['''token_str'''] for el in sorted(_lowercase , key=lambda _lowercase : x["score"] , reverse=_lowercase )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_lowercase ).issubset(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=3 , targets=_lowercase )
# They should yield exactly the same result
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
def a ( self : Union[str, Any] , _lowercase : Tuple , _lowercase : Union[str, Any] ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = tokenizer.get_vocab()
# String duplicates + id duplicates
__UpperCAmelCase = sorted(vocab.keys() )[:3]
__UpperCAmelCase = [targets[0], targets[1], targets[0], targets[2], targets[1]]
__UpperCAmelCase = fill_masker(F'''My name is {tokenizer.mask_token}''' , targets=_lowercase , top_k=10 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(_lowercase ) , 3 )
def a ( self : Dict , _lowercase : Dict , _lowercase : Any ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(
F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
_lowercase , [
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
] , )
| 368
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 86
| 0
|
"""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 : List[Any] = ['gpt2']
_a : List[str] = 'gpt2'
if is_tf_available():
class __A ( tf.Module ):
def __init__( self , a__ ):
super().__init__()
_lowerCAmelCase : int = tokenizer
_lowerCAmelCase : Tuple = AutoConfig.from_pretrained(a__ )
_lowerCAmelCase : Union[str, Any] = TFGPTaLMHeadModel.from_config(a__ )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) )
def __A ( self , a__ ):
_lowerCAmelCase : Optional[int] = self.tokenizer(a__ )
_lowerCAmelCase : List[str] = tokenized["""input_ids"""].to_tensor()
_lowerCAmelCase : Union[str, Any] = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
_lowerCAmelCase : List[str] = self.model(input_ids=a__ , attention_mask=a__ )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class __A ( unittest.TestCase ):
def __A ( self ):
super().setUp()
_lowerCAmelCase : Dict = [GPTaTokenizer.from_pretrained(a__ ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
_lowerCAmelCase : str = [TFGPTaTokenizer.from_pretrained(a__ ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
_lowerCAmelCase : str = [
"""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""",
]
_lowerCAmelCase : Dict = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __A ( self ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
_lowerCAmelCase : Dict = tokenizer([test_inputs] , return_tensors="""tf""" )
_lowerCAmelCase : Dict = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
_lowerCAmelCase : List[str] = python_outputs[key].numpy()
_lowerCAmelCase : Union[str, Any] = 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 __A ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : Tuple = tf.function(a__ )
for test_inputs in self.test_sentences:
_lowerCAmelCase : Optional[Any] = tf.constant(a__ )
_lowerCAmelCase : Dict = compiled_tokenizer(a__ )
_lowerCAmelCase : Union[str, Any] = tf_tokenizer(a__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __A ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : Tuple = ModelToSave(tokenizer=a__ )
_lowerCAmelCase : Optional[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
_lowerCAmelCase : List[str] = model.serving(a__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_lowerCAmelCase : str = Path(a__ ) / """saved.model"""
tf.saved_model.save(a__ , a__ , signatures={"""serving_default""": model.serving} )
_lowerCAmelCase : Union[str, Any] = tf.saved_model.load(a__ )
_lowerCAmelCase : 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 __A ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : List[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
_lowerCAmelCase : str = tf_tokenizer(a__ ) # Build model with some sample inputs
_lowerCAmelCase : Optional[int] = tf_tokenizer.get_config()
_lowerCAmelCase : List[str] = TFGPTaTokenizer.from_config(a__ )
_lowerCAmelCase : 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 __A ( self ):
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
_lowerCAmelCase : List[str] = 123123
for max_length in [3, 5, 1024]:
_lowerCAmelCase : Optional[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
_lowerCAmelCase : List[str] = tf_tokenizer(a__ , max_length=a__ )
_lowerCAmelCase : Union[str, Any] = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 44
|
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='new-model'
if is_tf_available():
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =NewModelConfig
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = '''bert-base-cased'''
__snake_case : Dict = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : int = TFAutoModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = '''bert-base-cased'''
__snake_case : Optional[Any] = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : List[Any] = TFAutoModelForPreTraining.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : int = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : Dict = TFAutoModelForCausalLM.from_pretrained(a_ )
__snake_case , __snake_case : int = TFAutoModelForCausalLM.from_pretrained(a_ , output_loading_info=a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Tuple = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : Tuple = TFAutoModelWithLMHead.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : List[str] = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : Optional[Any] = TFAutoModelForMaskedLM.from_pretrained(a_ )
__snake_case , __snake_case : int = TFAutoModelForMaskedLM.from_pretrained(a_ , output_loading_info=a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : int = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(a_ )
__snake_case , __snake_case : Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(a_ , output_loading_info=a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
__snake_case : Any = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : Dict = TFAutoModelForSequenceClassification.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
__snake_case : Optional[int] = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : Optional[Any] = TFAutoModelForQuestionAnswering.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
@slow
@require_tensorflow_probability
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
__snake_case : Dict = AutoConfig.from_pretrained(a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
__snake_case : List[str] = TFAutoModelForTableQuestionAnswering.from_pretrained(a_ )
__snake_case , __snake_case : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(
a_ , output_loading_info=a_ )
self.assertIsNotNone(a_ )
self.assertIsInstance(a_ , a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = TFAutoModelWithLMHead.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=a_ ) , 1_44_10 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModelWithLMHead.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=a_ ) , 1_44_10 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Dict = TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny''' )
self.assertIsInstance(a_ , a_ )
__snake_case : Optional[Any] = copy.deepcopy(model.config )
__snake_case : int = ['''FunnelBaseModel''']
__snake_case : Any = TFAutoModel.from_config(a_ )
self.assertIsInstance(a_ , a_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(a_ )
__snake_case : Dict = TFAutoModel.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
try:
AutoConfig.register('''new-model''' , a_ )
__snake_case : List[str] = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(a_ ):
auto_class.register(a_ , a_ )
auto_class.register(a_ , a_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a_ ):
auto_class.register(a_ , a_ )
# Now that the config is registered, it can be used as any other config with the auto-API
__snake_case : Union[str, Any] = BertModelTester(self ).get_config()
__snake_case : str = NewModelConfig(**tiny_config.to_dict() )
__snake_case : Optional[int] = auto_class.from_config(a_ )
self.assertIsInstance(a_ , a_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(a_ )
__snake_case : Optional[int] = auto_class.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
with self.assertRaisesRegex(
a_ , '''bert-base is not a local folder and is not a valid model identifier''' ):
__snake_case : Any = TFAutoModel.from_pretrained('''bert-base''' )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
with self.assertRaisesRegex(
a_ , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__snake_case : Dict = TFAutoModel.from_pretrained(a_ , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
with self.assertRaisesRegex(
a_ , '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin''' , ):
__snake_case : Any = TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
with self.assertRaisesRegex(a_ , '''Use `from_pt=True` to load this model''' ):
__snake_case : Dict = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
__snake_case : List[Any] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
__snake_case : Dict = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
with RequestCounter() as counter:
__snake_case : Dict = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 102
| 0
|
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : bool = False
SCREAMING_SNAKE_CASE_ : float = 3.0
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
self.assertDictEqual(MockClass().to_kwargs() ,{} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() ,{'''a''': 2} )
self.assertDictEqual(MockClass(a=2 ,b=SCREAMING_SNAKE_CASE__ ).to_kwargs() ,{'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 ,c=2.2_5 ).to_kwargs() ,{'''a''': 2, '''c''': 2.2_5} )
@require_cuda
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = GradScalerKwargs(init_scale=10_24 ,growth_factor=2 )
AcceleratorState._reset_state()
__SCREAMING_SNAKE_CASE :int = Accelerator(mixed_precision='''fp16''' ,kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
__SCREAMING_SNAKE_CASE :Union[str, Any] = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale ,1_0_2_4.0 )
self.assertEqual(scaler._growth_factor ,2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor ,0.5 )
self.assertEqual(scaler._growth_interval ,20_00 )
self.assertEqual(scaler._enabled ,SCREAMING_SNAKE_CASE__ )
@require_multi_gpu
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(SCREAMING_SNAKE_CASE__ ,env=os.environ.copy() )
if __name__ == "__main__":
lowerCamelCase_ = DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
lowerCamelCase_ = Accelerator(kwargs_handlers=[ddp_scaler])
lowerCamelCase_ = torch.nn.Linear(1_0_0, 2_0_0)
lowerCamelCase_ = accelerator.prepare(model)
# Check the values changed in kwargs
lowerCamelCase_ = ""
lowerCamelCase_ = model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
if observed_bucket_cap_map != 1_5:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 239
|
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 10 , a_ : int = 22 ) -> int:
__SCREAMING_SNAKE_CASE :Optional[int] = range(1 , a_ )
__SCREAMING_SNAKE_CASE :List[Any] = range(1 , a_ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(f'{solution(1_0, 2_2) = }')
| 239
| 1
|
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''char'''
__snake_case = '''bpe'''
__snake_case = '''wp'''
UpperCAmelCase__ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''char_tokenizer''']
__snake_case = '''ViTImageProcessor'''
__snake_case = '''MgpstrTokenizer'''
def __init__( self : Optional[int] , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Dict=None , **__UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
a = tokenizer
a = AutoTokenizer.from_pretrained('''gpt2''' )
a = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : List[str] ) ->List[Any]:
"""simple docstring"""
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None:
a = self.char_tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
a = encodings['''input_ids''']
return inputs
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[Any] ) ->List[Any]:
"""simple docstring"""
a , a , a = sequences
a = char_preds.size(0 )
a , a = self._decode_helper(__UpperCAmelCase , '''char''' )
a , a = self._decode_helper(__UpperCAmelCase , '''bpe''' )
a , a = self._decode_helper(__UpperCAmelCase , '''wp''' )
a = []
a = []
for i in range(__UpperCAmelCase ):
a = [char_scores[i], bpe_scores[i], wp_scores[i]]
a = [char_strs[i], bpe_strs[i], wp_strs[i]]
a = scores.index(max(__UpperCAmelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
a = {}
a = final_strs
a = final_scores
a = char_strs
a = bpe_strs
a = wp_strs
return out
def __lowerCAmelCase ( self : int , __UpperCAmelCase : List[Any] , __UpperCAmelCase : str ) ->int:
"""simple docstring"""
if format == DecodeType.CHARACTER:
a = self.char_decode
a = 1
a = '''[s]'''
elif format == DecodeType.BPE:
a = self.bpe_decode
a = 2
a = '''#'''
elif format == DecodeType.WORDPIECE:
a = self.wp_decode
a = 102
a = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
a , a = [], []
a = pred_logits.size(0 )
a = pred_logits.size(1 )
a , a = pred_logits.topk(1 , dim=-1 , largest=__UpperCAmelCase , sorted=__UpperCAmelCase )
a = preds_index.view(-1 , __UpperCAmelCase )[:, 1:]
a = decoder(__UpperCAmelCase )
a , a = torch.nn.functional.softmax(__UpperCAmelCase , dim=2 ).max(dim=2 )
a = preds_max_prob[:, 1:]
for index in range(__UpperCAmelCase ):
a = preds_str[index].find(__UpperCAmelCase )
a = preds_str[index][:pred_eos]
a = preds_index[index].cpu().tolist()
a = pred_index.index(__UpperCAmelCase ) if eos_token in pred_index else -1
a = preds_max_prob[index][: pred_eos_index + 1]
a = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__UpperCAmelCase )
conf_scores.append(__UpperCAmelCase )
return dec_strs, conf_scores
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str ) ->Union[str, Any]:
"""simple docstring"""
a = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase )]
return decode_strs
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[Any] ) ->List[Any]:
"""simple docstring"""
return self.bpe_tokenizer.batch_decode(__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : List[Any] ) ->List[Any]:
"""simple docstring"""
a = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase )]
return decode_strs
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ['''input_features''', '''attention_mask''']
def __init__( self : List[Any] , lowerCAmelCase__ : Union[str, Any]=8_0 , lowerCAmelCase__ : Tuple=1_6_0_0_0 , lowerCAmelCase__ : Union[str, Any]=8_0 , lowerCAmelCase__ : List[Any]=0.0 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[Any]=True , **lowerCAmelCase__ : int , ) -> int:
"""simple docstring"""
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCAmelCase : str = num_mel_bins
_UpperCAmelCase : Optional[int] = do_ceptral_normalize
_UpperCAmelCase : List[str] = normalize_means
_UpperCAmelCase : str = normalize_vars
_UpperCAmelCase : Union[str, Any] = True
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : np.ndarray , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Tuple = waveform * (2**1_5) # Kaldi compliance: 16-bit signed integers
_UpperCAmelCase : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).unsqueeze(0 )
_UpperCAmelCase : List[Any] = ta_kaldi.fbank(lowerCAmelCase__ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : float = 0.0 , ) -> np.ndarray:
"""simple docstring"""
if normalize_means:
_UpperCAmelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_UpperCAmelCase : Dict = np.subtract(lowerCAmelCase__ , lowerCAmelCase__ )
if normalize_vars:
_UpperCAmelCase : Any = x[:input_length].std(axis=0 )
_UpperCAmelCase : Optional[int] = np.divide(lowerCAmelCase__ , lowerCAmelCase__ )
if input_length < x.shape[0]:
_UpperCAmelCase : str = padding_value
# make sure array is in float32
_UpperCAmelCase : Union[str, Any] = x.astype(np.floataa )
return x
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : List[np.ndarray] , lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
"""simple docstring"""
_UpperCAmelCase : Tuple = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(lowerCAmelCase__ , lowerCAmelCase__ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(lowerCAmelCase__ , lowerCAmelCase__ )
]
def __call__( self : List[Any] , lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[Union[str, TensorType]] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[bool] = None , **lowerCAmelCase__ : Optional[Any] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(lowerCAmelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : List[Any] = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : Any = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
_UpperCAmelCase : Dict = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[Any] = [raw_speech]
# extract fbank features
_UpperCAmelCase : Tuple = [self._extract_fbank_features(lowerCAmelCase__ ) for waveform in raw_speech]
# convert into correct format for padding
_UpperCAmelCase : Optional[Any] = BatchFeature({"input_features": features} )
_UpperCAmelCase : Optional[Any] = self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
# make sure list is in array format
_UpperCAmelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] , lowerCAmelCase__ ):
_UpperCAmelCase : int = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for feature in input_features]
_UpperCAmelCase : Optional[int] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_UpperCAmelCase : Dict = [np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
_UpperCAmelCase : List[str] = (
np.array(lowerCAmelCase__ , dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_UpperCAmelCase : str = self.normalize(
padded_inputs["input_features"] , attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
_UpperCAmelCase : Any = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 145
| 0
|
import sys
import turtle
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
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(_lowerCamelCase , get_mid(_lowerCamelCase , _lowerCamelCase ) , get_mid(_lowerCamelCase , _lowerCamelCase ) , depth - 1 )
triangle(_lowerCamelCase , get_mid(_lowerCamelCase , _lowerCamelCase ) , get_mid(_lowerCamelCase , _lowerCamelCase ) , depth - 1 )
triangle(_lowerCamelCase , get_mid(_lowerCamelCase , _lowerCamelCase ) , get_mid(_lowerCamelCase , _lowerCamelCase ) , 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>"
)
_snake_case = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("red")
_snake_case = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 300
|
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig):
lowerCamelCase__ = None
class UpperCAmelCase_ ( datasets.ArrowBasedBuilder):
lowerCamelCase__ = PandasConfig
def snake_case__ ( self):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features)
def snake_case__ ( self, __a):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
_lowerCAmelCase : str = dl_manager.download_and_extract(self.config.data_files)
if isinstance(__a, (str, list, tuple)):
_lowerCAmelCase : str = data_files
if isinstance(__a, __a):
_lowerCAmelCase : int = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_lowerCAmelCase : Union[str, Any] = [dl_manager.iter_files(__a) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files})]
_lowerCAmelCase : str = []
for split_name, files in data_files.items():
if isinstance(__a, __a):
_lowerCAmelCase : Optional[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_lowerCAmelCase : str = [dl_manager.iter_files(__a) for file in files]
splits.append(datasets.SplitGenerator(name=__a, gen_kwargs={"files": files}))
return splits
def snake_case__ ( self, __a):
'''simple docstring'''
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
_lowerCAmelCase : str = table_cast(__a, self.config.features.arrow_schema)
return pa_table
def snake_case__ ( self, __a):
'''simple docstring'''
for i, file in enumerate(itertools.chain.from_iterable(__a)):
with open(__a, "rb") as f:
_lowerCAmelCase : Optional[Any] = pa.Table.from_pandas(pd.read_pickle(__a))
yield i, self._cast_table(__a)
| 300
| 1
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class lowercase__ ( lowercase ):
lowercase__ = 42
lowercase__ = 42
class lowercase__ ( nn.Module ):
lowercase__ = 42
lowercase__ = (16, 32, 96, 2_56)
lowercase__ = jnp.floataa
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : List[str] = nn.Conv(
self.block_out_channels[0] ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
_UpperCamelCase : List[Any] = []
for i in range(len(self.block_out_channels ) - 1 ):
_UpperCamelCase : Optional[int] = self.block_out_channels[i]
_UpperCamelCase : List[Any] = self.block_out_channels[i + 1]
_UpperCamelCase : int = nn.Conv(
lowerCamelCase__ ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = nn.Conv(
lowerCamelCase__ ,kernel_size=(3, 3) ,strides=(2, 2) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(lowerCamelCase__ )
_UpperCamelCase : Any = blocks
_UpperCamelCase : List[str] = nn.Conv(
self.conditioning_embedding_channels ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self : List[Any] ,lowerCamelCase__ : Tuple ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = self.conv_in(lowerCamelCase__ )
_UpperCamelCase : List[Any] = nn.silu(lowerCamelCase__ )
for block in self.blocks:
_UpperCamelCase : List[str] = block(lowerCamelCase__ )
_UpperCamelCase : Dict = nn.silu(lowerCamelCase__ )
_UpperCamelCase : List[str] = self.conv_out(lowerCamelCase__ )
return embedding
@flax_register_to_config
class lowercase__ ( nn.Module , lowercase , lowercase ):
lowercase__ = 32
lowercase__ = 4
lowercase__ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
lowercase__ = False
lowercase__ = (3_20, 6_40, 12_80, 12_80)
lowercase__ = 2
lowercase__ = 8
lowercase__ = None
lowercase__ = 12_80
lowercase__ = 0.0
lowercase__ = False
lowercase__ = jnp.floataa
lowercase__ = True
lowercase__ = 0
lowercase__ = "rgb"
lowercase__ = (16, 32, 96, 2_56)
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : jax.random.KeyArray ):
'''simple docstring'''
# init input tensors
_UpperCamelCase : Optional[Any] = (1, self.in_channels, self.sample_size, self.sample_size)
_UpperCamelCase : Tuple = jnp.zeros(lowerCamelCase__ ,dtype=jnp.floataa )
_UpperCamelCase : Optional[int] = jnp.ones((1,) ,dtype=jnp.intaa )
_UpperCamelCase : Any = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa )
_UpperCamelCase : Tuple = (1, 3, self.sample_size * 8, self.sample_size * 8)
_UpperCamelCase : Union[str, Any] = jnp.zeros(lowerCamelCase__ ,dtype=jnp.floataa )
_UpperCamelCase , _UpperCamelCase : Optional[int] = jax.random.split(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = {'params': params_rng, 'dropout': dropout_rng}
return self.init(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )["params"]
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.block_out_channels
_UpperCamelCase : Optional[int] = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_UpperCamelCase : Dict = self.num_attention_heads or self.attention_head_dim
# input
_UpperCamelCase : Optional[int] = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
_UpperCamelCase : Union[str, Any] = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift )
_UpperCamelCase : List[Any] = FlaxTimestepEmbedding(lowerCamelCase__ ,dtype=self.dtype )
_UpperCamelCase : str = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] ,block_out_channels=self.conditioning_embedding_out_channels ,)
_UpperCamelCase : Tuple = self.only_cross_attention
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : int = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : List[str] = (num_attention_heads,) * len(self.down_block_types )
# down
_UpperCamelCase : List[Any] = []
_UpperCamelCase : Union[str, Any] = []
_UpperCamelCase : Dict = block_out_channels[0]
_UpperCamelCase : int = nn.Conv(
lowerCamelCase__ ,kernel_size=(1, 1) ,padding='VALID' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(lowerCamelCase__ )
for i, down_block_type in enumerate(self.down_block_types ):
_UpperCamelCase : Dict = output_channel
_UpperCamelCase : List[Any] = block_out_channels[i]
_UpperCamelCase : Any = i == len(lowerCamelCase__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_UpperCamelCase : Any = FlaxCrossAttnDownBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,num_attention_heads=num_attention_heads[i] ,add_downsample=not is_final_block ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,dtype=self.dtype ,)
else:
_UpperCamelCase : Dict = FlaxDownBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(lowerCamelCase__ )
for _ in range(self.layers_per_block ):
_UpperCamelCase : Any = nn.Conv(
lowerCamelCase__ ,kernel_size=(1, 1) ,padding='VALID' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(lowerCamelCase__ )
if not is_final_block:
_UpperCamelCase : List[Any] = nn.Conv(
lowerCamelCase__ ,kernel_size=(1, 1) ,padding='VALID' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = down_blocks
_UpperCamelCase : List[str] = controlnet_down_blocks
# mid
_UpperCamelCase : Any = block_out_channels[-1]
_UpperCamelCase : Tuple = FlaxUNetMidBlockaDCrossAttn(
in_channels=lowerCamelCase__ ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,dtype=self.dtype ,)
_UpperCamelCase : Any = nn.Conv(
lowerCamelCase__ ,kernel_size=(1, 1) ,padding='VALID' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self : int ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : int ,lowerCamelCase__ : str ,lowerCamelCase__ : float = 1.0 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = False ,):
'''simple docstring'''
_UpperCamelCase : Any = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_UpperCamelCase : Optional[Any] = jnp.flip(lowerCamelCase__ ,axis=1 )
# 1. time
if not isinstance(lowerCamelCase__ ,jnp.ndarray ):
_UpperCamelCase : Tuple = jnp.array([timesteps] ,dtype=jnp.intaa )
elif isinstance(lowerCamelCase__ ,jnp.ndarray ) and len(timesteps.shape ) == 0:
_UpperCamelCase : Optional[Any] = timesteps.astype(dtype=jnp.floataa )
_UpperCamelCase : List[str] = jnp.expand_dims(lowerCamelCase__ ,0 )
_UpperCamelCase : Any = self.time_proj(lowerCamelCase__ )
_UpperCamelCase : Dict = self.time_embedding(lowerCamelCase__ )
# 2. pre-process
_UpperCamelCase : List[Any] = jnp.transpose(lowerCamelCase__ ,(0, 2, 3, 1) )
_UpperCamelCase : Optional[int] = self.conv_in(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = jnp.transpose(lowerCamelCase__ ,(0, 2, 3, 1) )
_UpperCamelCase : Tuple = self.controlnet_cond_embedding(lowerCamelCase__ )
sample += controlnet_cond
# 3. down
_UpperCamelCase : Any = (sample,)
for down_block in self.down_blocks:
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase , _UpperCamelCase : Union[str, Any] = down_block(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
else:
_UpperCamelCase , _UpperCamelCase : str = down_block(lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
_UpperCamelCase : Optional[int] = self.mid_block(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
# 5. contronet blocks
_UpperCamelCase : Union[str, Any] = ()
for down_block_res_sample, controlnet_block in zip(lowerCamelCase__ ,self.controlnet_down_blocks ):
_UpperCamelCase : List[str] = controlnet_block(lowerCamelCase__ )
controlnet_down_block_res_samples += (down_block_res_sample,)
_UpperCamelCase : Optional[int] = controlnet_down_block_res_samples
_UpperCamelCase : Tuple = self.controlnet_mid_block(lowerCamelCase__ )
# 6. scaling
_UpperCamelCase : Dict = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=lowerCamelCase__ ,mid_block_res_sample=lowerCamelCase__ )
| 83
|
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
UpperCamelCase : List[Any] = False
for divisor in range(2 , int(round(sqrt(_lowerCAmelCase ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
UpperCamelCase : Union[str, Any] = False
break
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'status' must been from type bool"
return status
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
UpperCamelCase : int = list(range(2 , n + 1 ) )
UpperCamelCase : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_lowerCAmelCase ) ):
for j in range(i + 1 , len(_lowerCAmelCase ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
UpperCamelCase : Tuple = 0
# filters actual prime numbers.
UpperCamelCase : str = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Optional[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
UpperCamelCase : str = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(_lowerCAmelCase ):
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and number >= 0, "'number' must been an int and >= 0"
UpperCamelCase : Optional[Any] = [] # this list will be returns of the function.
# potential prime number factors.
UpperCamelCase : Tuple = 2
UpperCamelCase : str = number
if number == 0 or number == 1:
ans.append(_lowerCAmelCase )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_lowerCAmelCase ):
while quotient != 1:
if is_prime(_lowerCAmelCase ) and (quotient % factor == 0):
ans.append(_lowerCAmelCase )
quotient /= factor
else:
factor += 1
else:
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase ) -> Any:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
UpperCamelCase : List[Any] = 0
# prime factorization of 'number'
UpperCamelCase : Any = prime_factorization(_lowerCAmelCase )
UpperCamelCase : List[Any] = max(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase ) -> Union[str, Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
UpperCamelCase : List[Any] = 0
# prime factorization of 'number'
UpperCamelCase : Dict = prime_factorization(_lowerCAmelCase )
UpperCamelCase : List[Any] = min(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase ) -> Optional[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 == 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 == 0
def A_ ( _lowerCAmelCase ) -> List[Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 != 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 != 0
def A_ ( _lowerCAmelCase ) -> Any:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (number > 2) and is_even(_lowerCAmelCase )
), "'number' must been an int, even and > 2"
UpperCamelCase : List[str] = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
UpperCamelCase : Dict = get_prime_numbers(_lowerCAmelCase )
UpperCamelCase : Tuple = len(_lowerCAmelCase )
# run variable for while-loops.
UpperCamelCase : Optional[int] = 0
UpperCamelCase : int = None
# exit variable. for break up the loops
UpperCamelCase : Union[str, Any] = True
while i < len_pn and loop:
UpperCamelCase : Tuple = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
UpperCamelCase : Any = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (len(_lowerCAmelCase ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
UpperCamelCase : Tuple = 0
while numbera != 0:
UpperCamelCase : Tuple = numbera % numbera
UpperCamelCase : Any = numbera
UpperCamelCase : Union[str, Any] = rest
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
UpperCamelCase : Optional[int] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
UpperCamelCase : List[Any] = prime_factorization(_lowerCAmelCase )
UpperCamelCase : Union[str, Any] = prime_factorization(_lowerCAmelCase )
elif numbera == 1 or numbera == 1:
UpperCamelCase : Optional[Any] = []
UpperCamelCase : int = []
UpperCamelCase : List[Any] = max(_lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase : Optional[int] = 0
UpperCamelCase : Tuple = 0
UpperCamelCase : List[str] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
UpperCamelCase : str = prime_fac_a.count(_lowerCAmelCase )
UpperCamelCase : Tuple = prime_fac_a.count(_lowerCAmelCase )
for _ in range(max(_lowerCAmelCase , _lowerCAmelCase ) ):
ans *= n
else:
UpperCamelCase : str = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
UpperCamelCase : Any = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def A_ ( _lowerCAmelCase ) -> Tuple:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'number' must been a positive int"
UpperCamelCase : int = 0
UpperCamelCase : int = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_lowerCAmelCase ):
ans += 1
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and is_prime(
_lowerCAmelCase ), "'ans' must been a prime number and from type int"
return ans
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
assert (
is_prime(_lowerCAmelCase ) and is_prime(_lowerCAmelCase ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
UpperCamelCase : str = p_number_a + 1 # jump to the next number
UpperCamelCase : Dict = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
while number < p_number_a:
ans.append(_lowerCAmelCase )
number += 1
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and ans[0] != p_number_a
and ans[len(_lowerCAmelCase ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def A_ ( _lowerCAmelCase ) -> List[str]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 1), "'n' must been int and >= 1"
UpperCamelCase : Dict = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(_lowerCAmelCase )
# precondition
assert ans[0] == 1 and ans[len(_lowerCAmelCase ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def A_ ( _lowerCAmelCase ) -> int:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number > 1
), "'number' must been an int and >= 1"
UpperCamelCase : int = get_divisors(_lowerCAmelCase )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (divisors[0] == 1)
and (divisors[len(_lowerCAmelCase ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
UpperCamelCase : List[str] = gcd(abs(_lowerCAmelCase ) , abs(_lowerCAmelCase ) )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def A_ ( _lowerCAmelCase ) -> Dict:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been a int and >= 0"
UpperCamelCase : str = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def A_ ( _lowerCAmelCase ) -> Tuple:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been an int and >= 0"
UpperCamelCase : Dict = 0
UpperCamelCase : Dict = 1
UpperCamelCase : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
UpperCamelCase : Any = ans
ans += fiba
UpperCamelCase : str = tmp
return ans
| 52
| 0
|
"""simple docstring"""
from __future__ import annotations
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : Dict = list(range(len(A__ ) ) )
UpperCAmelCase_ : List[Any] = [v / w for v, w in zip(A__ ,A__ )]
index.sort(key=lambda A__ : ratio[i] ,reverse=A__ )
UpperCAmelCase_ : float = 0
UpperCAmelCase_ : list[float] = [0] * len(A__ )
for i in index:
if weight[i] <= capacity:
UpperCAmelCase_ : Union[str, Any] = 1
max_value += value[i]
capacity -= weight[i]
else:
UpperCAmelCase_ : List[Any] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361
|
"""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
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''microsoft/table-transformer-detection''': (
'''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
),
}
class UpperCamelCase_ (__A ):
__magic_name__ = '''table-transformer'''
__magic_name__ = ['''past_key_values''']
__magic_name__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : Optional[Any]=100 , lowerCAmelCase_ : Optional[int]=6 , lowerCAmelCase_ : List[Any]=2_048 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Dict=6 , lowerCAmelCase_ : List[Any]=2_048 , lowerCAmelCase_ : Optional[int]=8 , lowerCAmelCase_ : Tuple=0.0 , lowerCAmelCase_ : str=0.0 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[int]="relu" , lowerCAmelCase_ : List[Any]=256 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Optional[int]=0.0 , lowerCAmelCase_ : Union[str, Any]=0.0 , lowerCAmelCase_ : Dict=0.0_2 , lowerCAmelCase_ : Any=1.0 , lowerCAmelCase_ : Any=False , lowerCAmelCase_ : Dict="sine" , lowerCAmelCase_ : Optional[Any]="resnet50" , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : int=5 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : Optional[Any]=0.1 , **lowerCAmelCase_ : Dict , ) -> Union[str, Any]:
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_ : Union[str, Any] = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ : Dict = backbone_config.get("model_type" )
UpperCAmelCase_ : str = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase_ : Any = config_class.from_dict(lowerCAmelCase_ )
# set timm attributes to None
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = None, None, None
UpperCAmelCase_ : int = use_timm_backbone
UpperCAmelCase_ : int = backbone_config
UpperCAmelCase_ : Dict = num_channels
UpperCAmelCase_ : Optional[Any] = num_queries
UpperCAmelCase_ : List[str] = d_model
UpperCAmelCase_ : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase_ : Optional[Any] = encoder_layers
UpperCAmelCase_ : List[str] = encoder_attention_heads
UpperCAmelCase_ : int = decoder_ffn_dim
UpperCAmelCase_ : int = decoder_layers
UpperCAmelCase_ : Optional[int] = decoder_attention_heads
UpperCAmelCase_ : List[str] = dropout
UpperCAmelCase_ : Dict = attention_dropout
UpperCAmelCase_ : Union[str, Any] = activation_dropout
UpperCAmelCase_ : Optional[int] = activation_function
UpperCAmelCase_ : int = init_std
UpperCAmelCase_ : Any = init_xavier_std
UpperCAmelCase_ : Union[str, Any] = encoder_layerdrop
UpperCAmelCase_ : Dict = decoder_layerdrop
UpperCAmelCase_ : Union[str, Any] = encoder_layers
UpperCAmelCase_ : Any = auxiliary_loss
UpperCAmelCase_ : List[str] = position_embedding_type
UpperCAmelCase_ : Dict = backbone
UpperCAmelCase_ : Optional[int] = use_pretrained_backbone
UpperCAmelCase_ : Tuple = dilation
# Hungarian matcher
UpperCAmelCase_ : Optional[Any] = class_cost
UpperCAmelCase_ : List[Any] = bbox_cost
UpperCAmelCase_ : Optional[int] = giou_cost
# Loss coefficients
UpperCAmelCase_ : Optional[int] = mask_loss_coefficient
UpperCAmelCase_ : List[str] = dice_loss_coefficient
UpperCAmelCase_ : Union[str, Any] = bbox_loss_coefficient
UpperCAmelCase_ : Union[str, Any] = giou_loss_coefficient
UpperCAmelCase_ : Dict = eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
return self.d_model
class UpperCamelCase_ (__A ):
__magic_name__ = version.parse('''1.11''' )
@property
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> float:
return 1e-5
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return 12
| 253
| 0
|
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __lowerCAmelCase ( a__ ) -> Dict:
return 1 / (1 + np.exp(-z ))
def __lowerCAmelCase ( a__ , a__ ) -> Tuple:
return (-y * np.log(a__ ) - (1 - y) * np.log(1 - h )).mean()
def __lowerCAmelCase ( a__ , a__ , a__ ) -> List[Any]:
__a = np.dot(a__ , a__ )
return np.sum(y * scores - np.log(1 + np.exp(a__ ) ) )
def __lowerCAmelCase ( a__ , a__ , a__ , a__=7_0000 ) -> Tuple:
__a = np.zeros(x.shape[1] )
for iterations in range(a__ ):
__a = np.dot(a__ , a__ )
__a = sigmoid_function(a__ )
__a = np.dot(x.T , h - y ) / y.size
__a = theta - alpha * gradient # updating the weights
__a = np.dot(a__ , a__ )
__a = sigmoid_function(a__ )
__a = cost_function(a__ , a__ )
if iterations % 100 == 0:
print(F"""loss: {j} \t""" ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
A : List[Any] = datasets.load_iris()
A : Any = iris.data[:, :2]
A : int = (iris.target != 0) * 1
A : Dict = 0.1
A : str = logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
print('theta: ', theta) # printing the theta i.e our weights vector
def __lowerCAmelCase ( a__ ) -> Union[str, Any]:
return sigmoid_function(
np.dot(a__ , a__ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(1_0, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
((A) , (A)) : Tuple = (x[:, 0].min(), x[:, 0].max())
((A) , (A)) : int = (x[:, 1].min(), x[:, 1].max())
((A) , (A)) : List[str] = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
A : int = np.c_[xxa.ravel(), xxa.ravel()]
A : Any = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')
plt.legend()
plt.show()
| 6
|
def __lowerCAmelCase ( a__ , a__ ) -> float:
def get_matched_characters(a__ , a__ ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__a = int(max(0 , i - limit ) )
__a = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(a__ )
__a = F"""{_stra[0:_stra.index(a__ )]} {_stra[_stra.index(a__ ) + 1:]}"""
return "".join(a__ )
# matching characters
__a = get_matched_characters(a__ , a__ )
__a = get_matched_characters(a__ , a__ )
__a = len(a__ )
# transposition
__a = (
len([(ca, ca) for ca, ca in zip(a__ , a__ ) if ca != ca] ) // 2
)
if not match_count:
__a = 0.0
else:
__a = (
1
/ 3
* (
match_count / len(a__ )
+ match_count / len(a__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__a = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 6
| 1
|
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : int = [0] * no_of_processes
__lowerCAmelCase : str = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(__snake_case ):
__lowerCAmelCase : List[Any] = burst_time[i]
__lowerCAmelCase : Union[str, Any] = 0
__lowerCAmelCase : str = 0
__lowerCAmelCase : Optional[int] = 9_9999_9999
__lowerCAmelCase : Optional[Any] = 0
__lowerCAmelCase : List[str] = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(__snake_case ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
__lowerCAmelCase : List[str] = remaining_time[j]
__lowerCAmelCase : int = j
__lowerCAmelCase : Optional[int] = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
__lowerCAmelCase : Tuple = remaining_time[short]
if minm == 0:
__lowerCAmelCase : Any = 9_9999_9999
if remaining_time[short] == 0:
complete += 1
__lowerCAmelCase : str = False
# Find finish time of current process
__lowerCAmelCase : Union[str, Any] = increment_time + 1
# Calculate waiting time
__lowerCAmelCase : Optional[Any] = finish_time - arrival_time[short]
__lowerCAmelCase : Optional[Any] = finar - burst_time[short]
if waiting_time[short] < 0:
__lowerCAmelCase : Any = 0
# Increment time
increment_time += 1
return waiting_time
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Dict = [0] * no_of_processes
for i in range(__snake_case ):
__lowerCAmelCase : Optional[Any] = burst_time[i] + waiting_time[i]
return turn_around_time
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Any = 0
__lowerCAmelCase : List[str] = 0
for i in range(__snake_case ):
__lowerCAmelCase : int = total_waiting_time + waiting_time[i]
__lowerCAmelCase : str = total_turn_around_time + turn_around_time[i]
print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}" )
print('Average turn around time =' , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("""Enter how many process you want to analyze""")
lowerCamelCase__ = int(input())
lowerCamelCase__ = [0] * no_of_processes
lowerCamelCase__ = [0] * no_of_processes
lowerCamelCase__ = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
lowerCamelCase__ , lowerCamelCase__ = map(int, input().split())
lowerCamelCase__ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
lowerCamelCase__ = burst_time
lowerCamelCase__ = no_of_processes
lowerCamelCase__ = waiting_time
lowerCamelCase__ = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
lowerCamelCase__ = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"""Process""",
"""BurstTime""",
"""ArrivalTime""",
"""WaitingTime""",
"""TurnAroundTime""",
],
)
# Printing the dataFrame
pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
print(fcfs)
| 362
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class A__ ( _lowerCamelCase):
A_ : str = 'nllb-moe'
A_ : Optional[Any] = ['past_key_values']
A_ : List[str] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , _SCREAMING_SNAKE_CASE=12_81_12 , _SCREAMING_SNAKE_CASE=10_24 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=40_96 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=40_96 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=0.05 , _SCREAMING_SNAKE_CASE=0.05 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=10_24 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="float32" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1_28 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=0.001 , _SCREAMING_SNAKE_CASE=0.001 , _SCREAMING_SNAKE_CASE="all" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=0.2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : int = vocab_size
__lowerCAmelCase : str = max_position_embeddings
__lowerCAmelCase : Dict = d_model
__lowerCAmelCase : Tuple = encoder_ffn_dim
__lowerCAmelCase : Optional[Any] = encoder_layers
__lowerCAmelCase : Any = encoder_attention_heads
__lowerCAmelCase : Tuple = decoder_ffn_dim
__lowerCAmelCase : Dict = decoder_layers
__lowerCAmelCase : str = decoder_attention_heads
__lowerCAmelCase : str = dropout
__lowerCAmelCase : List[str] = attention_dropout
__lowerCAmelCase : Optional[int] = activation_dropout
__lowerCAmelCase : List[Any] = activation_function
__lowerCAmelCase : List[str] = init_std
__lowerCAmelCase : Union[str, Any] = encoder_layerdrop
__lowerCAmelCase : List[Any] = decoder_layerdrop
__lowerCAmelCase : Optional[int] = use_cache
__lowerCAmelCase : Optional[Any] = encoder_layers
__lowerCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCAmelCase : Union[str, Any] = router_z_loss_coef
__lowerCAmelCase : Optional[Any] = router_aux_loss_coef
__lowerCAmelCase : int = decoder_sparse_step
__lowerCAmelCase : str = encoder_sparse_step
__lowerCAmelCase : Tuple = num_experts
__lowerCAmelCase : Dict = expert_capacity
__lowerCAmelCase : Union[str, Any] = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
__lowerCAmelCase : Union[str, Any] = router_dtype
__lowerCAmelCase : Any = router_ignore_padding_tokens
__lowerCAmelCase : str = batch_prioritized_routing
__lowerCAmelCase : Tuple = second_expert_policy
__lowerCAmelCase : List[str] = normalize_router_prob_before_dropping
__lowerCAmelCase : Dict = moe_eval_capacity_token_fraction
__lowerCAmelCase : Union[str, Any] = moe_token_dropout
__lowerCAmelCase : List[Any] = output_router_logits
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , is_encoder_decoder=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
| 182
| 0
|
'''simple docstring'''
import qiskit
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> qiskit.result.counts.Counts:
'''simple docstring'''
snake_case_ = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
snake_case_ = qiskit.QuantumCircuit(__UpperCAmelCase, __UpperCAmelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0], [0] )
# Execute the circuit on the simulator
snake_case_ = qiskit.execute(__UpperCAmelCase, __UpperCAmelCase, shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__UpperCAmelCase )
if __name__ == "__main__":
print(f'''Total count for various states are: {single_qubit_measure(1, 1)}''')
| 56
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = 1 / 100
a : str = ''
a : Any = ''
a : Optional[int] = ''
a : List[str] = 250
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dataset(__UpperCAmelCase, __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
snake_case_ = random.sample(range(len(__UpperCAmelCase ) ), 4 )
snake_case_ ,snake_case_ ,snake_case_ = update_image_and_anno(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, filter_scale=__UpperCAmelCase, )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = path.split(os.sep )[-1].rsplit('''.''', 1 )[0]
snake_case_ = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg", __UpperCAmelCase, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case_ = []
for anno in new_annos:
snake_case_ = anno[3] - anno[1]
snake_case_ = anno[4] - anno[2]
snake_case_ = anno[1] + width / 2
snake_case_ = anno[2] + height / 2
snake_case_ = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(F"{file_root}.txt", '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple[list, list]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase, '''*.txt''' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('''.''', 1 )[0]
with open(__UpperCAmelCase ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(__UpperCAmelCase, F"{label_name}.jpg" )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('''\n''' ).split(''' ''' )
snake_case_ = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, ) -> tuple[list, list, str]:
'''simple docstring'''
snake_case_ = np.zeros([output_size[0], output_size[1], 3], dtype=np.uinta )
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = int(scale_x * output_size[1] )
snake_case_ = int(scale_y * output_size[0] )
snake_case_ = []
snake_case_ = []
for i, index in enumerate(__UpperCAmelCase ):
snake_case_ = all_img_list[index]
path_list.append(__UpperCAmelCase )
snake_case_ = all_annos[index]
snake_case_ = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = bbox[2] * scale_y
snake_case_ = bbox[3] * scale_x
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ = cva.resize(__UpperCAmelCase, (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = bbox[2] * scale_y
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = bbox[3] * scale_x
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ = cva.resize(
__UpperCAmelCase, (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 56
| 1
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
lowercase__ : int = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase__ : Union[str, Any] = 256
class a__ ( UpperCamelCase__ ):
a : Any = ["""melgan"""]
def __init__( self , A , A , A , A , A , ) -> None:
'''simple docstring'''
super().__init__()
# From MELGAN
a = math.log(1e-5 ) # Matches MelGAN training.
a = 4.0 # Largest value for most examples
a = 128
self.register_modules(
notes_encoder=A , continuous_encoder=A , decoder=A , scheduler=A , melgan=A , )
def lowerCAmelCase_ ( self , A , A=(-1.0, 1.0) , A=False ) -> Optional[Any]:
'''simple docstring'''
a , a = output_range
if clip:
a = torch.clip(A , self.min_value , self.max_value )
# Scale to [0, 1].
a = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def lowerCAmelCase_ ( self , A , A=(-1.0, 1.0) , A=False ) -> Optional[Any]:
'''simple docstring'''
a , a = input_range
a = torch.clip(A , A , A ) if clip else outputs
# Scale to [0, 1].
a = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def lowerCAmelCase_ ( self , A , A , A ) -> Union[str, Any]:
'''simple docstring'''
a = input_tokens > 0
a , a = self.notes_encoder(
encoder_input_tokens=A , encoder_inputs_mask=A )
a , a = self.continuous_encoder(
encoder_inputs=A , encoder_inputs_mask=A )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def lowerCAmelCase_ ( self , A , A , A ) -> Optional[Any]:
'''simple docstring'''
a = noise_time
if not torch.is_tensor(A ):
a = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
a = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
a = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
a = self.decoder(
encodings_and_masks=A , decoder_input_tokens=A , decoder_noise_time=A )
return logits
@torch.no_grad()
def __call__( self , A , A = None , A = 100 , A = True , A = "numpy" , A = None , A = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
'''simple docstring'''
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(A , A ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(A )}.''' )
a = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
a = np.zeros([1, 0, self.n_dims] , np.floataa )
a = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=A , device=self.device )
for i, encoder_input_tokens in enumerate(A ):
if i == 0:
a = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
a = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=A , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
a = ones
a = self.scale_features(
A , output_range=[-1.0, 1.0] , clip=A )
a = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=A , continuous_mask=A , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
a = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=A , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(A )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.decode(
encodings_and_masks=A , input_tokens=A , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
a = self.scheduler.step(A , A , A , generator=A ).prev_sample
a = self.scale_to_features(A , input_range=[-1.0, 1.0] )
a = mel[:1]
a = mel.cpu().float().numpy()
a = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A , A )
logger.info("Generated segment" , A )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'." )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'." )
if output_type == "numpy":
a = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
a = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=A )
| 180
|
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
a = 0
a = number
while duplicate > 0:
a , a = divmod(__UpperCamelCase , 10)
fact_sum += factorial(__UpperCamelCase)
return fact_sum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
lowercase__ : str = int(input("Enter number: ").strip())
print(
F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
)
| 180
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
a__ : Optional[int] = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : Dict , *UpperCAmelCase__ : Dict , **UpperCAmelCase__ : Tuple ) -> None:
warnings.warn(
"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use BeitImageProcessor instead." , UpperCAmelCase__ , )
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
| 54
|
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a__ : Any = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
warnings.warn(lowerCAmelCase_ , lowerCAmelCase_ )
requires_backends(lowerCAmelCase_ , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
warnings.warn(lowerCAmelCase_ , lowerCAmelCase_ )
requires_backends(lowerCAmelCase_ , "sklearn" )
__SCREAMING_SNAKE_CASE = simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = fa_score(y_true=lowerCAmelCase_ , y_pred=lowerCAmelCase_ )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
warnings.warn(lowerCAmelCase_ , lowerCAmelCase_ )
requires_backends(lowerCAmelCase_ , "sklearn" )
__SCREAMING_SNAKE_CASE = pearsonr(lowerCAmelCase_ , lowerCAmelCase_ )[0]
__SCREAMING_SNAKE_CASE = spearmanr(lowerCAmelCase_ , lowerCAmelCase_ )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
warnings.warn(lowerCAmelCase_ , lowerCAmelCase_ )
requires_backends(lowerCAmelCase_ , "sklearn" )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_ )} and {len(lowerCAmelCase_ )}"""
if task_name == "cola":
return {"mcc": matthews_corrcoef(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "mrpc":
return acc_and_fa(lowerCAmelCase_ , lowerCAmelCase_ )
elif task_name == "sts-b":
return pearson_and_spearman(lowerCAmelCase_ , lowerCAmelCase_ )
elif task_name == "qqp":
return acc_and_fa(lowerCAmelCase_ , lowerCAmelCase_ )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "qnli":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "rte":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "wnli":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
elif task_name == "hans":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
else:
raise KeyError(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
warnings.warn(lowerCAmelCase_ , lowerCAmelCase_ )
requires_backends(lowerCAmelCase_ , "sklearn" )
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_ )} and {len(lowerCAmelCase_ )}""" )
if task_name == "xnli":
return {"acc": simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )}
else:
raise KeyError(lowerCAmelCase_ )
| 54
| 1
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
lowerCAmelCase : Optional[Any] = {
'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
'tokenizer_config_file': {
'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
},
}
lowerCAmelCase : Any = {'facebook/blenderbot-3B': 128}
class __lowercase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_UpperCAmelCase : Optional[int] = VOCAB_FILES_NAMES
_UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : Union[str, Any] = ['''input_ids''', '''attention_mask''']
_UpperCAmelCase : List[Any] = BlenderbotTokenizer
def __init__( self : List[str] , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : str=None , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Tuple="replace" , lowerCAmelCase__ : List[Any]="<s>" , lowerCAmelCase__ : Union[str, Any]="</s>" , lowerCAmelCase__ : Optional[Any]="</s>" , lowerCAmelCase__ : int="<s>" , lowerCAmelCase__ : str="<unk>" , lowerCAmelCase__ : int="<pad>" , lowerCAmelCase__ : Dict="<mask>" , lowerCAmelCase__ : int=False , lowerCAmelCase__ : List[Any]=True , **lowerCAmelCase__ : Optional[int] , ):
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , errors=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ , **lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE_: List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , lowerCAmelCase__) != add_prefix_space:
SCREAMING_SNAKE_CASE_: Tuple = getattr(lowerCAmelCase__ , pre_tok_state.pop("type"))
SCREAMING_SNAKE_CASE_: Optional[int] = add_prefix_space
SCREAMING_SNAKE_CASE_: Tuple = pre_tok_class(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = add_prefix_space
SCREAMING_SNAKE_CASE_: Dict = 'post_processor'
SCREAMING_SNAKE_CASE_: Dict = getattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__)
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE_: int = json.loads(tokenizer_component_instance.__getstate__())
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE_: List[Any] = tuple(state["sep"])
if "cls" in state:
SCREAMING_SNAKE_CASE_: Optional[int] = tuple(state["cls"])
SCREAMING_SNAKE_CASE_: Union[str, Any] = False
if state.get("add_prefix_space" , lowerCAmelCase__) != add_prefix_space:
SCREAMING_SNAKE_CASE_: Any = add_prefix_space
SCREAMING_SNAKE_CASE_: Tuple = True
if state.get("trim_offsets" , lowerCAmelCase__) != trim_offsets:
SCREAMING_SNAKE_CASE_: Dict = trim_offsets
SCREAMING_SNAKE_CASE_: Union[str, Any] = True
if changes_to_apply:
SCREAMING_SNAKE_CASE_: Any = getattr(lowerCAmelCase__ , state.pop("type"))
SCREAMING_SNAKE_CASE_: List[str] = component_class(**lowerCAmelCase__)
setattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__)
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return None
return str(self._mask_token)
@mask_token.setter
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]):
SCREAMING_SNAKE_CASE_: Tuple = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else value
SCREAMING_SNAKE_CASE_: str = value
def _SCREAMING_SNAKE_CASE ( self : List[Any] , *lowerCAmelCase__ : Any , **lowerCAmelCase__ : Optional[Any]):
SCREAMING_SNAKE_CASE_: Optional[Any] = 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 _SCREAMING_SNAKE_CASE ( self : List[Any] , *lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Union[str, Any]):
SCREAMING_SNAKE_CASE_: str = 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 _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None):
SCREAMING_SNAKE_CASE_: Optional[int] = self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__)
return tuple(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: str = [self.sep_token_id]
SCREAMING_SNAKE_CASE_: Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
return token_ids_a + [self.eos_token_id]
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : "Conversation"):
SCREAMING_SNAKE_CASE_: Dict = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text)
else:
# Generated responses should contain them already.
inputs.append(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = ' '.join(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = self.encode(lowerCAmelCase__)
if len(lowerCAmelCase__) > self.model_max_length:
SCREAMING_SNAKE_CASE_: Any = input_ids[-self.model_max_length :]
logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.")
return input_ids
| 359
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[int] = [
"""NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NllbMoeForConditionalGeneration""",
"""NllbMoeModel""",
"""NllbMoePreTrainedModel""",
"""NllbMoeTop2Router""",
"""NllbMoeSparseMLP""",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowerCAmelCase : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 127
| 0
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __lowercase ( _lowercase ):
lowerCamelCase : Optional[int] = "microsoft/speecht5_tts"
lowerCamelCase : Dict = (
"This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
"text to read (in English) and returns a waveform object containing the sound."
)
lowerCamelCase : Union[str, Any] = "text_reader"
lowerCamelCase : Dict = SpeechTaProcessor
lowerCamelCase : Optional[int] = SpeechTaForTextToSpeech
lowerCamelCase : Tuple = SpeechTaHifiGan
lowerCamelCase : List[str] = ["text"]
lowerCamelCase : List[str] = ["audio"]
def UpperCAmelCase__ (self ):
if self.post_processor is None:
lowerCamelCase_ : int = '''microsoft/speecht5_hifigan'''
super().setup()
def UpperCAmelCase__ (self , A , A=None ):
lowerCamelCase_ : List[str] = self.pre_processor(text=A , return_tensors='''pt''' , truncation=A )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.''' )
lowerCamelCase_ : Tuple = load_dataset('''Matthijs/cmu-arctic-xvectors''' , split='''validation''' )
lowerCamelCase_ : Optional[int] = torch.tensor(embeddings_dataset[7_3_0_5]['''xvector'''] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCAmelCase__ (self , A ):
with torch.no_grad():
return self.model.generate_speech(**A )
def UpperCAmelCase__ (self , A ):
with torch.no_grad():
return self.post_processor(A ).cpu().detach()
| 318
|
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , A , A , A = None , A = None ):
super().__init__()
lowerCamelCase_ : List[Any] = pad_token_id
lowerCamelCase_ : Union[str, Any] = max_length
lowerCamelCase_ : List[Any] = vocab
lowerCamelCase_ : Optional[int] = merges
lowerCamelCase_ : List[str] = BytePairTokenizer(A , A , sequence_length=A )
@classmethod
def UpperCAmelCase__ (cls , A , *A , **A ):
lowerCamelCase_ : int = [''' '''.join(A ) for m in tokenizer.bpe_ranks.keys()]
lowerCamelCase_ : Dict = tokenizer.get_vocab()
return cls(A , A , *A , **A )
@classmethod
def UpperCAmelCase__ (cls , A , *A , **A ):
lowerCamelCase_ : Optional[int] = GPTaTokenizer.from_pretrained(A , *A , **A )
return cls.from_tokenizer(A , *A , **A )
@classmethod
def UpperCAmelCase__ (cls , A ):
return cls(**A )
def UpperCAmelCase__ (self ):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def UpperCAmelCase__ (self , A , A = None ):
lowerCamelCase_ : str = self.tf_tokenizer(A )
lowerCamelCase_ : Any = tf.ones_like(A )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowerCamelCase_ : Tuple = max_length if max_length is not None else self.max_length
if max_length is not None:
lowerCamelCase_, lowerCamelCase_ : Tuple = pad_model_inputs(
A , max_seq_length=A , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 318
| 1
|
def A__ ( __lowerCamelCase, __lowerCamelCase ):
return 1 if input_a == input_a else 0
def A__ ( ):
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ) == 0
assert xnor_gate(1, 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 257
|
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[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, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
SCREAMING_SNAKE_CASE_ = kruskal(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(__lowerCamelCase ) == sorted(__lowerCamelCase )
| 257
| 1
|
"""simple docstring"""
__magic_name__ = [0, 2, 4, 6, 8]
__magic_name__ = [1, 3, 5, 7, 9]
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
__SCREAMING_SNAKE_CASE = 0
for digit in range(10 ):
__SCREAMING_SNAKE_CASE = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _UpperCAmelCase , _UpperCAmelCase )
return result
__SCREAMING_SNAKE_CASE = 0
for digita in range(10 ):
__SCREAMING_SNAKE_CASE = digita
if (remainder + digita) % 2 == 0:
__SCREAMING_SNAKE_CASE = ODD_DIGITS
else:
__SCREAMING_SNAKE_CASE = EVEN_DIGITS
for digita in other_parity_digits:
__SCREAMING_SNAKE_CASE = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _UpperCAmelCase , _UpperCAmelCase , )
return result
def _lowerCAmelCase ( UpperCamelCase_ = 9 ):
__SCREAMING_SNAKE_CASE = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_UpperCAmelCase , 0 , [0] * length , _UpperCAmelCase )
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 100
|
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339
| 0
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( __lowerCAmelCase ):
a__ = ["""image_processor""", """tokenizer"""]
a__ = """LayoutLMv3ImageProcessor"""
a__ = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
def __init__( self , lowercase=None , lowercase=None , **lowercase) -> Any:
'''simple docstring'''
a__: Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase , )
a__: Union[str, Any] = kwargs.pop('feature_extractor')
a__: Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.')
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.')
super().__init__(lowercase , lowercase)
def __call__( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ) -> BatchEncoding:
'''simple docstring'''
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')
# first, apply the image processor
a__: Optional[int] = self.image_processor(images=lowercase , return_tensors=lowercase)
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowercase , lowercase):
a__: List[Any] = [text] # add batch dimension (as the image processor always adds a batch dimension)
a__: Any = features['words']
a__: Dict = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
# add pixel values
a__: List[str] = features.pop('pixel_values')
if return_overflowing_tokens is True:
a__: str = self.get_overflowing_images(lowercase , encoded_inputs['overflow_to_sample_mapping'])
a__: Optional[Any] = images
return encoded_inputs
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(lowercase) != len(lowercase):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
f' {len(lowercase)} and {len(lowercase)}')
return images_with_overflow
def lowerCamelCase_ ( self , *lowercase , **lowercase) -> Optional[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase)
def lowerCamelCase_ ( self , *lowercase , **lowercase) -> str:
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase)
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase , )
return self.image_processor
| 203
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowercase__ = {
'vocab_file': {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
},
'tokenizer_file': {
'unc-nlp/lxmert-base-uncased': (
'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
),
},
}
lowercase__ = {
'unc-nlp/lxmert-base-uncased': 512,
}
lowercase__ = {
'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class __snake_case ( __lowerCAmelCase ):
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = PRETRAINED_INIT_CONFIGURATION
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = LxmertTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=True , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase=True , lowercase=None , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
a__: Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('lowercase' , lowercase) != do_lower_case
or normalizer_state.get('strip_accents' , lowercase) != strip_accents
or normalizer_state.get('handle_chinese_chars' , lowercase) != tokenize_chinese_chars
):
a__: int = getattr(lowercase , normalizer_state.pop('type'))
a__: Dict = do_lower_case
a__: Dict = strip_accents
a__: Optional[int] = tokenize_chinese_chars
a__: List[Any] = normalizer_class(**lowercase)
a__: Optional[int] = do_lower_case
def lowerCamelCase_ ( self , lowercase , lowercase=None) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase_ ( self , lowercase , lowercase = None) -> List[int]:
'''simple docstring'''
a__: List[Any] = [self.sep_token_id]
a__: List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def lowerCamelCase_ ( self , lowercase , lowercase = None) -> Tuple[str]:
'''simple docstring'''
a__: List[Any] = self._tokenizer.model.save(lowercase , name=lowercase)
return tuple(lowercase)
| 203
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : List[str] ={
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[str] = '''beit'''
def __init__( self , _A=8_192 , _A=768 , _A=12 , _A=12 , _A=3_072 , _A="gelu" , _A=0.0 , _A=0.0 , _A=0.0_2 , _A=1e-12 , _A=224 , _A=16 , _A=3 , _A=False , _A=False , _A=False , _A=False , _A=0.1 , _A=0.1 , _A=True , _A=[3, 5, 7, 11] , _A=[1, 2, 3, 6] , _A=True , _A=0.4 , _A=256 , _A=1 , _A=False , _A=255 , **_A , ):
'''simple docstring'''
super().__init__(**_A )
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = use_mask_token
__SCREAMING_SNAKE_CASE = use_absolute_position_embeddings
__SCREAMING_SNAKE_CASE = use_relative_position_bias
__SCREAMING_SNAKE_CASE = use_shared_relative_position_bias
__SCREAMING_SNAKE_CASE = layer_scale_init_value
__SCREAMING_SNAKE_CASE = drop_path_rate
__SCREAMING_SNAKE_CASE = use_mean_pooling
# decode head attributes (semantic segmentation)
__SCREAMING_SNAKE_CASE = out_indices
__SCREAMING_SNAKE_CASE = pool_scales
# auxiliary head attributes (semantic segmentation)
__SCREAMING_SNAKE_CASE = use_auxiliary_head
__SCREAMING_SNAKE_CASE = auxiliary_loss_weight
__SCREAMING_SNAKE_CASE = auxiliary_channels
__SCREAMING_SNAKE_CASE = auxiliary_num_convs
__SCREAMING_SNAKE_CASE = auxiliary_concat_input
__SCREAMING_SNAKE_CASE = semantic_loss_ignore_index
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[str] = version.parse('''1.11''' )
@property
def _A ( self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _A ( self ):
'''simple docstring'''
return 1e-4
| 257
|
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
def __lowercase ( a__ , a__ , a__ , a__ ) -> Tuple[int, int]:
def constraint_to_multiple_of(a__ , a__ , a__=0 , a__=None ):
__SCREAMING_SNAKE_CASE = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
__SCREAMING_SNAKE_CASE = math.floor(val / multiple ) * multiple
if x < min_val:
__SCREAMING_SNAKE_CASE = math.ceil(val / multiple ) * multiple
return x
__SCREAMING_SNAKE_CASE = (output_size, output_size) if isinstance(a__ , a__ ) else output_size
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_image_size(a__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = output_size
# determine new height and width
__SCREAMING_SNAKE_CASE = output_height / input_height
__SCREAMING_SNAKE_CASE = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
__SCREAMING_SNAKE_CASE = scale_width
else:
# fit height
__SCREAMING_SNAKE_CASE = scale_height
__SCREAMING_SNAKE_CASE = constraint_to_multiple_of(scale_height * input_height , multiple=a__ )
__SCREAMING_SNAKE_CASE = constraint_to_multiple_of(scale_width * input_width , multiple=a__ )
return (new_height, new_width)
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[str] = ['''pixel_values''']
def __init__( self , _A = True , _A = None , _A = PILImageResampling.BILINEAR , _A = False , _A = 1 , _A = True , _A = 1 / 255 , _A = True , _A = None , _A = None , **_A , ):
'''simple docstring'''
super().__init__(**_A )
__SCREAMING_SNAKE_CASE = size if size is not None else {'height': 384, 'width': 384}
__SCREAMING_SNAKE_CASE = get_size_dict(_A )
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = keep_aspect_ratio
__SCREAMING_SNAKE_CASE = ensure_multiple_of
__SCREAMING_SNAKE_CASE = resample
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _A ( self , _A , _A , _A = False , _A = 1 , _A = PILImageResampling.BICUBIC , _A = None , **_A , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
__SCREAMING_SNAKE_CASE = get_resize_output_image_size(
_A , output_size=(size['height'], size['width']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def _A ( self , _A , _A , _A = None , **_A , ):
'''simple docstring'''
return rescale(_A , scale=_A , data_format=_A , **_A )
def _A ( self , _A , _A , _A , _A = None , **_A , ):
'''simple docstring'''
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def _A ( self , _A , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = ChannelDimension.FIRST , **_A , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE = size if size is not None else self.size
__SCREAMING_SNAKE_CASE = get_size_dict(_A )
__SCREAMING_SNAKE_CASE = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
__SCREAMING_SNAKE_CASE = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
__SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE = [to_numpy_array(_A ) for image in images]
if do_resize:
__SCREAMING_SNAKE_CASE = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
__SCREAMING_SNAKE_CASE = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
__SCREAMING_SNAKE_CASE = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
__SCREAMING_SNAKE_CASE = [to_channel_dimension_format(_A , _A ) for image in images]
__SCREAMING_SNAKE_CASE = {'pixel_values': images}
return BatchFeature(data=_A , tensor_type=_A )
def _A ( self , _A , _A = None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_A ):
__SCREAMING_SNAKE_CASE = target_sizes.numpy()
__SCREAMING_SNAKE_CASE = []
for idx in range(len(_A ) ):
__SCREAMING_SNAKE_CASE = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_A )
__SCREAMING_SNAKE_CASE = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
__SCREAMING_SNAKE_CASE = logits.argmax(dim=1 )
__SCREAMING_SNAKE_CASE = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 257
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =StableDiffusionInpaintPipeline
SCREAMING_SNAKE_CASE_ =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
SCREAMING_SNAKE_CASE_ =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE_ =frozenset([] )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=9 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=snake_case__ , )
UpperCAmelCase__ : Tuple = PNDMScheduler(skip_prk_steps=snake_case__ )
torch.manual_seed(0 )
UpperCAmelCase__ : Optional[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
UpperCAmelCase__ : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
UpperCAmelCase__ : Dict = CLIPTextModel(snake_case__ )
UpperCAmelCase__ : str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCAmelCase__ : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __a ( self : Dict , snake_case__ : List[str] , snake_case__ : Optional[int]=0 ):
'''simple docstring'''
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
UpperCAmelCase__ : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase__ : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase__ : Dict = Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((6_4, 6_4) )
UpperCAmelCase__ : str = Image.fromarray(np.uinta(image + 4 ) ).convert("RGB" ).resize((6_4, 6_4) )
if str(snake_case__ ).startswith("mps" ):
UpperCAmelCase__ : Dict = torch.manual_seed(snake_case__ )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase__ : str = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __a ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ : List[str] = self.get_dummy_components()
UpperCAmelCase__ : Optional[int] = StableDiffusionInpaintPipeline(**snake_case__ )
UpperCAmelCase__ : Optional[int] = sd_pipe.to(snake_case__ )
sd_pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase__ : Dict = self.get_dummy_inputs(snake_case__ )
UpperCAmelCase__ : Dict = sd_pipe(**snake_case__ ).images
UpperCAmelCase__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
UpperCAmelCase__ : str = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __a ( self : Optional[int] ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def __a ( self : Tuple ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
UpperCAmelCase__ : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
UpperCAmelCase__ : Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy" )
UpperCAmelCase__ : List[Any] = "stabilityai/stable-diffusion-2-inpainting"
UpperCAmelCase__ : Tuple = StableDiffusionInpaintPipeline.from_pretrained(snake_case__ , safety_checker=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
pipe.enable_attention_slicing()
UpperCAmelCase__ : Optional[int] = "Face of a yellow cat, high resolution, sitting on a park bench"
UpperCAmelCase__ : int = torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , generator=snake_case__ , output_type="np" , )
UpperCAmelCase__ : List[Any] = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
UpperCAmelCase__ : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
UpperCAmelCase__ : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy" )
UpperCAmelCase__ : int = "stabilityai/stable-diffusion-2-inpainting"
UpperCAmelCase__ : List[str] = StableDiffusionInpaintPipeline.from_pretrained(
snake_case__ , torch_dtype=torch.floataa , safety_checker=snake_case__ , )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
pipe.enable_attention_slicing()
UpperCAmelCase__ : Optional[Any] = "Face of a yellow cat, high resolution, sitting on a park bench"
UpperCAmelCase__ : List[str] = torch.manual_seed(0 )
UpperCAmelCase__ : Dict = pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , generator=snake_case__ , output_type="np" , )
UpperCAmelCase__ : int = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def __a ( self : str ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase__ : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
UpperCAmelCase__ : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
UpperCAmelCase__ : Any = "stabilityai/stable-diffusion-2-inpainting"
UpperCAmelCase__ : Optional[Any] = PNDMScheduler.from_pretrained(snake_case__ , subfolder="scheduler" )
UpperCAmelCase__ : Dict = StableDiffusionInpaintPipeline.from_pretrained(
snake_case__ , safety_checker=snake_case__ , scheduler=snake_case__ , torch_dtype=torch.floataa , )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCAmelCase__ : Union[str, Any] = "Face of a yellow cat, high resolution, sitting on a park bench"
UpperCAmelCase__ : Dict = torch.manual_seed(0 )
UpperCAmelCase__ : int = pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , generator=snake_case__ , num_inference_steps=2 , output_type="np" , )
UpperCAmelCase__ : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 1_0**9
| 298
|
"""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
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": """bpe.codes""",
}
_lowerCAmelCase : List[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""",
},
}
_lowerCAmelCase : int = {
"""vinai/phobert-base""": 256,
"""vinai/phobert-large""": 256,
}
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = set()
UpperCAmelCase__ : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : Dict = char
UpperCAmelCase__ : Tuple = set(snake_case )
return pairs
class lowerCAmelCase__ ( __magic_name__ ):
SCREAMING_SNAKE_CASE_ =VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ =PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : List[Any] , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Tuple="<s>" , snake_case__ : List[Any]="</s>" , snake_case__ : Union[str, Any]="</s>" , snake_case__ : Union[str, Any]="<s>" , snake_case__ : Any="<unk>" , snake_case__ : int="<pad>" , snake_case__ : List[str]="<mask>" , **snake_case__ : Optional[int] , ):
'''simple docstring'''
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , cls_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase__ : Dict = vocab_file
UpperCAmelCase__ : Tuple = merges_file
UpperCAmelCase__ : List[Any] = {}
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : int = 1
UpperCAmelCase__ : Dict = 2
UpperCAmelCase__ : Dict = 3
self.add_from_file(snake_case__ )
UpperCAmelCase__ : Optional[Any] = {v: k for k, v in self.encoder.items()}
with open(snake_case__ , encoding="utf-8" ) as merges_handle:
UpperCAmelCase__ : Tuple = merges_handle.read().split("\n" )[:-1]
UpperCAmelCase__ : Optional[Any] = [tuple(merge.split()[:-1] ) for merge in merges]
UpperCAmelCase__ : List[Any] = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase__ : Dict = {}
def __a ( self : int , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
UpperCAmelCase__ : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __a ( self : List[str] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]
def __a ( self : Union[str, Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __a ( self : List[str] ):
'''simple docstring'''
return len(self.encoder )
def __a ( self : Any ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def __a ( self : Dict , snake_case__ : Tuple ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : Optional[Any] = tuple(snake_case__ )
UpperCAmelCase__ : Optional[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
UpperCAmelCase__ : Any = get_pairs(snake_case__ )
if not pairs:
return token
while True:
UpperCAmelCase__ : List[Any] = min(snake_case__ , key=lambda snake_case__ : self.bpe_ranks.get(snake_case__ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = bigram
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Tuple = 0
while i < len(snake_case__ ):
try:
UpperCAmelCase__ : Union[str, Any] = word.index(snake_case__ , snake_case__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ : Dict = j
if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ : Dict = tuple(snake_case__ )
UpperCAmelCase__ : List[Any] = new_word
if len(snake_case__ ) == 1:
break
else:
UpperCAmelCase__ : Dict = get_pairs(snake_case__ )
UpperCAmelCase__ : List[Any] = "@@ ".join(snake_case__ )
UpperCAmelCase__ : Optional[int] = word[:-4]
UpperCAmelCase__ : Union[str, Any] = word
return word
def __a ( self : List[Any] , snake_case__ : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : int = re.findall(R"\S+\n?" , snake_case__ )
for token in words:
split_tokens.extend(list(self.bpe(snake_case__ ).split(" " ) ) )
return split_tokens
def __a ( self : Dict , snake_case__ : List[str] ):
'''simple docstring'''
return self.encoder.get(snake_case__ , self.encoder.get(self.unk_token ) )
def __a ( self : List[Any] , snake_case__ : Any ):
'''simple docstring'''
return self.decoder.get(snake_case__ , self.unk_token )
def __a ( self : str , snake_case__ : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = " ".join(snake_case__ ).replace("@@ " , "" ).strip()
return out_string
def __a ( self : Any , snake_case__ : str , snake_case__ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
UpperCAmelCase__ : Tuple = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : str = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
if os.path.abspath(self.merges_file ) != os.path.abspath(snake_case__ ):
copyfile(self.merges_file , snake_case__ )
return out_vocab_file, out_merge_file
def __a ( self : List[Any] , snake_case__ : Union[str, Any] ):
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
try:
with open(snake_case__ , "r" , encoding="utf-8" ) as fd:
self.add_from_file(snake_case__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'Incorrect encoding detected in {f}, please rebuild the dataset' )
return
UpperCAmelCase__ : Dict = f.readlines()
for lineTmp in lines:
UpperCAmelCase__ : Optional[int] = lineTmp.strip()
UpperCAmelCase__ : Tuple = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
UpperCAmelCase__ : Any = line[:idx]
UpperCAmelCase__ : str = len(self.encoder )
| 298
| 1
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def _a ( a :int , a :Dict=False ) -> Optional[Any]:
a = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('''head''' ):
a = '''segformer.encoder.''' + key
if key.startswith('''backbone''' ):
a = key.replace('''backbone''' , '''segformer.encoder''' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
a = key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
a = key.replace(F"""patch_embed{idx}""" , F"""patch_embeddings.{int(a )-1}""" )
if "norm" in key:
a = key.replace('''norm''' , '''layer_norm''' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
a = key[key.find('''segformer.encoder.layer_norm''' ) + len('''segformer.encoder.layer_norm''' )]
a = key.replace(F"""layer_norm{idx}""" , F"""layer_norm.{int(a )-1}""" )
if "layer_norm1" in key:
a = key.replace('''layer_norm1''' , '''layer_norm_1''' )
if "layer_norm2" in key:
a = key.replace('''layer_norm2''' , '''layer_norm_2''' )
if "block" in key:
# replace for example block1 by block.0
a = key[key.find('''block''' ) + len('''block''' )]
a = key.replace(F"""block{idx}""" , F"""block.{int(a )-1}""" )
if "attn.q" in key:
a = key.replace('''attn.q''' , '''attention.self.query''' )
if "attn.proj" in key:
a = key.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in key:
a = key.replace('''attn''' , '''attention.self''' )
if "fc1" in key:
a = key.replace('''fc1''' , '''dense1''' )
if "fc2" in key:
a = key.replace('''fc2''' , '''dense2''' )
if "linear_pred" in key:
a = key.replace('''linear_pred''' , '''classifier''' )
if "linear_fuse" in key:
a = key.replace('''linear_fuse.conv''' , '''linear_fuse''' )
a = key.replace('''linear_fuse.bn''' , '''batch_norm''' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
a = key[key.find('''linear_c''' ) + len('''linear_c''' )]
a = key.replace(F"""linear_c{idx}""" , F"""linear_c.{int(a )-1}""" )
if key.startswith('''head''' ):
a = key.replace('''head''' , '''classifier''' )
a = value
return new_state_dict
def _a ( a :List[str] , a :Union[str, Any] ) -> Tuple:
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
a = state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
a = state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
a = kv_weight[
: config.hidden_sizes[i], :
]
a = kv_bias[: config.hidden_sizes[i]]
a = kv_weight[
config.hidden_sizes[i] :, :
]
a = kv_bias[
config.hidden_sizes[i] :
]
def _a ( ) -> Optional[Any]:
a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def _a ( a :Tuple , a :Any , a :int ) -> Any:
a = SegformerConfig()
a = False
# set attributes based on model_name
a = '''huggingface/label-files'''
if "segformer" in model_name:
a = model_name[len('''segformer.''' ) : len('''segformer.''' ) + 2]
if "ade" in model_name:
a = 150
a = '''ade20k-id2label.json'''
a = (1, 150, 128, 128)
elif "city" in model_name:
a = 19
a = '''cityscapes-id2label.json'''
a = (1, 19, 128, 128)
else:
raise ValueError(F"""Model {model_name} not supported""" )
elif "mit" in model_name:
a = True
a = model_name[4:6]
a = 1_000
a = '''imagenet-1k-id2label.json'''
a = (1, 1_000)
else:
raise ValueError(F"""Model {model_name} not supported""" )
# set config attributes
a = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
a = {int(a ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
a = [64, 128, 320, 512]
a = 256
elif size == "b2":
a = [64, 128, 320, 512]
a = 768
a = [3, 4, 6, 3]
elif size == "b3":
a = [64, 128, 320, 512]
a = 768
a = [3, 4, 18, 3]
elif size == "b4":
a = [64, 128, 320, 512]
a = 768
a = [3, 8, 27, 3]
elif size == "b5":
a = [64, 128, 320, 512]
a = 768
a = [3, 6, 40, 3]
else:
raise ValueError(F"""Size {size} not supported""" )
# load image processor (only resize + normalize)
a = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=a , align=a , do_random_crop=a )
# prepare image
a = prepare_img()
a = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(F"""Converting model {model_name}...""" )
# load original state dict
if encoder_only:
a = torch.load(a , map_location=torch.device('''cpu''' ) )
else:
a = torch.load(a , map_location=torch.device('''cpu''' ) )['''state_dict''']
# rename keys
a = rename_keys(a , encoder_only=a )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(a , a )
# create HuggingFace model and load state dict
if encoder_only:
a = False
a = SegformerForImageClassification(a )
else:
a = SegformerForSemanticSegmentation(a )
model.load_state_dict(a )
model.eval()
# forward pass
a = model(a )
a = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
a = torch.tensor(
[
[[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
[[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
[[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
a = torch.tensor(
[
[[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
[[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
[[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
a = torch.tensor(
[
[[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
[[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
[[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
a = torch.tensor(
[
[[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
[[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
[[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
a = torch.tensor(
[
[[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
[[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
[[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
a = torch.tensor(
[
[[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
[[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
[[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
a = torch.tensor(
[
[[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
[[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
[[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
a = torch.tensor(
[
[[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
[[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
[[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
a = torch.tensor(
[
[
[-1.1372e01, -1.2787e01, -1.3477e01],
[-1.2536e01, -1.4194e01, -1.4409e01],
[-1.3217e01, -1.4888e01, -1.5327e01],
],
[
[-1.4791e01, -1.7122e01, -1.8277e01],
[-1.7163e01, -1.9192e01, -1.9533e01],
[-1.7897e01, -1.9991e01, -2.0315e01],
],
[
[7.6723e-01, 4.1921e-01, -7.7878e-02],
[4.7772e-01, 9.5557e-03, -2.8082e-01],
[3.6032e-01, -2.4826e-01, -5.1168e-01],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
a = torch.tensor(
[
[[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
[[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
[[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
a = torch.tensor(
[
[[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
[[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
[[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
a = torch.tensor(
[
[[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
[[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
[[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
a = torch.tensor(
[
[[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
[[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
[[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
a = torch.tensor(
[
[[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
[[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
[[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
a = torch.tensor(
[
[[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
[[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
[[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
] )
else:
a = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="segformer.b0.512x512.ade.160k",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
UpperCAmelCase__ = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : List[str] = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : str = [
'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMForMultipleChoice',
'XLMForQuestionAnswering',
'XLMForQuestionAnsweringSimple',
'XLMForSequenceClassification',
'XLMForTokenClassification',
'XLMModel',
'XLMPreTrainedModel',
'XLMWithLMHeadModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Dict = [
'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMForMultipleChoice',
'TFXLMForQuestionAnsweringSimple',
'TFXLMForSequenceClassification',
'TFXLMForTokenClassification',
'TFXLMMainLayer',
'TFXLMModel',
'TFXLMPreTrainedModel',
'TFXLMWithLMHeadModel',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
__lowerCAmelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 88
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_SCREAMING_SNAKE_CASE : Any = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 355
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : int = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Any:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : int = ["flax"]
def __init__( self , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Any = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : str = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Tuple = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
| 92
| 0
|
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
snake_case__ : Optional[Any] = '''sshleifer/mar_enro_6_3_student'''
class snake_case_( a__ ):
def lowerCamelCase__ ( self : List[Any] ):
super().setUp()
lowerCAmelCase : Optional[Any] = cached_path(
'''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz''' , extract_compressed_file=UpperCamelCase_ , )
lowerCAmelCase : List[Any] = F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def lowerCamelCase__ ( self : Any ):
MarianMTModel.from_pretrained(UpperCamelCase_ )
@slow
@require_torch_gpu
def lowerCamelCase__ ( self : Union[str, Any] ):
lowerCAmelCase : List[str] = {
'''$MAX_LEN''': 6_4,
'''$BS''': 6_4,
'''$GAS''': 1,
'''$ENRO_DIR''': self.data_dir,
'''facebook/mbart-large-cc25''': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
'''--num_train_epochs 6''': '''--num_train_epochs 1''',
}
# Clean up bash script
lowerCAmelCase : Tuple = (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip()
lowerCAmelCase : Any = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' )
for k, v in env_vars_to_replace.items():
lowerCAmelCase : Any = bash_script.replace(UpperCamelCase_ , str(UpperCamelCase_ ) )
lowerCAmelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
lowerCAmelCase : List[Any] = F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
lowerCAmelCase : Union[str, Any] = ['''finetune.py'''] + bash_script.split() + args
with patch.object(UpperCamelCase_ , '''argv''' , UpperCamelCase_ ):
lowerCAmelCase : Dict = argparse.ArgumentParser()
lowerCAmelCase : Dict = pl.Trainer.add_argparse_args(UpperCamelCase_ )
lowerCAmelCase : Optional[int] = SummarizationModule.add_model_specific_args(UpperCamelCase_ , os.getcwd() )
lowerCAmelCase : List[Any] = parser.parse_args()
lowerCAmelCase : Union[str, Any] = main(UpperCamelCase_ )
# Check metrics
lowerCAmelCase : str = load_json(model.metrics_save_path )
lowerCAmelCase : Any = metrics['''val'''][0]
lowerCAmelCase : Optional[Any] = metrics['''val'''][-1]
self.assertEqual(len(metrics['''val'''] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , UpperCamelCase_ )
self.assertGreater(last_step_stats['''val_avg_gen_time'''] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['''val_avg_gen_time'''] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu'''] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['''val_avg_bleu'''] , 1_7 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu'''] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCAmelCase : List[Any] = os.listdir(UpperCamelCase_ )
lowerCAmelCase : Any = [x for x in contents if x.endswith('''.ckpt''' )][0]
lowerCAmelCase : Optional[int] = os.path.join(args.output_dir , UpperCamelCase_ )
lowerCAmelCase : Optional[Any] = torch.load(UpperCamelCase_ , map_location='''cpu''' )
lowerCAmelCase : List[str] = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCAmelCase : str = {os.path.basename(UpperCamelCase_ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test'''] ) == 1
class snake_case_( a__ ):
@timeout_decorator.timeout(6_0_0 )
@slow
@require_torch_gpu
def lowerCamelCase__ ( self : Optional[int] ):
lowerCAmelCase : Any = F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
lowerCAmelCase : Any = {
'''--fp16_opt_level=O1''': '''''',
'''$MAX_LEN''': 1_2_8,
'''$BS''': 1_6,
'''$GAS''': 1,
'''$ENRO_DIR''': data_dir,
'''$m''': '''sshleifer/student_marian_en_ro_6_1''',
'''val_check_interval=0.25''': '''val_check_interval=1.0''',
}
# Clean up bash script
lowerCAmelCase : Tuple = (
(self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip()
)
lowerCAmelCase : Union[str, Any] = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' )
lowerCAmelCase : Dict = bash_script.replace('''--fp16 ''' , ''' ''' )
for k, v in env_vars_to_replace.items():
lowerCAmelCase : List[Any] = bash_script.replace(UpperCamelCase_ , str(UpperCamelCase_ ) )
lowerCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : str = bash_script.replace('''--fp16''' , '''''' )
lowerCAmelCase : Optional[Any] = 6
lowerCAmelCase : Union[str, Any] = (
['''distillation.py''']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'''--gpus=1''',
'''--learning_rate=1e-3''',
F'''--num_train_epochs={epochs}''',
'''--warmup_steps=10''',
'''--val_check_interval=1.0''',
'''--do_predict''',
]
)
with patch.object(UpperCamelCase_ , '''argv''' , UpperCamelCase_ ):
lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
lowerCAmelCase : List[Any] = pl.Trainer.add_argparse_args(UpperCamelCase_ )
lowerCAmelCase : int = SummarizationDistiller.add_model_specific_args(UpperCamelCase_ , os.getcwd() )
lowerCAmelCase : int = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
lowerCAmelCase : int = distill_main(UpperCamelCase_ )
# Check metrics
lowerCAmelCase : Dict = load_json(model.metrics_save_path )
lowerCAmelCase : Optional[int] = metrics['''val'''][0]
lowerCAmelCase : str = metrics['''val'''][-1]
assert len(metrics['''val'''] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , UpperCamelCase_ )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCAmelCase : Dict = os.listdir(UpperCamelCase_ )
lowerCAmelCase : List[str] = [x for x in contents if x.endswith('''.ckpt''' )][0]
lowerCAmelCase : Dict = os.path.join(args.output_dir , UpperCamelCase_ )
lowerCAmelCase : Any = torch.load(UpperCamelCase_ , map_location='''cpu''' )
lowerCAmelCase : Dict = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCAmelCase : Tuple = {os.path.basename(UpperCamelCase_ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test'''] ) == 1
| 60
|
from math import pi, sqrt, tan
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
__lowerCamelCase = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(UpperCamelCase__ , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
__lowerCamelCase = (sidea + sidea + sidea) / 2
__lowerCamelCase = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f'''Rectangle: {area_rectangle(10, 20) = }''')
print(f'''Square: {area_square(10) = }''')
print(f'''Triangle: {area_triangle(10, 10) = }''')
print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(f'''Rhombus: {area_rhombus(10, 20) = }''')
print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(f'''Circle: {area_circle(20) = }''')
print(f'''Ellipse: {area_ellipse(10, 20) = }''')
print("\nSurface Areas of various geometric shapes: \n")
print(f'''Cube: {surface_area_cube(20) = }''')
print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(f'''Sphere: {surface_area_sphere(20) = }''')
print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(f'''Cone: {surface_area_cone(10, 20) = }''')
print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(f'''Torus: {surface_area_torus(20, 10) = }''')
print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(f'''Square: {area_reg_polygon(4, 10) = }''')
print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')
| 90
| 0
|
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a__ : Any = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
'''
a__ : Dict = '''\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
'''
a__ : Optional[int] = r'''
Calculates accuracy after canonicalizing inputs.
Args:
predictions: list of predictions to score. Each prediction
is a string that contains natural language and LaTex.
references: list of reference for each prediction. Each
reference is a string that contains natural language
and LaTex.
Returns:
accuracy: accuracy after canonicalizing inputs
(e.g., converting "1/2" to "\\frac{1}{2}")
Examples:
>>> metric = datasets.load_metric("competition_math")
>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
>>> print(results)
{\'accuracy\': 1.0}
'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCamelCase_ ( datasets.Metric):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/hendrycks/math" , codebase_urls=["https://github.com/hendrycks/math"] , )
def UpperCAmelCase_ ( self : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] ) -> Any:
__SCREAMING_SNAKE_CASE = 0.0
for i, j in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
n_correct += 1.0 if math_equivalence.is_equiv(UpperCAmelCase__ , UpperCAmelCase__ ) else 0.0
__SCREAMING_SNAKE_CASE = n_correct / len(UpperCAmelCase__ )
return {
"accuracy": accuracy,
}
| 368
|
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowerCAmelCase_ ) , lowerCAmelCase_ )
return number - int(lowerCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal_isolate(35.3_45, 2))
print(decimal_isolate(35.3_45, 3))
print(decimal_isolate(-14.7_89, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.1_23, 1))
print(decimal_isolate(-14.1_23, 2))
print(decimal_isolate(-14.1_23, 3))
| 195
| 0
|
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class a ( unittest.TestCase ):
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
for a, b in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertAlmostEqual(__lowerCamelCase , __lowerCamelCase , delta=__lowerCamelCase )
def UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
lowerCamelCase_ = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__lowerCamelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def UpperCamelCase ( self : Dict ) -> int:
lowerCamelCase_ = None
ops.enable_eager_execution_internal()
lowerCamelCase_ = tf.config.list_physical_devices('CPU' )
if len(__lowerCamelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCamelCase_ = tf.config.list_logical_devices(device_type='CPU' )
lowerCamelCase_ = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCamelCase_ = GradientAccumulator()
lowerCamelCase_ = tf.Variable([4.0, 3.0] )
lowerCamelCase_ , lowerCamelCase_ = create_optimizer(5e-5 , 10 , 5 )
lowerCamelCase_ = tf.Variable([0.0, 0.0] , trainable=__lowerCamelCase )
def accumulate_on_replica(__SCREAMING_SNAKE_CASE : Dict ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict ):
with strategy.scope():
lowerCamelCase_ = strategy.experimental_local_results(__lowerCamelCase )
local_variables[0].assign(__lowerCamelCase )
local_variables[1].assign(__lowerCamelCase )
strategy.run(__lowerCamelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__lowerCamelCase )
def _check_local_values(__SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict ):
lowerCamelCase_ = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __lowerCamelCase , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , __lowerCamelCase , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 183
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {'vocab_file': 'spiece.model'}
__lowerCAmelCase : Optional[int] = {
'vocab_file': {
'bert_for_seq_generation': (
'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
),
}
}
__lowerCAmelCase : Dict = {'bert_for_seq_generation': 512}
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : List[int] = []
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : str="<s>" , __lowerCamelCase : Optional[int]="</s>" , __lowerCamelCase : int="<unk>" , __lowerCamelCase : List[str]="<pad>" , __lowerCamelCase : Union[str, Any]="<::::>" , __lowerCamelCase : Optional[Dict[str, Any]] = None , **__lowerCamelCase : List[str] , ) -> None:
a = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , pad_token=__lowerCamelCase , sep_token=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCamelCase )
@property
def __UpperCAmelCase ( self : Dict ) -> Dict:
return self.sp_model.get_piece_size()
def __UpperCAmelCase ( self : Tuple ) -> Optional[int]:
a = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ) -> Optional[Any]:
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : Optional[Any] , __lowerCamelCase : Dict ) -> Optional[Any]:
a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
def __UpperCAmelCase ( self : str , __lowerCamelCase : Union[str, Any] ) -> int:
return self.sp_model.piece_to_id(__lowerCamelCase )
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ) -> Any:
a = self.sp_model.IdToPiece(__lowerCamelCase )
return token
def __UpperCAmelCase ( self : Any , __lowerCamelCase : Dict ) -> Any:
a = []
a = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__lowerCamelCase ) + token
a = []
else:
current_sub_tokens.append(__lowerCamelCase )
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def __UpperCAmelCase ( self : Dict , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCamelCase , "wb" ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,)
| 107
| 0
|
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A : Tuple = '''▁'''
A : int = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase ( a_ , unittest.TestCase ):
"""simple docstring"""
a = BigBirdTokenizer
a = BigBirdTokenizerFast
a = True
a = True
def A ( self : Tuple):
super().setUp()
_A : Dict = self.tokenizer_class(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE)
tokenizer.save_pretrained(self.tmpdirname)
def A ( self : List[Any]):
_A : Tuple = '<s>'
_A : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE) , SCREAMING_SNAKE_CASE)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE) , SCREAMING_SNAKE_CASE)
def A ( self : Tuple):
_A : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<unk>')
self.assertEqual(vocab_keys[1] , '<s>')
self.assertEqual(vocab_keys[-1] , '[MASK]')
self.assertEqual(len(SCREAMING_SNAKE_CASE) , 1004)
def A ( self : List[str]):
self.assertEqual(self.get_tokenizer().vocab_size , 1000)
def A ( self : Union[str, Any]):
if not self.test_rust_tokenizer:
return
_A : Tuple = self.get_tokenizer()
_A : int = self.get_rust_tokenizer()
_A : Optional[Any] = 'I was born in 92000, and this is falsé.'
_A : List[str] = tokenizer.tokenize(SCREAMING_SNAKE_CASE)
_A : Optional[int] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE)
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
_A : str = tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE)
_A : List[str] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE)
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
_A : str = self.get_rust_tokenizer()
_A : List[Any] = tokenizer.encode(SCREAMING_SNAKE_CASE)
_A : Tuple = rust_tokenizer.encode(SCREAMING_SNAKE_CASE)
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
def A ( self : Any):
_A : Dict = BigBirdTokenizer(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE)
_A : int = tokenizer.tokenize('This is a test')
self.assertListEqual(SCREAMING_SNAKE_CASE , ['▁This', '▁is', '▁a', '▁t', 'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE) , [285, 46, 10, 170, 382] , )
_A : List[Any] = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
_A : Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)
self.assertListEqual(
SCREAMING_SNAKE_CASE , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_A : List[str] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE)
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def A ( self : List[Any]):
return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
@slow
def A ( self : Tuple):
_A : Union[str, Any] = 'Hello World!'
_A : Optional[int] = [65, 18536, 2260, 101, 66]
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE))
@slow
def A ( self : List[Any]):
_A : Optional[int] = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
# fmt: off
_A : str = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE))
@require_torch
@slow
def A ( self : List[Any]):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
_A : Any = list(self.big_tokenizer.get_vocab().keys())[:10]
_A : Any = ' '.join(SCREAMING_SNAKE_CASE)
_A : List[Any] = self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE , return_tensors='pt' , return_token_type_ids=SCREAMING_SNAKE_CASE)
_A : Optional[Any] = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] , return_tensors='pt' , return_token_type_ids=SCREAMING_SNAKE_CASE)
_A : str = BigBirdConfig(attention_type='original_full')
_A : List[str] = BigBirdModel(SCREAMING_SNAKE_CASE)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE)
model(**SCREAMING_SNAKE_CASE)
@slow
def A ( self : Optional[Any]):
_A : List[str] = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
_A : Tuple = tokenizer.decode(tokenizer('Paris is the [MASK].').input_ids)
self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]')
@slow
def A ( self : Optional[Any]):
# fmt: off
_A : List[str] = {'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE , model_name='google/bigbird-roberta-base' , revision='215c99f1600e06f83acce68422f2035b2b5c3510' , )
| 227
|
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple=None):
_A : Any = data
_A : Optional[Any] = None
def __repr__( self : List[str]):
_A : List[Any] = []
_A : Any = self
while temp:
string_rep.append(F'{temp.data}')
_A : List[Any] = temp.next
return "->".join(SCREAMING_SNAKE_CASE)
def lowerCAmelCase__ ( lowerCamelCase : list ):
if not elements_list:
raise Exception('The Elements List is empty' )
_A : Union[str, Any] = Node(elements_list[0] )
for i in range(1 ,len(lowerCamelCase ) ):
_A : Dict = Node(elements_list[i] )
_A : int = current.next
return head
def lowerCAmelCase__ ( lowerCamelCase : Node ):
if head_node is not None and isinstance(lowerCamelCase ,lowerCamelCase ):
print_reverse(head_node.next )
print(head_node.data )
def lowerCAmelCase__ ( ):
from doctest import testmod
testmod()
_A : List[str] = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(lowerCamelCase )
print('Elements in Reverse:' )
print_reverse(lowerCamelCase )
if __name__ == "__main__":
main()
| 227
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , snake_case_ : Tuple , snake_case_ : Dict=13 , snake_case_ : Optional[Any]=7 , snake_case_ : List[Any]=True , snake_case_ : int=True , snake_case_ : Optional[Any]=True , snake_case_ : Optional[Any]=True , snake_case_ : Optional[Any]=99 , snake_case_ : Any=32 , snake_case_ : List[Any]=5 , snake_case_ : Union[str, Any]=4 , snake_case_ : Any=37 , snake_case_ : int="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : str=0.1 , snake_case_ : str=512 , snake_case_ : int=16 , snake_case_ : Dict=2 , snake_case_ : Dict=0.02 , snake_case_ : List[Any]=4 , ):
snake_case__ : List[Any] = parent
snake_case__ : List[str] = batch_size
snake_case__ : Tuple = seq_length
snake_case__ : Dict = is_training
snake_case__ : List[Any] = use_attention_mask
snake_case__ : Optional[int] = use_token_type_ids
snake_case__ : Union[str, Any] = use_labels
snake_case__ : Optional[Any] = vocab_size
snake_case__ : List[Any] = hidden_size
snake_case__ : List[Any] = num_hidden_layers
snake_case__ : Tuple = num_attention_heads
snake_case__ : Dict = intermediate_size
snake_case__ : List[Any] = hidden_act
snake_case__ : Tuple = hidden_dropout_prob
snake_case__ : Tuple = attention_probs_dropout_prob
snake_case__ : str = max_position_embeddings
snake_case__ : List[str] = type_vocab_size
snake_case__ : Any = type_sequence_label_size
snake_case__ : Optional[int] = initializer_range
snake_case__ : int = num_choices
def lowerCamelCase ( self : Any ):
snake_case__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ : int = None
if self.use_attention_mask:
snake_case__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : str = None
if self.use_token_type_ids:
snake_case__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase ( self : str ):
snake_case__ : Tuple = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Tuple = config_and_inputs
snake_case__ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
lowercase = True
lowercase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase ( self : Dict ):
snake_case__ : Optional[Any] = FlaxRoFormerModelTester(self )
@slow
def lowerCamelCase ( self : int ):
for model_class_name in self.all_model_classes:
snake_case__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=snake_case_ )
snake_case__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case_ )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase ( self : Optional[int] ):
snake_case__ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case__ : Dict = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case__ : List[str] = model(snake_case_ )[0]
snake_case__ : Union[str, Any] = 50_000
snake_case__ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , snake_case_ )
snake_case__ : Tuple = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
| 35
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 164
| 0
|
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
'''simple docstring'''
A : Any = IFPipeline
A : int = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
A : Optional[Any] = TEXT_TO_IMAGE_BATCH_PARAMS
A : Dict = PipelineTesterMixin.required_optional_params - {'latents'}
def _lowerCAmelCase ( self ) -> Optional[Any]:
return self._get_dummy_components()
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ) -> Tuple:
if str(snake_case__ ).startswith("mps" ):
snake_case_ : Tuple = torch.manual_seed(snake_case__ )
else:
snake_case_ : Dict = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
snake_case_ : Optional[int] = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def _lowerCAmelCase ( self ) -> Any:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def _lowerCAmelCase ( self ) -> int:
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _lowerCAmelCase ( self ) -> Tuple:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _lowerCAmelCase ( self ) -> str:
self._test_save_load_local()
def _lowerCAmelCase ( self ) -> Any:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _lowerCAmelCase ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> List[str]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self ) -> int:
snake_case_ : List[Any] = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0" , variant="fp16" , torch_dtype=torch.floataa )
snake_case_ : Any = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0" , variant="fp16" , torch_dtype=torch.floataa , text_encoder=snake_case__ , tokenizer=snake_case__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("cuda" )
snake_case_ : List[Any] = pipe_a.encode_prompt("anime turtle" , device="cuda" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
snake_case_ : List[str] = None
snake_case_ : Tuple = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
snake_case_ : List[str] = IFImgaImgPipeline(**pipe_a.components )
snake_case_ : int = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
snake_case_ : int = IFInpaintingPipeline(**pipe_a.components )
snake_case_ : Dict = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
_start_torch_memory_measurement()
snake_case_ : Tuple = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : int = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , num_inference_steps=2 , generator=snake_case__ , output_type="np" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
snake_case_ : Union[str, Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ : int = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : List[Any] = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , image=snake_case__ , generator=snake_case__ , num_inference_steps=2 , output_type="np" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ : Optional[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
_start_torch_memory_measurement()
snake_case_ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : List[str] = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : int = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , image=snake_case__ , num_inference_steps=2 , generator=snake_case__ , output_type="np" , )
snake_case_ : Optional[Any] = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
snake_case_ : Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ : Tuple = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : Union[str, Any] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : Union[str, Any] = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , image=snake_case__ , original_image=snake_case__ , generator=snake_case__ , num_inference_steps=2 , output_type="np" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ : int = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
_start_torch_memory_measurement()
snake_case_ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(snake_case__ )
snake_case_ : str = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : Any = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , image=snake_case__ , mask_image=snake_case__ , num_inference_steps=2 , generator=snake_case__ , output_type="np" , )
snake_case_ : Optional[Any] = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ : str = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
snake_case_ : int = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ : Dict = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : Optional[Any] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(snake_case__ )
snake_case_ : Any = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(snake_case__ )
snake_case_ : int = pipe_a(
prompt_embeds=snake_case__ , negative_prompt_embeds=snake_case__ , image=snake_case__ , mask_image=snake_case__ , original_image=snake_case__ , generator=snake_case__ , num_inference_steps=2 , output_type="np" , )
snake_case_ : List[str] = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy" )
assert_mean_pixel_difference(snake_case__ , snake_case__ )
def lowerCAmelCase__ ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 353
|
import unittest
import numpy as np
from transformers import BertConfig, 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.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=4 , ) -> List[str]:
snake_case_ : Dict = parent
snake_case_ : List[Any] = batch_size
snake_case_ : Union[str, Any] = seq_length
snake_case_ : Tuple = is_training
snake_case_ : List[str] = use_attention_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : Tuple = vocab_size
snake_case_ : Dict = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : List[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : Optional[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : List[Any] = num_choices
def _lowerCAmelCase ( self ) -> Union[str, Any]:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : str = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Tuple = BertConfig(
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=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCAmelCase ( self ) -> List[Any]:
snake_case_ : int = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = config_and_inputs
snake_case_ : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def _lowerCAmelCase ( self ) -> Optional[int]:
snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : str = True
snake_case_ : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A : List[str] = True
A : List[str] = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCAmelCase ( self ) -> List[str]:
snake_case_ : List[str] = FlaxBertModelTester(self )
@slow
def _lowerCAmelCase ( self ) -> Dict:
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
snake_case_ : int = FlaxBertModel.from_pretrained("bert-base-cased" )
snake_case_ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
| 36
| 0
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=1_2 , __lowerCAmelCase=7 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=9_9 , __lowerCAmelCase=3_2 , __lowerCAmelCase=3_2 , __lowerCAmelCase=2 , __lowerCAmelCase=4 , __lowerCAmelCase=3_7 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=0 , __lowerCAmelCase=None , ):
'''simple docstring'''
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = seq_length
lowerCamelCase__ = is_training
lowerCamelCase__ = use_input_mask
lowerCamelCase__ = use_labels
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = projection_dim
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = dropout
lowerCamelCase__ = attention_dropout
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scope
lowerCamelCase__ = bos_token_id
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase__ = None
if self.use_input_mask:
lowerCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCamelCase__ = input_mask.numpy()
lowerCamelCase__ , lowerCamelCase__ = input_mask.shape
lowerCamelCase__ = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__lowerCAmelCase ):
lowerCamelCase__ = 1
lowerCamelCase__ = 0
lowerCamelCase__ = self.get_config()
return config, input_ids, tf.convert_to_tensor(__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = TFBlipTextModel(config=__lowerCAmelCase )
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , training=__lowerCAmelCase )
lowerCamelCase__ = model(__lowerCAmelCase , training=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = config_and_inputs
lowerCamelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __A ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = BlipTextModelTester(self )
lowerCamelCase__ = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=3_7 )
def __lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ = TFBlipTextModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase=True ):
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCAmelCase )
| 209
|
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """Speech2TextFeatureExtractor"""
lowerCAmelCase_ = """Speech2TextTokenizer"""
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
def __call__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*__lowerCAmelCase , **__lowerCAmelCase )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
lowerCamelCase__ = kwargs.pop('''raw_speech''' )
else:
lowerCamelCase__ = kwargs.pop('''audio''' , __lowerCAmelCase )
lowerCamelCase__ = kwargs.pop('''sampling_rate''' , __lowerCAmelCase )
lowerCamelCase__ = kwargs.pop('''text''' , __lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
lowerCamelCase__ = args[0]
lowerCamelCase__ = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
lowerCamelCase__ = self.feature_extractor(__lowerCAmelCase , *__lowerCAmelCase , sampling_rate=__lowerCAmelCase , **__lowerCAmelCase )
if text is not None:
lowerCamelCase__ = self.tokenizer(__lowerCAmelCase , **__lowerCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ = encodings['''input_ids''']
return inputs
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@contextmanager
def __lowerCamelCase ( self ):
'''simple docstring'''
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
lowerCamelCase__ = True
lowerCamelCase__ = self.tokenizer
yield
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
| 209
| 1
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : Union[List[PIL.Image.Image], np.ndarray]
__lowerCamelCase : Optional[List[bool]]
__lowerCamelCase : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 276
|
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
A__ = prime_factors(__a )
if is_square_free(__a ):
return -1 if len(__a ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 276
| 1
|
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __lowercase ( a__ ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = np.inf
def set_batch_size(a__ ) -> None:
nonlocal batch_size
if isinstance(a__ , a__ ):
__SCREAMING_SNAKE_CASE = min(a__ , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(a__ , a__ ):
__SCREAMING_SNAKE_CASE = min(a__ , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(a__ , a__ ) and feature.dtype == "binary":
__SCREAMING_SNAKE_CASE = min(a__ , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(a__ , a__ )
return None if batch_size is np.inf else batch_size
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A = None , _A = None , _A = None , _A = False , _A = False , _A = None , **_A , ):
'''simple docstring'''
super().__init__(
_A , split=_A , features=_A , cache_dir=_A , keep_in_memory=_A , streaming=_A , num_proc=_A , **_A , )
__SCREAMING_SNAKE_CASE = path_or_paths if isinstance(_A , _A ) else {self.split: path_or_paths}
__SCREAMING_SNAKE_CASE = _PACKAGED_DATASETS_MODULES['parquet'][1]
__SCREAMING_SNAKE_CASE = Parquet(
cache_dir=_A , data_files=_A , features=_A , hash=_A , **_A , )
def _A ( self ):
'''simple docstring'''
if self.streaming:
__SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=_A , download_mode=_A , verification_mode=_A , base_path=_A , num_proc=self.num_proc , )
__SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split=self.split , verification_mode=_A , in_memory=self.keep_in_memory )
return dataset
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _A , _A , _A = None , **_A , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = path_or_buf
__SCREAMING_SNAKE_CASE = batch_size or get_writer_batch_size(dataset.features )
__SCREAMING_SNAKE_CASE = parquet_writer_kwargs
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , 'wb+' ) as buffer:
__SCREAMING_SNAKE_CASE = self._write(file_obj=_A , batch_size=_A , **self.parquet_writer_kwargs )
else:
__SCREAMING_SNAKE_CASE = self._write(file_obj=self.path_or_buf , batch_size=_A , **self.parquet_writer_kwargs )
return written
def _A ( self , _A , _A , **_A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = parquet_writer_kwargs.pop('path_or_buf' , _A )
__SCREAMING_SNAKE_CASE = self.dataset.features.arrow_schema
__SCREAMING_SNAKE_CASE = pq.ParquetWriter(_A , schema=_A , **_A )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , _A ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating parquet from Arrow format' , ):
__SCREAMING_SNAKE_CASE = query_table(
table=self.dataset._data , key=slice(_A , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(_A )
written += batch.nbytes
writer.close()
return written
| 257
|
def __lowercase ( a__ , a__ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 257
| 1
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any]=13 , SCREAMING_SNAKE_CASE : Tuple=30 , SCREAMING_SNAKE_CASE : Dict=2 , SCREAMING_SNAKE_CASE : str=3 , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : Dict=True , SCREAMING_SNAKE_CASE : List[Any]=32 , SCREAMING_SNAKE_CASE : int=5 , SCREAMING_SNAKE_CASE : Optional[Any]=4 , SCREAMING_SNAKE_CASE : Dict=37 , SCREAMING_SNAKE_CASE : Dict="gelu" , SCREAMING_SNAKE_CASE : str=0.1 , SCREAMING_SNAKE_CASE : List[Any]=0.1 , SCREAMING_SNAKE_CASE : Tuple=10 , SCREAMING_SNAKE_CASE : Dict=0.02 , SCREAMING_SNAKE_CASE : int=None , ):
_A : int = parent
_A : Tuple = batch_size
_A : Union[str, Any] = image_size
_A : List[str] = patch_size
_A : Optional[Any] = num_channels
_A : Dict = is_training
_A : Union[str, Any] = use_labels
_A : str = hidden_size
_A : Tuple = num_hidden_layers
_A : Tuple = num_attention_heads
_A : Tuple = intermediate_size
_A : Union[str, Any] = hidden_act
_A : Dict = hidden_dropout_prob
_A : int = attention_probs_dropout_prob
_A : str = type_sequence_label_size
_A : List[str] = initializer_range
_A : int = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_A : List[str] = (image_size // patch_size) ** 2
_A : int = num_patches + 1
def A ( self : List[str]):
_A : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_A : List[Any] = None
if self.use_labels:
_A : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_A : Optional[Any] = self.get_config()
return config, pixel_values, labels
def A ( self : List[str]):
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def A ( self : Dict , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict):
_A : str = ViTMSNModel(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_A : Union[str, Any] = model(SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def A ( self : Dict , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int]):
_A : str = self.type_sequence_label_size
_A : Union[str, Any] = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_A : int = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE)
print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
print('Labels: {labels}')
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
_A : int = 1
_A : List[Any] = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_A : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
_A : str = model(SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def A ( self : int):
_A : Dict = self.prepare_config_and_inputs()
_A , _A , _A : int = config_and_inputs
_A : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
a = (
{"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
if is_torch_available()
else {}
)
a = False
a = False
a = False
a = False
def A ( self : Union[str, Any]):
_A : Optional[Any] = ViTMSNModelTester(self)
_A : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37)
def A ( self : Optional[Any]):
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMSN does not use inputs_embeds')
def A ( self : Tuple):
pass
def A ( self : str):
_A , _A : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A : List[Any] = model_class(SCREAMING_SNAKE_CASE)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
_A : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , nn.Linear))
def A ( self : int):
_A , _A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A : Dict = model_class(SCREAMING_SNAKE_CASE)
_A : int = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A : Any = [*signature.parameters.keys()]
_A : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE)
def A ( self : Union[str, Any]):
_A : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)
def A ( self : Optional[Any]):
_A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)
@slow
def A ( self : str):
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A : Tuple = ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE)
self.assertIsNotNone(SCREAMING_SNAKE_CASE)
def lowerCAmelCase__ ( ):
_A : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def A ( self : int):
return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None
@slow
def A ( self : Optional[Any]):
torch.manual_seed(2)
_A : Any = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(SCREAMING_SNAKE_CASE)
_A : Dict = self.default_image_processor
_A : Any = prepare_img()
_A : Union[str, Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='pt').to(SCREAMING_SNAKE_CASE)
# forward pass
with torch.no_grad():
_A : int = model(**SCREAMING_SNAKE_CASE)
# verify the logits
_A : int = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE)
_A : str = torch.tensor([-0.0803, -0.4454, -0.2375]).to(SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4))
| 227
|
'''simple docstring'''
A : Dict = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( lowerCamelCase : str ):
_A : Dict = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_A : Stack[int] = Stack()
_A : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(lowerCamelCase )
elif i == ")":
# RULE 4
_A : List[str] = operator_stack.peek()
operator_stack.pop()
_A : Dict = operand_stack.peek()
operand_stack.pop()
_A : Optional[int] = operand_stack.peek()
operand_stack.pop()
_A : Dict = operators[opr](lowerCamelCase ,lowerCamelCase )
operand_stack.push(lowerCamelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
A : Dict = '''(5 + ((4 * 2) * (2 + 3)))'''
# answer = 45
print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
| 227
| 1
|
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def snake_case_ ( A_ : Optional[Any] ):
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def snake_case_ ( A_ : Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Tuple = np.max(_outputs, axis=-1, keepdims=A_ )
_lowerCamelCase : str = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=A_ )
class __snake_case ( _lowercase):
snake_case__ : Tuple = "sigmoid"
snake_case__ : str = "softmax"
snake_case__ : Tuple = "none"
@add_end_docstrings(
_lowercase , R"\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `\"sigmoid\"`: Applies the sigmoid function on the output.\n - `\"softmax\"`: Applies the softmax function on the output.\n - `\"none\"`: Does not apply any function on the output.\n " , )
class __snake_case ( _lowercase):
snake_case__ : Dict = False
snake_case__ : List[str] = ClassificationFunction.NONE
def __init__( self : List[str] , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : Dict=None , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : List[str]="" , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Any = tokenizer_kwargs
_lowerCamelCase : List[Any] = {}
if hasattr(self.model.config , '''return_all_scores''' ) and return_all_scores is None:
_lowerCamelCase : str = self.model.config.return_all_scores
if isinstance(__lowerCAmelCase , __lowerCAmelCase ) or top_k is None:
_lowerCamelCase : Tuple = top_k
_lowerCamelCase : List[str] = False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' , __lowerCAmelCase , )
if return_all_scores:
_lowerCamelCase : Dict = None
else:
_lowerCamelCase : Any = 1
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : str = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
_lowerCamelCase : int = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self : Tuple , *__lowerCAmelCase : int , **__lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = super().__call__(*__lowerCAmelCase , **__lowerCAmelCase )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
_lowerCamelCase : Dict = '''top_k''' not in kwargs
if isinstance(args[0] , __lowerCAmelCase ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = self.framework
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return self.tokenizer(**__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ) and len(__lowerCAmelCase ) == 1 and isinstance(inputs[0] , __lowerCAmelCase ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
return self.model(**__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : int=1 , __lowerCAmelCase : Dict=True ):
"""simple docstring"""
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
_lowerCamelCase : Optional[int] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
_lowerCamelCase : Optional[int] = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , '''function_to_apply''' ) and function_to_apply is None:
_lowerCamelCase : List[str] = self.model.config.function_to_apply
else:
_lowerCamelCase : List[Any] = ClassificationFunction.NONE
_lowerCamelCase : Union[str, Any] = model_outputs['''logits'''][0]
_lowerCamelCase : Dict = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
_lowerCamelCase : Dict = sigmoid(__lowerCAmelCase )
elif function_to_apply == ClassificationFunction.SOFTMAX:
_lowerCamelCase : Tuple = softmax(__lowerCAmelCase )
elif function_to_apply == ClassificationFunction.NONE:
_lowerCamelCase : str = outputs
else:
raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}''' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
_lowerCamelCase : Tuple = [
{'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(__lowerCAmelCase )
]
if not _legacy:
dict_scores.sort(key=lambda __lowerCAmelCase : x["score"] , reverse=__lowerCAmelCase )
if top_k is not None:
_lowerCamelCase : Tuple = dict_scores[:top_k]
return dict_scores
| 72
|
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A : List[Any] = True
except ImportError:
__A : int = False
__A : str = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase_ ( A__ : Namespace ):
'''simple docstring'''
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
@staticmethod
def __lowercase ( lowerCamelCase : ArgumentParser ) -> int:
lowerCAmelCase_ : Optional[int] = parser.add_parser("""add-new-model""" )
add_new_model_parser.add_argument("""--testing""" , action="""store_true""" , help="""If in testing mode.""" )
add_new_model_parser.add_argument("""--testing_file""" , type=lowerCamelCase , help="""Configuration file on which to run.""" )
add_new_model_parser.add_argument(
"""--path""" , type=lowerCamelCase , help="""Path to cookiecutter. Should only be used for testing purposes.""" )
add_new_model_parser.set_defaults(func=lowerCamelCase )
def __init__( self : List[str] , lowerCamelCase : bool , lowerCamelCase : str , lowerCamelCase : Any=None , *lowerCamelCase : List[str] ) -> Optional[Any]:
lowerCAmelCase_ : int = testing
lowerCAmelCase_ : Union[str, Any] = testing_file
lowerCAmelCase_ : Tuple = path
def __lowercase ( self : Tuple ) -> int:
warnings.warn(
"""The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
"""It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
"""checks, you should use `transformers-cli add-new-model-like` instead.""" )
if not _has_cookiecutter:
raise ImportError(
"""Model creation dependencies are required to use the `add_new_model` command. Install them by running """
"""the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n""" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCAmelCase_ : int = [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
if len(lowerCamelCase ) > 0:
raise ValueError(
"""Several directories starting with `cookiecutter-template-` in current working directory. """
"""Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
"""change your working directory.""" )
lowerCAmelCase_ : List[Any] = (
Path(lowerCamelCase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowerCAmelCase_ : Dict = path_to_transformer_root / """templates""" / """adding_a_new_model"""
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowerCamelCase ) )
else:
with open(self._testing_file , """r""" ) as configuration_file:
lowerCAmelCase_ : Tuple = json.load(lowerCamelCase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowerCamelCase , extra_context=lowerCamelCase , )
lowerCAmelCase_ : List[str] = [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]
# Retrieve configuration
with open(directory + """/configuration.json""" , """r""" ) as configuration_file:
lowerCAmelCase_ : Tuple = json.load(lowerCamelCase )
lowerCAmelCase_ : str = configuration["""lowercase_modelname"""]
lowerCAmelCase_ : List[str] = configuration["""generate_tensorflow_pytorch_and_flax"""]
os.remove(F'{directory}/configuration.json' )
lowerCAmelCase_ : Dict = """PyTorch""" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : Optional[int] = """TensorFlow""" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : List[str] = """Flax""" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase_ : Union[str, Any] = F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}' , exist_ok=lowerCamelCase )
# Tests require submodules as they have parent imports
with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' , """w""" ):
pass
shutil.move(
F'{directory}/__init__.py' , F'{model_dir}/__init__.py' , )
shutil.move(
F'{directory}/configuration_{lowercase_model_name}.py' , F'{model_dir}/configuration_{lowercase_model_name}.py' , )
def remove_copy_lines(lowerCamelCase : Any ):
with open(lowerCamelCase , """r""" ) as f:
lowerCAmelCase_ : List[str] = f.readlines()
with open(lowerCamelCase , """w""" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowerCamelCase )
if output_pytorch:
if not self._testing:
remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
F'{directory}/modeling_{lowercase_model_name}.py' , F'{model_dir}/modeling_{lowercase_model_name}.py' , )
shutil.move(
F'{directory}/test_modeling_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' , )
else:
os.remove(F'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
F'{directory}/modeling_tf_{lowercase_model_name}.py' , F'{model_dir}/modeling_tf_{lowercase_model_name}.py' , )
shutil.move(
F'{directory}/test_modeling_tf_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' , )
else:
os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
F'{directory}/modeling_flax_{lowercase_model_name}.py' , F'{model_dir}/modeling_flax_{lowercase_model_name}.py' , )
shutil.move(
F'{directory}/test_modeling_flax_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' , )
else:
os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
F'{directory}/{lowercase_model_name}.md' , F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' , )
shutil.move(
F'{directory}/tokenization_{lowercase_model_name}.py' , F'{model_dir}/tokenization_{lowercase_model_name}.py' , )
shutil.move(
F'{directory}/tokenization_fast_{lowercase_model_name}.py' , F'{model_dir}/tokenization_{lowercase_model_name}_fast.py' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : List[str] ):
# Create temp file
lowerCAmelCase_, lowerCAmelCase_ : int = mkstemp()
lowerCAmelCase_ : List[Any] = False
with fdopen(lowerCamelCase , """w""" ) as new_file:
with open(lowerCamelCase ) as old_file:
for line in old_file:
new_file.write(lowerCamelCase )
if line_to_copy_below in line:
lowerCAmelCase_ : List[str] = True
for line_to_copy in lines_to_copy:
new_file.write(lowerCamelCase )
if not line_found:
raise ValueError(F'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(lowerCamelCase , lowerCamelCase )
# Remove original file
remove(lowerCamelCase )
# Move new file
move(lowerCamelCase , lowerCamelCase )
def skip_units(lowerCamelCase : Optional[int] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowerCamelCase : Any ):
with open(lowerCamelCase ) as datafile:
lowerCAmelCase_ : Dict = []
lowerCAmelCase_ : List[str] = False
lowerCAmelCase_ : str = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCAmelCase_ : Dict = line.split("""\"""" )[1]
lowerCAmelCase_ : int = skip_units(lowerCamelCase )
elif "# Below: " in line and "##" not in line:
lowerCAmelCase_ : Any = line.split("""\"""" )[1]
lowerCAmelCase_ : Tuple = skip_units(lowerCamelCase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowerCamelCase , lowerCamelCase , lowerCamelCase )
lowerCAmelCase_ : Dict = []
elif "# Replace with" in line and "##" not in line:
lowerCAmelCase_ : int = []
elif "##" not in line:
lines_to_copy.append(lowerCamelCase )
remove(lowerCamelCase )
replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(lowerCamelCase )
| 120
| 0
|
import functools
from typing import Any
def snake_case (__lowercase , __lowercase ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or len(__lowercase ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(__lowercase , __lowercase ) or not all(
isinstance(__lowercase , __lowercase ) and len(__lowercase ) > 0 for item in words ):
raise ValueError("the words should be a list of non-empty strings" )
# Build trie
_snake_case : dict[str, Any] = {}
_snake_case : Any = "WORD_KEEPER"
for word in words:
_snake_case : str = trie
for c in word:
if c not in trie_node:
_snake_case : List[Any] = {}
_snake_case : Dict = trie_node[c]
_snake_case : Any = True
_snake_case : Union[str, Any] = len(__lowercase )
# Dynamic programming method
@functools.cache
def is_breakable(__lowercase ) -> bool:
if index == len_string:
return True
_snake_case : str = trie
for i in range(__lowercase , __lowercase ):
_snake_case : Any = trie_node.get(string[i] , __lowercase )
if trie_node is None:
return False
if trie_node.get(__lowercase , __lowercase ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 284
|
import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'bertabs'
def __init__( self , lowercase_=30_522 , lowercase_=512 , lowercase_=6 , lowercase_=512 , lowercase_=8 , lowercase_=512 , lowercase_=0.2 , lowercase_=6 , lowercase_=768 , lowercase_=8 , lowercase_=2_048 , lowercase_=0.2 , **lowercase_ , ):
super().__init__(**lowercase_ )
_snake_case : List[Any] = vocab_size
_snake_case : int = max_pos
_snake_case : Tuple = enc_layers
_snake_case : Optional[Any] = enc_hidden_size
_snake_case : Union[str, Any] = enc_heads
_snake_case : str = enc_ff_size
_snake_case : Any = enc_dropout
_snake_case : Tuple = dec_layers
_snake_case : Optional[Any] = dec_hidden_size
_snake_case : Dict = dec_heads
_snake_case : str = dec_ff_size
_snake_case : List[str] = dec_dropout
| 284
| 1
|
import math
def lowerCAmelCase__ ( a__: int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(a__ )
def lowerCAmelCase__ ( a__: float = 1 / 1_2_3_4_5 ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 3
while True:
_UpperCAmelCase = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(a__ ):
_UpperCAmelCase = int(a__ )
total_partitions += 1
if check_partition_perfect(a__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(a__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 329
|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class __a ( unittest.TestCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=400 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1 / 255 , _SCREAMING_SNAKE_CASE=True , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
_UpperCAmelCase = do_rescale
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = do_pad
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
if not batched:
_UpperCAmelCase = image_inputs[0]
if isinstance(_SCREAMING_SNAKE_CASE , Image.Image ):
_UpperCAmelCase , _UpperCAmelCase = image.size
else:
_UpperCAmelCase , _UpperCAmelCase = image.shape[1], image.shape[2]
if w < h:
_UpperCAmelCase = int(self.size['shortest_edge'] * h / w )
_UpperCAmelCase = self.size['shortest_edge']
elif w > h:
_UpperCAmelCase = self.size['shortest_edge']
_UpperCAmelCase = int(self.size['shortest_edge'] * w / h )
else:
_UpperCAmelCase = self.size['shortest_edge']
_UpperCAmelCase = self.size['shortest_edge']
else:
_UpperCAmelCase = []
for image in image_inputs:
_UpperCAmelCase , _UpperCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_UpperCAmelCase = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[0] )[0]
_UpperCAmelCase = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __a ( UpperCAmelCase , unittest.TestCase ):
_a : str = DeformableDetrImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = DeformableDetrImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_mean' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_std' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_normalize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_resize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_rescale' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_pad' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'size' ) )
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
pass
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
_UpperCAmelCase , _UpperCAmelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
_UpperCAmelCase = json.loads(f.read() )
_UpperCAmelCase = {'image_id': 39769, 'annotations': target}
# encode them
_UpperCAmelCase = DeformableDetrImageProcessor()
_UpperCAmelCase = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
# verify pixel values
_UpperCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
# verify area
_UpperCAmelCase = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
_UpperCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# verify image_id
_UpperCAmelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
_UpperCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
_UpperCAmelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _SCREAMING_SNAKE_CASE ) )
# verify orig_size
_UpperCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _SCREAMING_SNAKE_CASE ) )
# verify size
_UpperCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _SCREAMING_SNAKE_CASE ) )
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
_UpperCAmelCase = json.loads(f.read() )
_UpperCAmelCase = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}
_UpperCAmelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
_UpperCAmelCase = DeformableDetrImageProcessor(format='coco_panoptic' )
_UpperCAmelCase = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , masks_path=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
# verify pixel values
_UpperCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
# verify area
_UpperCAmelCase = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
_UpperCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# verify image_id
_UpperCAmelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
_UpperCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
_UpperCAmelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _SCREAMING_SNAKE_CASE ) )
# verify masks
_UpperCAmelCase = 822873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _SCREAMING_SNAKE_CASE )
# verify orig_size
_UpperCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _SCREAMING_SNAKE_CASE ) )
# verify size
_UpperCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _SCREAMING_SNAKE_CASE ) )
| 329
| 1
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :str = logging.get_logger(__name__)
class _A ( __lowercase ):
UpperCamelCase__ : Optional[Any] = '''encoder-decoder'''
UpperCamelCase__ : List[str] = True
def __init__( self : Any , **__SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
super().__init__(**UpperCAmelCase__)
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
__a = kwargs.pop('''encoder''')
__a = encoder_config.pop('''model_type''')
__a = kwargs.pop('''decoder''')
__a = decoder_config.pop('''model_type''')
from ..auto.configuration_auto import AutoConfig
__a = AutoConfig.for_model(UpperCAmelCase__ , **UpperCAmelCase__)
__a = AutoConfig.for_model(UpperCAmelCase__ , **UpperCAmelCase__)
__a = True
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , __SCREAMING_SNAKE_CASE : PretrainedConfig , __SCREAMING_SNAKE_CASE : PretrainedConfig , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
__a = True
__a = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **UpperCAmelCase__)
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = copy.deepcopy(self.__dict__)
__a = self.encoder.to_dict()
__a = self.decoder.to_dict()
__a = self.__class__.model_type
return output
| 361
|
def __snake_case ( _UpperCAmelCase = 1000000 ):
__a = limit + 1
__a = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__a = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f'{solution() = }')
| 131
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Any , A : int ) -> None:
lowercase_ : List[str] = value
lowercase_ : Node | None = None
lowercase_ : Node | None = None
class _UpperCAmelCase :
def __init__( self : Optional[int] , A : Node ) -> None:
lowercase_ : Optional[Any] = tree
def A ( self : Any , A : Node | None ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : int ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
|
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334
| 0
|
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _SCREAMING_SNAKE_CASE ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowerCAmelCase__ = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def lowerCamelCase_ ( ):
if os.name == "nt":
lowerCamelCase_ = CursorInfo()
lowerCamelCase_ = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
lowerCamelCase_ = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def lowerCamelCase_ ( ):
if os.name == "nt":
lowerCamelCase_ = CursorInfo()
lowerCamelCase_ = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
lowerCamelCase_ = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 47
|
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase_ ( lowerCamelCase__ ):
if is_torch_version("<" , "2.0.0" ) or not hasattr(lowerCamelCase__ , "_dynamo" ):
return False
return isinstance(lowerCamelCase__ , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ = True ):
lowerCamelCase_ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
lowerCamelCase_ = is_compiled_module(lowerCamelCase__ )
if is_compiled:
lowerCamelCase_ = model
lowerCamelCase_ = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = model.module
if not keep_fpaa_wrapper:
lowerCamelCase_ = getattr(lowerCamelCase__ , "forward" )
lowerCamelCase_ = model.__dict__.pop("_original_forward" , lowerCamelCase__ )
if original_forward is not None:
while hasattr(lowerCamelCase__ , "__wrapped__" ):
lowerCamelCase_ = forward.__wrapped__
if forward == original_forward:
break
lowerCamelCase_ = forward
if getattr(lowerCamelCase__ , "_converted_to_transformer_engine" , lowerCamelCase__ ):
convert_model(lowerCamelCase__ , to_transformer_engine=lowerCamelCase__ )
if is_compiled:
lowerCamelCase_ = model
lowerCamelCase_ = compiled_model
return model
def lowerCamelCase_ ( ):
PartialState().wait_for_everyone()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowerCamelCase__ , lowerCamelCase__ )
elif PartialState().local_process_index == 0:
torch.save(lowerCamelCase__ , lowerCamelCase__ )
@contextmanager
def lowerCamelCase_ ( **lowerCamelCase__ ):
for key, value in kwargs.items():
lowerCamelCase_ = str(lowerCamelCase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase_ ( lowerCamelCase__ ):
if not hasattr(lowerCamelCase__ , "__qualname__" ) and not hasattr(lowerCamelCase__ , "__name__" ):
lowerCamelCase_ = getattr(lowerCamelCase__ , "__class__" , lowerCamelCase__ )
if hasattr(lowerCamelCase__ , "__qualname__" ):
return obj.__qualname__
if hasattr(lowerCamelCase__ , "__name__" ):
return obj.__name__
return str(lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
for key, value in source.items():
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = destination.setdefault(lowerCamelCase__ , {} )
merge_dicts(lowerCamelCase__ , lowerCamelCase__ )
else:
lowerCamelCase_ = value
return destination
def lowerCamelCase_ ( lowerCamelCase__ = None ):
if port is None:
lowerCamelCase_ = 2_9_5_0_0
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 47
| 1
|
import pprint
import requests
a__ = '''https://zenquotes.io/api'''
def __UpperCAmelCase ( ) -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def __UpperCAmelCase ( ) -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
a__ = random_quotes()
pprint.pprint(response)
| 235
|
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=7 , _a=False , _a=True , _a=False , _a=False , _a=1_9 , _a=3_2 , _a=5 , _a=4 , _a=3_7 , _a="gelu" , _a=0.1 , _a=0.1 , _a=5_1_2 , _a=1_6 , _a=2 , _a=0.02 , _a=3 , _a=4 , _a=None , ) -> Union[str, Any]:
_a : Optional[Any] = parent
_a : Union[str, Any] = batch_size
_a : List[Any] = seq_length
_a : Dict = is_training
_a : int = use_input_mask
_a : str = use_token_type_ids
_a : Any = use_labels
_a : List[Any] = vocab_size
_a : Any = hidden_size
_a : int = num_hidden_layers
_a : str = num_attention_heads
_a : Dict = intermediate_size
_a : List[str] = hidden_act
_a : Optional[Any] = hidden_dropout_prob
_a : Optional[Any] = attention_probs_dropout_prob
_a : int = max_position_embeddings
_a : Tuple = type_vocab_size
_a : str = type_sequence_label_size
_a : Any = initializer_range
_a : Union[str, Any] = num_labels
_a : Dict = num_choices
_a : Union[str, Any] = scope
def __lowercase ( self ) -> List[Any]:
_a : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : Dict = None
if self.use_input_mask:
_a : int = random_attention_mask([self.batch_size, self.seq_length] )
_a : List[Any] = None
_a : Tuple = None
_a : Any = None
if self.use_labels:
_a : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
_a : str = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase ( self ) -> str:
_a : Optional[int] = EsmConfig(
vocab_size=3_3 , hidden_size=self.hidden_size , pad_token_id=1 , 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 , is_folding_model=_a , esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False} , )
return config
def __lowercase ( self , _a , _a , _a , _a , _a , _a ) -> str:
_a : Union[str, Any] = EsmForProteinFolding(config=_a ).float()
model.to(_a )
model.eval()
_a : str = model(_a , attention_mask=_a )
_a : Union[str, Any] = model(_a )
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 1_4, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def __lowercase ( self ) -> str:
_a : List[str] = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : Optional[Any] = config_and_inputs
_a : List[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : Any = (EsmForProteinFolding,) if is_torch_available() else ()
UpperCAmelCase__ : Union[str, Any] = ()
UpperCAmelCase__ : int = {} if is_torch_available() else {}
UpperCAmelCase__ : Optional[int] = False
def __lowercase ( self ) -> List[Any]:
_a : Optional[int] = EsmFoldModelTester(self )
_a : Dict = ConfigTester(self , config_class=_a , hidden_size=3_7 )
def __lowercase ( self ) -> List[str]:
self.config_tester.run_common_tests()
def __lowercase ( self ) -> str:
_a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
@unittest.skip('''Does not support attention outputs''' )
def __lowercase ( self ) -> int:
pass
@unittest.skip
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def __lowercase ( self ) -> int:
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def __lowercase ( self ) -> Optional[Any]:
pass
@unittest.skip('''ESMFold does not support passing input embeds!''' )
def __lowercase ( self ) -> int:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def __lowercase ( self ) -> str:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def __lowercase ( self ) -> Any:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def __lowercase ( self ) -> Tuple:
pass
@unittest.skip('''ESMFold does not output hidden states in the normal way.''' )
def __lowercase ( self ) -> Tuple:
pass
@unittest.skip('''ESMfold does not output hidden states in the normal way.''' )
def __lowercase ( self ) -> Tuple:
pass
@unittest.skip('''ESMFold only has one output format.''' )
def __lowercase ( self ) -> Tuple:
pass
@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''' )
def __lowercase ( self ) -> Dict:
pass
@unittest.skip('''ESMFold does not support input chunking.''' )
def __lowercase ( self ) -> Tuple:
pass
@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def __lowercase ( self ) -> List[Any]:
pass
@unittest.skip('''ESMFold doesn\'t support data parallel.''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
@require_torch
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
@slow
def __lowercase ( self ) -> Optional[int]:
_a : Dict = EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''' ).float()
model.eval()
_a : Tuple = torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
_a : Optional[Any] = model(_a )['''positions''']
_a : Union[str, Any] = torch.tensor([2.5828, 0.7993, -10.9334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , _a , atol=1e-4 ) )
| 235
| 1
|
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__lowercase = logging.get_logger(__name__)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Any = ["""input_features""", """is_longer"""]
def __init__( self , __lowercase=64 , __lowercase=48_000 , __lowercase=480 , __lowercase=10 , __lowercase=1_024 , __lowercase=0.0 , __lowercase=False , __lowercase = 0 , __lowercase = 14_000 , __lowercase = None , __lowercase = "fusion" , __lowercase = "repeatpad" , **__lowercase , ) -> int:
super().__init__(
feature_size=__lowercase , sampling_rate=__lowercase , padding_value=__lowercase , return_attention_mask=__lowercase , **__lowercase , )
__UpperCamelCase :Union[str, Any] = top_db
__UpperCamelCase :Union[str, Any] = truncation
__UpperCamelCase :List[str] = padding
__UpperCamelCase :Dict = fft_window_size
__UpperCamelCase :str = (fft_window_size >> 1) + 1
__UpperCamelCase :Union[str, Any] = hop_length
__UpperCamelCase :List[Any] = max_length_s
__UpperCamelCase :Union[str, Any] = max_length_s * sampling_rate
__UpperCamelCase :Any = sampling_rate
__UpperCamelCase :Tuple = frequency_min
__UpperCamelCase :str = frequency_max
__UpperCamelCase :Optional[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__lowercase , min_frequency=__lowercase , max_frequency=__lowercase , sampling_rate=__lowercase , norm=__lowercase , mel_scale='''htk''' , )
__UpperCamelCase :str = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__lowercase , min_frequency=__lowercase , max_frequency=__lowercase , sampling_rate=__lowercase , norm='''slaney''' , mel_scale='''slaney''' , )
def UpperCamelCase__ ( self) -> Dict[str, Any]:
__UpperCamelCase :str = copy.deepcopy(self.__dict__)
__UpperCamelCase :Optional[int] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def UpperCamelCase__ ( self , __lowercase , __lowercase = None) -> np.ndarray:
__UpperCamelCase :Optional[Any] = spectrogram(
__lowercase , window_function(self.fft_window_size , '''hann''') , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=__lowercase , log_mel='''dB''' , )
return log_mel_spectrogram.T
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[str]:
__UpperCamelCase :Union[str, Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCamelCase :Union[str, Any] = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCamelCase :int = [0]
# randomly choose index for each part
__UpperCamelCase :str = np.random.choice(ranges[0])
__UpperCamelCase :Optional[Any] = np.random.choice(ranges[1])
__UpperCamelCase :List[Any] = np.random.choice(ranges[2])
__UpperCamelCase :Tuple = mel[idx_front : idx_front + chunk_frames, :]
__UpperCamelCase :List[Any] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCamelCase :Any = mel[idx_back : idx_back + chunk_frames, :]
__UpperCamelCase :int = torch.tensor(mel[None, None, :])
__UpperCamelCase :Tuple = torch.nn.functional.interpolate(
__lowercase , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=__lowercase)
__UpperCamelCase :Optional[Any] = mel_shrink[0][0].numpy()
__UpperCamelCase :Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCamelCase :List[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCamelCase :Tuple = len(__lowercase) - max_length
__UpperCamelCase :List[Any] = np.random.randint(0 , overflow + 1)
__UpperCamelCase :Any = waveform[idx : idx + max_length]
__UpperCamelCase :Tuple = self._np_extract_fbank_features(__lowercase , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCamelCase :str = self._np_extract_fbank_features(__lowercase , self.mel_filters)
__UpperCamelCase :int = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCamelCase :str = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCamelCase :Union[str, Any] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCamelCase :Optional[Any] = False
else:
__UpperCamelCase :Dict = self._random_mel_fusion(__lowercase , __lowercase , __lowercase)
__UpperCamelCase :Tuple = True
else:
raise NotImplementedError(f"""data_truncating {truncation} not implemented""")
else:
__UpperCamelCase :Dict = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCamelCase :Dict = int(max_length / len(__lowercase))
__UpperCamelCase :Any = np.stack(np.tile(__lowercase , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCamelCase :int = int(max_length / len(__lowercase))
__UpperCamelCase :Union[str, Any] = np.stack(np.tile(__lowercase , __lowercase))
__UpperCamelCase :Union[str, Any] = np.pad(__lowercase , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0)
if truncation == "fusion":
__UpperCamelCase :List[str] = self._np_extract_fbank_features(__lowercase , self.mel_filters)
__UpperCamelCase :Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCamelCase :str = self._np_extract_fbank_features(__lowercase , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , **__lowercase , ) -> BatchFeature:
__UpperCamelCase :Optional[int] = truncation if truncation is not None else self.truncation
__UpperCamelCase :str = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""")
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''')
__UpperCamelCase :Optional[Any] = isinstance(__lowercase , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""")
__UpperCamelCase :Optional[Any] = is_batched_numpy or (
isinstance(__lowercase , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCamelCase :List[Any] = [np.asarray(__lowercase , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(__lowercase , np.ndarray):
__UpperCamelCase :Dict = np.asarray(__lowercase , dtype=np.floataa)
elif isinstance(__lowercase , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCamelCase :Union[str, Any] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCamelCase :List[Any] = [np.asarray(__lowercase)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCamelCase :Optional[int] = [
self._get_input_mel(__lowercase , max_length if max_length else self.nb_max_samples , __lowercase , __lowercase)
for waveform in raw_speech
]
__UpperCamelCase :List[Any] = []
__UpperCamelCase :str = []
for mel, longer in padded_inputs:
input_mel.append(__lowercase)
is_longer.append(__lowercase)
if truncation == "fusion" and sum(__lowercase) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCamelCase :Tuple = np.random.randint(0 , len(__lowercase))
__UpperCamelCase :Any = True
if isinstance(input_mel[0] , __lowercase):
__UpperCamelCase :Dict = [np.asarray(__lowercase , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCamelCase :Dict = [[longer] for longer in is_longer]
__UpperCamelCase :Optional[Any] = {'''input_features''': input_mel, '''is_longer''': is_longer}
__UpperCamelCase :Dict = BatchFeature(__lowercase)
if return_tensors is not None:
__UpperCamelCase :int = input_features.convert_to_tensors(__lowercase)
return input_features
| 105
|
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 105
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ :Optional[int] = logging.get_logger(__name__)
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
# initialize config
if "resnet-50" in model_name:
lowercase = ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
elif "resnet-101" in model_name:
lowercase = ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
else:
raise ValueError('''Model name should include either resnet50 or resnet101''' )
lowercase = DetrConfig(use_timm_backbone=lowerCAmelCase__ , backbone_config=lowerCAmelCase__ )
# set label attributes
lowercase = '''panoptic''' in model_name
if is_panoptic:
lowercase = 250
else:
lowercase = 91
lowercase = '''huggingface/label-files'''
lowercase = '''coco-detection-id2label.json'''
lowercase = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase = []
# stem
# fmt: off
rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''') )
rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''') )
rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''') )
rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''') )
rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
) )
rename_keys.append(
(
f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
f'encoder.layers.{i}.self_attn.out_proj.weight',
) )
rename_keys.append(
(f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias') )
rename_keys.append(
(f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append(
(f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append(
(f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
f'decoder.layers.{i}.self_attn.out_proj.weight',
) )
rename_keys.append(
(f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
) )
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
f'decoder.layers.{i}.encoder_attn.out_proj.bias',
) )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias') )
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight') )
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias') )
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
] )
return rename_keys
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=False ):
'''simple docstring'''
lowercase = ''''''
if is_panoptic:
lowercase = '''detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
lowercase = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase = in_proj_weight[:256, :]
lowercase = in_proj_bias[:256]
lowercase = in_proj_weight[256:512, :]
lowercase = in_proj_bias[256:512]
lowercase = in_proj_weight[-256:, :]
lowercase = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
lowercase = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase = in_proj_weight[:256, :]
lowercase = in_proj_bias[:256]
lowercase = in_proj_weight[256:512, :]
lowercase = in_proj_bias[256:512]
lowercase = in_proj_weight[-256:, :]
lowercase = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
lowercase = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase = in_proj_weight_cross_attn[:256, :]
lowercase = in_proj_bias_cross_attn[:256]
lowercase = in_proj_weight_cross_attn[256:512, :]
lowercase = in_proj_bias_cross_attn[256:512]
lowercase = in_proj_weight_cross_attn[-256:, :]
lowercase = in_proj_bias_cross_attn[-256:]
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=False ):
'''simple docstring'''
lowercase = get_detr_config(lowerCAmelCase__ )
# load original model from torch hub
lowercase = {
'''detr-resnet-50''': '''detr_resnet50''',
'''detr-resnet-101''': '''detr_resnet101''',
}
logger.info(f'Converting model {model_name}...' )
lowercase = torch.hub.load('''facebookresearch/detr''' , model_name_to_original_name[model_name] , pretrained=lowerCAmelCase__ ).eval()
lowercase = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(lowerCAmelCase__ ):
if is_panoptic:
lowercase = '''detr.''' + src
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCAmelCase__ , is_panoptic=lowerCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase = '''detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val
# finally, create HuggingFace model and load state dict
lowercase = DetrForSegmentation(lowerCAmelCase__ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
model.eval()
# verify our conversion on an image
lowercase = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
lowercase = DetrImageProcessor(format=lowerCAmelCase__ )
lowercase = processor(images=prepare_img() , return_tensors='''pt''' )
lowercase = encoding['''pixel_values''']
lowercase = detr(lowerCAmelCase__ )
lowercase = model(lowerCAmelCase__ )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('''Uploading PyTorch model and image processor to the hub...''' )
model.push_to_hub(f'nielsr/{model_name}' )
processor.push_to_hub(f'nielsr/{model_name}' )
if __name__ == "__main__":
lowercase__ :str = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
lowercase__ :Tuple = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 101
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowercase__ =True
except ImportError:
lowercase__ =False
lowercase__ =logging.get_logger(__name__) # pylint: disable=invalid-name
def __UpperCamelCase ( lowerCAmelCase__ : Namespace ):
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class UpperCamelCase__ ( __lowercase ):
@staticmethod
def lowerCAmelCase (snake_case_ : ArgumentParser ):
__a : List[Any] = parser.add_parser('''add-new-model''' )
add_new_model_parser.add_argument('''--testing''' , action='''store_true''' , help='''If in testing mode.''' )
add_new_model_parser.add_argument('''--testing_file''' , type=snake_case_ , help='''Configuration file on which to run.''' )
add_new_model_parser.add_argument(
'''--path''' , type=snake_case_ , help='''Path to cookiecutter. Should only be used for testing purposes.''' )
add_new_model_parser.set_defaults(func=snake_case_ )
def __init__(self : Dict , snake_case_ : bool , snake_case_ : str , snake_case_ : Dict=None , *snake_case_ : Optional[Any] ):
__a : Union[str, Any] = testing
__a : List[Any] = testing_file
__a : Any = path
def lowerCAmelCase (self : int ):
warnings.warn(
'''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
'''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
'''checks, you should use `transformers-cli add-new-model-like` instead.''' )
if not _has_cookiecutter:
raise ImportError(
'''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
'''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n''' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
__a : Union[str, Any] = [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
if len(snake_case_ ) > 0:
raise ValueError(
'''Several directories starting with `cookiecutter-template-` in current working directory. '''
'''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
'''change your working directory.''' )
__a : Union[str, Any] = (
Path(snake_case_ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
__a : Union[str, Any] = path_to_transformer_root / '''templates''' / '''adding_a_new_model'''
# Execute cookiecutter
if not self._testing:
cookiecutter(str(snake_case_ ) )
else:
with open(self._testing_file , '''r''' ) as configuration_file:
__a : List[Any] = json.load(snake_case_ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=snake_case_ , extra_context=snake_case_ , )
__a : List[str] = [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:2_2]][0]
# Retrieve configuration
with open(directory + '''/configuration.json''' , '''r''' ) as configuration_file:
__a : Optional[Any] = json.load(snake_case_ )
__a : str = configuration['''lowercase_modelname''']
__a : int = configuration['''generate_tensorflow_pytorch_and_flax''']
os.remove(f"{directory}/configuration.json" )
__a : Any = '''PyTorch''' in generate_tensorflow_pytorch_and_flax
__a : Dict = '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
__a : Optional[int] = '''Flax''' in generate_tensorflow_pytorch_and_flax
__a : Dict = f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(snake_case_ , exist_ok=snake_case_ )
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}" , exist_ok=snake_case_ )
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py" , '''w''' ):
pass
shutil.move(
f"{directory}/__init__.py" , f"{model_dir}/__init__.py" , )
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py" , f"{model_dir}/configuration_{lowercase_model_name}.py" , )
def remove_copy_lines(snake_case_ : Union[str, Any] ):
with open(snake_case_ , '''r''' ) as f:
__a : Union[str, Any] = f.readlines()
with open(snake_case_ , '''w''' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(snake_case_ )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py" , f"{model_dir}/modeling_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py" , f"{model_dir}/modeling_tf_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py" )
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py" , f"{model_dir}/modeling_flax_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/{lowercase_model_name}.md" , f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md" , )
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}_fast.py" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(snake_case_ : str , snake_case_ : str , snake_case_ : List[str] ):
# Create temp file
__a , __a : Tuple = mkstemp()
__a : Optional[Any] = False
with fdopen(snake_case_ , '''w''' ) as new_file:
with open(snake_case_ ) as old_file:
for line in old_file:
new_file.write(snake_case_ )
if line_to_copy_below in line:
__a : Tuple = True
for line_to_copy in lines_to_copy:
new_file.write(snake_case_ )
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file." )
# Copy the file permissions from the old file to the new file
copymode(snake_case_ , snake_case_ )
# Remove original file
remove(snake_case_ )
# Move new file
move(snake_case_ , snake_case_ )
def skip_units(snake_case_ : Any ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(snake_case_ : int ):
with open(snake_case_ ) as datafile:
__a : List[Any] = []
__a : int = False
__a : Tuple = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
__a : Optional[Any] = line.split('''"''' )[1]
__a : Dict = skip_units(snake_case_ )
elif "# Below: " in line and "##" not in line:
__a : str = line.split('''"''' )[1]
__a : Any = skip_units(snake_case_ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(snake_case_ , snake_case_ , snake_case_ )
__a : str = []
elif "# Replace with" in line and "##" not in line:
__a : Optional[int] = []
elif "##" not in line:
lines_to_copy.append(snake_case_ )
remove(snake_case_ )
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py" )
os.rmdir(snake_case_ )
| 216
| 0
|
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_UpperCamelCase = {
# 1536-bit
5: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 2048-bit
14: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 3072-bit
15: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 4096-bit
16: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
+ """FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 6144-bit
17: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
+ """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
+ """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
+ """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
+ """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
+ """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
+ """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
+ """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
+ """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
+ """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
+ """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
+ """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
+ """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
+ """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
+ """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
+ """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
+ """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
+ """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
+ """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
+ """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
+ """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
+ """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
+ """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
+ """6DCC4024FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 8192-bit
18: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
+ """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
+ """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
+ """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
+ """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
+ """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
+ """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
+ """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
+ """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
+ """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
+ """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
+ """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
+ """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
+ """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
+ """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
+ """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
+ """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
+ """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
+ """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
+ """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
+ """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
}
class lowerCamelCase__ :
def __init__( self ,A = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
UpperCAmelCase = primes[group]["""prime"""]
UpperCAmelCase = primes[group]["""generator"""]
UpperCAmelCase = int(hexlify(urandom(32 ) ) ,base=16 )
def _UpperCamelCase ( self ):
return hex(self.__private_key )[2:]
def _UpperCamelCase ( self ):
UpperCAmelCase = pow(self.generator ,self.__private_key ,self.prime )
return hex(A )[2:]
def _UpperCamelCase ( self ,A ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(A ,(self.prime - 1) // 2 ,self.prime ) == 1
)
def _UpperCamelCase ( self ,A ):
UpperCAmelCase = int(A ,base=16 )
if not self.is_valid_public_key(A ):
raise ValueError("""Invalid public key""" )
UpperCAmelCase = pow(A ,self.__private_key ,self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def _UpperCamelCase ( A ,A ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(A ,(prime - 1) // 2 ,A ) == 1
)
@staticmethod
def _UpperCamelCase ( A ,A ,A = 14 ):
UpperCAmelCase = int(A ,base=16 )
UpperCAmelCase = int(A ,base=16 )
UpperCAmelCase = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(A ,A ):
raise ValueError("""Invalid public key""" )
UpperCAmelCase = pow(A ,A ,A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 234
|
"""simple docstring"""
def _a ( _snake_case = 6008_5147_5143 ):
"""simple docstring"""
try:
UpperCAmelCase = int(_snake_case )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
UpperCAmelCase = 1
UpperCAmelCase = 2
while i * i <= n:
while n % i == 0:
UpperCAmelCase = i
n //= i
i += 1
if n > 1:
UpperCAmelCase = n
return int(_snake_case )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 234
| 1
|
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__(self : List[Any] , a__ : Optional[int] , a__ : Optional[int]=13 , a__ : List[Any]=7 , a__ : Dict=True , a__ : Optional[Any]=True , a__ : List[Any]=True , a__ : Optional[Any]=True , a__ : Optional[int]=99 , a__ : Optional[Any]=32 , a__ : List[str]=5 , a__ : Any=4 , a__ : str=37 , a__ : Optional[int]="gelu" , a__ : Optional[Any]=0.1 , a__ : Dict=0.1 , a__ : Any=512 , a__ : Union[str, Any]=16 , a__ : Any=2 , a__ : Optional[int]=0.0_2 , a__ : Optional[int]=4 , ):
"""simple docstring"""
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_attention_mask
__snake_case = use_token_type_ids
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = type_vocab_size
__snake_case = type_sequence_label_size
__snake_case = initializer_range
__snake_case = num_choices
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_attention_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case = None
if self.use_token_type_ids:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case = RobertaPreLayerNormConfig(
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 a (self : List[Any] ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def a (self : Dict ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = True
__snake_case = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__snake_case = 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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , unittest.TestCase ):
A_ : Any = True
A_ : Optional[Any] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a (self : Dict ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormModelTester(self )
@slow
def a (self : List[Any] ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
__snake_case = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = model(np.ones((1, 1) ) )
self.assertIsNotNone(a__ )
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def a (self : str ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
__snake_case = model(a__ )[0]
__snake_case = [1, 11, 5_0265]
self.assertEqual(list(output.shape ) , a__ )
# compare the actual values for a slice.
__snake_case = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
@slow
def a (self : Any ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
__snake_case = model(a__ )[0]
# compare the actual values for a slice.
__snake_case = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
| 24
|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 36
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : int , _UpperCamelCase : str , _UpperCamelCase : Dict ) -> Optional[int]:
'''simple docstring'''
if openai_config_file == "":
UpperCamelCase__ = OpenAIGPTConfig()
else:
UpperCamelCase__ = OpenAIGPTConfig.from_json_file(_UpperCamelCase )
UpperCamelCase__ = OpenAIGPTModel(_UpperCamelCase )
# Load weights from numpy
load_tf_weights_in_openai_gpt(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save pytorch-model
UpperCamelCase__ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCamelCase__ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , _UpperCamelCase )
print(F'Save configuration file to {pytorch_config_dump_path}' )
with open(_UpperCamelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__lowercase: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
__lowercase: int = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 369
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase: Dict = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase: Optional[int] = [
"TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TimeSeriesTransformerForPrediction",
"TimeSeriesTransformerModel",
"TimeSeriesTransformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__lowercase: Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 31
| 0
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_a = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCamelCase__ = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
UpperCamelCase__ = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
UpperCamelCase__ = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = ZeroShotClassificationPipeline(
model=UpperCAmelCase , tokenizer=UpperCAmelCase , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = classifier('Who are you voting for in 2020?' , candidate_labels='politics' )
self.assertEqual(UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase )]} )
# No kwarg
_UpperCAmelCase = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase )]} )
_UpperCAmelCase = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase )]} )
_UpperCAmelCase = classifier('Who are you voting for in 2020?' , candidate_labels='politics, public health' )
self.assertEqual(
UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_UpperCAmelCase = classifier('Who are you voting for in 2020?' , candidate_labels=['politics', 'public health'] )
self.assertEqual(
UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_UpperCAmelCase = classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='This text is about {}' )
self.assertEqual(UpperCAmelCase , {'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
_UpperCAmelCase = classifier(['I am happy'] , ['positive', 'negative'] )
self.assertEqual(
UpperCAmelCase , [
{'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )]}
for i in range(1 )
] , )
_UpperCAmelCase = classifier(['I am happy', 'I am sad'] , ['positive', 'negative'] )
self.assertEqual(
UpperCAmelCase , [
{'sequence': ANY(UpperCAmelCase ), 'labels': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )], 'scores': [ANY(UpperCAmelCase ), ANY(UpperCAmelCase )]}
for i in range(2 )
] , )
with self.assertRaises(UpperCAmelCase ):
classifier('' , candidate_labels='politics' )
with self.assertRaises(UpperCAmelCase ):
classifier(UpperCAmelCase , candidate_labels='politics' )
with self.assertRaises(UpperCAmelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels='' )
with self.assertRaises(UpperCAmelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels=UpperCAmelCase )
with self.assertRaises(UpperCAmelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='Not formatting template' , )
with self.assertRaises(UpperCAmelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template=UpperCAmelCase , )
self.run_entailment_id(UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = zero_shot_classifier.model.config
_UpperCAmelCase = config.labelaid
_UpperCAmelCase = zero_shot_classifier.entailment_id
_UpperCAmelCase = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
_UpperCAmelCase = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_UpperCAmelCase = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_UpperCAmelCase = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
_UpperCAmelCase = original_labelaid
self.assertEqual(UpperCAmelCase , zero_shot_classifier.entailment_id )
@require_torch
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 100 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
_UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_33, 0.3_33, 0.3_33],
} , )
@require_tf
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
_UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_33, 0.3_33, 0.3_33],
} , )
@slow
@require_torch
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
_UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_76, 0.0_15, 0.0_09],
} , )
_UpperCAmelCase = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=UpperCAmelCase , )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
@slow
@require_tf
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
_UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_76, 0.0_15, 0.0_09],
} , )
_UpperCAmelCase = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=UpperCAmelCase , )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
| 39
|
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = 42
__A = None
def a( A : Optional[Any] , A : Any=0.999 , A : Dict="cosine" , ) -> Optional[int]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(A : Optional[int] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
a = []
for i in range(A ):
a = i / num_diffusion_timesteps
a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A ) / alpha_bar_fn(A ) , A ) )
return torch.tensor(A , dtype=torch.floataa )
class _lowercase ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
__A = 1
@register_to_config
def __init__(self , lowerCamelCase_ = 1000 , lowerCamelCase_ = 0.0001 , lowerCamelCase_ = 0.02 , lowerCamelCase_ = "linear" , lowerCamelCase_ = None , lowerCamelCase_ = True , lowerCamelCase_ = True , lowerCamelCase_ = 0 , lowerCamelCase_ = "epsilon" , lowerCamelCase_ = 1.0 , **lowerCamelCase_ , ):
"""simple docstring"""
if kwargs.get("set_alpha_to_one" , lowerCamelCase_ ) is not None:
a = (
"The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
)
deprecate("set_alpha_to_one" , "1.0.0" , lowerCamelCase_ , standard_warn=lowerCamelCase_ )
a = kwargs["set_alpha_to_one"]
if trained_betas is not None:
a = torch.tensor(lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
a = torch.linspace(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
a = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCamelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
a = betas_for_alpha_bar(lowerCamelCase_ )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
a = 1.0 - self.betas
a = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
a = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
a = 1.0
# setable values
a = None
a = torch.from_numpy(np.arange(0 , lowerCamelCase_ ).copy().astype(np.intaa ) )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
return sample
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
F''' maximal {self.config.num_train_timesteps} timesteps.''' )
a = num_inference_steps
a = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
a = (np.arange(0 , lowerCamelCase_ ) * step_ratio).round().copy().astype(np.intaa )
a = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
self.timesteps += self.config.steps_offset
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0.0 , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = True , ):
"""simple docstring"""
a = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
a = self.alphas_cumprod[timestep]
a = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
a = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
a = model_output
elif self.config.prediction_type == "sample":
a = model_output
a = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
a = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
a = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
" `v_prediction`" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
a = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowerCamelCase_ , pred_original_sample=lowerCamelCase_ )
def __len__(self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 227
| 0
|
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : int = {'vocab_file': 'spiece.model'}
__A : Union[str, Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
__A : List[Any] = {
'google/bigbird-roberta-base': 4_096,
'google/bigbird-roberta-large': 4_096,
'google/bigbird-base-trivia-itc': 4_096,
}
class __UpperCamelCase ( lowercase__ ):
lowercase : Optional[Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict = ['input_ids', 'attention_mask']
lowercase : List[int] = []
def __init__( self :List[str] ,_UpperCamelCase :List[Any] ,_UpperCamelCase :Any="<unk>" ,_UpperCamelCase :Tuple="<s>" ,_UpperCamelCase :int="</s>" ,_UpperCamelCase :Optional[Any]="<pad>" ,_UpperCamelCase :List[str]="[SEP]" ,_UpperCamelCase :str="[MASK]" ,_UpperCamelCase :int="[CLS]" ,_UpperCamelCase :Optional[Dict[str, Any]] = None ,**_UpperCamelCase :Optional[Any] ,):
snake_case_ : Dict = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else bos_token
snake_case_ : List[Any] = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else eos_token
snake_case_ : str = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else unk_token
snake_case_ : Optional[int] = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else pad_token
snake_case_ : Optional[int] = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else cls_token
snake_case_ : int = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ : Optional[int] = AddedToken(_UpperCamelCase ,lstrip=_UpperCamelCase ,rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else mask_token
snake_case_ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCamelCase ,eos_token=_UpperCamelCase ,unk_token=_UpperCamelCase ,pad_token=_UpperCamelCase ,sep_token=_UpperCamelCase ,mask_token=_UpperCamelCase ,cls_token=_UpperCamelCase ,sp_model_kwargs=self.sp_model_kwargs ,**_UpperCamelCase ,)
snake_case_ : Union[str, Any] = vocab_file
snake_case_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@property
def a__ ( self :Union[str, Any] ):
return self.sp_model.get_piece_size()
def a__ ( self :List[str] ):
snake_case_ : Any = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self :Optional[Any] ):
snake_case_ : str = self.__dict__.copy()
snake_case_ : Tuple = None
return state
def __setstate__( self :Tuple ,_UpperCamelCase :Tuple ):
snake_case_ : Tuple = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
snake_case_ : Optional[Any] = {}
snake_case_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a__ ( self :int ,_UpperCamelCase :str ):
return self.sp_model.encode(_UpperCamelCase ,out_type=_UpperCamelCase )
def a__ ( self :Any ,_UpperCamelCase :Union[str, Any] ):
return self.sp_model.piece_to_id(_UpperCamelCase )
def a__ ( self :str ,_UpperCamelCase :Tuple ):
snake_case_ : List[str] = self.sp_model.IdToPiece(_UpperCamelCase )
return token
def a__ ( self :Any ,_UpperCamelCase :List[Any] ):
snake_case_ : Union[str, Any] = []
snake_case_ : Optional[Any] = """"""
snake_case_ : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
snake_case_ : Optional[int] = True
snake_case_ : int = []
else:
current_sub_tokens.append(_UpperCamelCase )
snake_case_ : Any = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def a__ ( self :Optional[Any] ,_UpperCamelCase :List[int] ,_UpperCamelCase :bool = False ,_UpperCamelCase :bool = None ,_UpperCamelCase :bool = True ,**_UpperCamelCase :str ,):
snake_case_ : Optional[Any] = kwargs.pop("""use_source_tokenizer""" ,_UpperCamelCase )
snake_case_ : Optional[Any] = self.convert_ids_to_tokens(_UpperCamelCase ,skip_special_tokens=_UpperCamelCase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
snake_case_ : List[str] = []
snake_case_ : str = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase ) )
snake_case_ : List[Any] = []
sub_texts.append(_UpperCamelCase )
else:
current_sub_text.append(_UpperCamelCase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
snake_case_ : List[Any] = re.sub(R""" (\[(MASK|SEP)\])""" ,R"""\1""" ,""" """.join(_UpperCamelCase ) )
else:
snake_case_ : str = """""".join(_UpperCamelCase )
snake_case_ : List[str] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
snake_case_ : Tuple = self.clean_up_tokenization(_UpperCamelCase )
return clean_text
else:
return text
def a__ ( self :str ,_UpperCamelCase :str ,_UpperCamelCase :Optional[str] = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ : Any = os.path.join(
_UpperCamelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase ,"""wb""" ) as fi:
snake_case_ : int = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,)
def a__ ( self :Tuple ,_UpperCamelCase :List[int] ,_UpperCamelCase :Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ : Dict = [self.cls_token_id]
snake_case_ : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def a__ ( self :List[str] ,_UpperCamelCase :List[int] ,_UpperCamelCase :Optional[List[int]] = None ,_UpperCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase ,token_ids_a=_UpperCamelCase ,already_has_special_tokens=_UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(_UpperCamelCase )) + [1]
return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def a__ ( self :Any ,_UpperCamelCase :List[int] ,_UpperCamelCase :Optional[List[int]] = None ):
snake_case_ : List[str] = [self.sep_token_id]
snake_case_ : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
| 8
|
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Tuple=None ):
'''simple docstring'''
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
snake_case_ : Optional[Any] = nn.Parameter(lowerCamelCase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
snake_case_ : List[str] = nn.Parameter(lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :Dict , lowerCamelCase_ :List[Any] ):
'''simple docstring'''
# set torch weights for 1-to-1 comparison
snake_case_ : Optional[Any] = np.asarray(weights[0] )
snake_case_ : int = np.asarray(weights[1] )
snake_case_ : Any = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase_ ).view(-1 , lowerCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase ( lowerCamelCase_ :List[str] , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Optional[Any] ):
'''simple docstring'''
# set torch weights for 1-to-1 comparison
snake_case_ : List[Any] = np.asarray(weights[0] )
snake_case_ : Optional[int] = np.asarray(weights[1] )
snake_case_ : Union[str, Any] = np.asarray(weights[2] )
snake_case_ : int = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase_ ).view(-1 , lowerCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCamelCase_ :List[str] , lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
# layernorm 1
snake_case_ : str = weights[0][0][0]
snake_case_ : int = np.asarray(layer_norm_a[0] )
snake_case_ : Optional[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# lsh weights + output
snake_case_ : Tuple = weights[0][1]
if len(lowerCamelCase_ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase_ , torch_block.attention , lowerCamelCase_ )
else:
set_layer_weights_in_torch_local(lowerCamelCase_ , torch_block.attention , lowerCamelCase_ )
# intermediate weighs
snake_case_ : str = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase_ ) == 4:
snake_case_ : List[Any] = intermediate_weights[2]
# layernorm 2
snake_case_ : Tuple = np.asarray(intermediate_weights[0][0] )
snake_case_ : Optional[Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# intermediate dense
snake_case_ : Any = np.asarray(intermediate_weights[1][0] )
snake_case_ : List[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
# intermediate out
snake_case_ : List[Any] = np.asarray(intermediate_weights[4][0] )
snake_case_ : Union[str, Any] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
def UpperCAmelCase ( lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :str , lowerCamelCase_ :Any ):
'''simple docstring'''
# reformer model
snake_case_ : Dict = torch_model.reformer
# word embeds
snake_case_ : List[Any] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase_ ) , )
if isinstance(weights[3] , lowerCamelCase_ ):
snake_case_ : Tuple = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
snake_case_ : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
snake_case_ : Optional[Any] = nn.Parameter(torch.tensor(lowerCamelCase_ ) )
snake_case_ : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
snake_case_ : str = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# output layer norm
snake_case_ : Optional[Any] = np.asarray(weights[7][0] )
snake_case_ : List[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# output embeddings
snake_case_ : Optional[int] = np.asarray(weights[9][0] )
snake_case_ : Any = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCamelCase_ :Dict , lowerCamelCase_ :List[Any] ):
'''simple docstring'''
# Initialise PyTorch model
snake_case_ : List[str] = ReformerConfig.from_json_file(lowerCamelCase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case_ : str = ReformerModelWithLMHead(lowerCamelCase_ )
with open(lowerCamelCase_ , """rb""" ) as f:
snake_case_ : List[Any] = pickle.load(lowerCamelCase_ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase_ , lowerCamelCase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowerCamelCase_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained Reformer model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__A : List[Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 8
| 1
|
'''simple docstring'''
import re
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[int] = split_input(str_ )
return "".join(
["""""".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : List[Any] = split_input(lowerCamelCase__ )
if upper:
A_ : Tuple = """""".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
A_ : Optional[int] = """""".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ ):
'''simple docstring'''
return to_simple_case(lowerCamelCase__ )
def a ( lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : Tuple = to_simple_case(lowerCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """_""" )
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """-""" )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 206
|
'''simple docstring'''
import re
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[int] = split_input(str_ )
return "".join(
["""""".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : List[Any] = split_input(lowerCamelCase__ )
if upper:
A_ : Tuple = """""".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
A_ : Optional[int] = """""".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ ):
'''simple docstring'''
return to_simple_case(lowerCamelCase__ )
def a ( lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : Tuple = to_simple_case(lowerCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """_""" )
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """-""" )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 206
| 1
|
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_UpperCamelCase = {
# 1536-bit
5: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
# 2048-bit
14: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AACAA68FFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
# 3072-bit
15: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
# 4096-bit
16: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
+ '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
+ '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
+ '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
+ '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
+ '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'''
+ '''FFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
# 6144-bit
17: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'''
+ '''8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'''
+ '''302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'''
+ '''A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'''
+ '''49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'''
+ '''FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'''
+ '''180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'''
+ '''3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'''
+ '''04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'''
+ '''B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'''
+ '''1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'''
+ '''E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'''
+ '''99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'''
+ '''04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'''
+ '''233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'''
+ '''D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
+ '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'''
+ '''AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'''
+ '''DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'''
+ '''2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'''
+ '''F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'''
+ '''BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
+ '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'''
+ '''B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'''
+ '''387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'''
+ '''6DCC4024FFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
# 8192-bit
18: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
+ '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
+ '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
+ '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
+ '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
+ '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
+ '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'''
+ '''F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'''
+ '''179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'''
+ '''DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'''
+ '''5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'''
+ '''D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'''
+ '''23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
+ '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'''
+ '''06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'''
+ '''DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'''
+ '''12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'''
+ '''38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'''
+ '''741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'''
+ '''3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'''
+ '''22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'''
+ '''4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'''
+ '''062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'''
+ '''4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'''
+ '''B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'''
+ '''4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'''
+ '''9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'''
+ '''60C980DD98EDD3DFFFFFFFFFFFFFFFFF''',
base=16,
),
'''generator''': 2,
},
}
class lowercase :
'''simple docstring'''
def __init__(self , __a = 14 ) -> Optional[int]:
"""simple docstring"""
if group not in primes:
raise ValueError('Unsupported Group' )
UpperCAmelCase__ = primes[group]['prime']
UpperCAmelCase__ = primes[group]['generator']
UpperCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
return hex(self.__private_key )[2:]
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(_UpperCAmelCase )[2:]
def UpperCamelCase__ (self , __a ) -> List[Any]:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(_UpperCAmelCase , (self.prime - 1) // 2 , self.prime ) == 1
)
def UpperCamelCase__ (self , __a ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = int(_UpperCAmelCase , base=16 )
if not self.is_valid_public_key(_UpperCAmelCase ):
raise ValueError('Invalid public key' )
UpperCAmelCase__ = pow(_UpperCAmelCase , self.__private_key , self.prime )
return shaaaa(str(_UpperCAmelCase ).encode() ).hexdigest()
@staticmethod
def UpperCamelCase__ (__a , __a ) -> Dict:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(_UpperCAmelCase , (prime - 1) // 2 , _UpperCAmelCase ) == 1
)
@staticmethod
def UpperCamelCase__ (__a , __a , __a = 14 ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = int(_UpperCAmelCase , base=16 )
UpperCAmelCase__ = int(_UpperCAmelCase , base=16 )
UpperCAmelCase__ = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Invalid public key' )
UpperCAmelCase__ = pow(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return shaaaa(str(_UpperCAmelCase ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369
|
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 335
| 0
|
'''simple docstring'''
import sys
def UpperCamelCase_ ( snake_case_ : int ) -> List[Any]:
'''simple docstring'''
__lowerCAmelCase = len(snake_case_ )
__lowerCAmelCase = [[0 for x in range(snake_case_ )] for x in range(snake_case_ )]
__lowerCAmelCase = [[0 for x in range(snake_case_ )] for x in range(snake_case_ )]
for chain_length in range(2 , snake_case_ ):
for a in range(1 , n - chain_length + 1 ):
__lowerCAmelCase = a + chain_length - 1
__lowerCAmelCase = sys.maxsize
for c in range(snake_case_ , snake_case_ ):
__lowerCAmelCase = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
__lowerCAmelCase = cost
__lowerCAmelCase = c
return matrix, sol
def UpperCamelCase_ ( snake_case_ : List[str] , snake_case_ : int , snake_case_ : int ) -> Optional[int]:
'''simple docstring'''
if i == j:
print("""A""" + str(snake_case_ ) , end=""" """ )
else:
print("""(""" , end=""" """ )
print_optiomal_solution(snake_case_ , snake_case_ , optimal_solution[i][j] )
print_optiomal_solution(snake_case_ , optimal_solution[i][j] + 1 , snake_case_ )
print(""")""" , end=""" """ )
def UpperCamelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase = [30, 35, 15, 5, 10, 20, 25]
__lowerCAmelCase = len(snake_case_ )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
__lowerCAmelCase , __lowerCAmelCase = matrix_chain_order(snake_case_ )
print("""No. of Operation required: """ + str(matrix[1][n - 1] ) )
print_optiomal_solution(snake_case_ , 1 , n - 1 )
if __name__ == "__main__":
main()
| 229
|
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_A : Optional[Any] = 16
_A : Union[str, Any] = 32
def UpperCamelCase_ ( snake_case_ : List[str] ) -> str:
'''simple docstring'''
return int(x / 2**20 )
class _lowercase :
'''simple docstring'''
def __enter__( self : List[Any] ) -> int:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
__lowerCAmelCase = torch.cuda.memory_allocated()
return self
def __exit__( self : Tuple , *SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[Any]:
gc.collect()
torch.cuda.empty_cache()
__lowerCAmelCase = torch.cuda.memory_allocated()
__lowerCAmelCase = torch.cuda.max_memory_allocated()
__lowerCAmelCase = bamb(self.end - self.begin )
__lowerCAmelCase = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def UpperCamelCase_ ( snake_case_ : Accelerator , snake_case_ : int = 16 , snake_case_ : str = "bert-base-cased" , snake_case_ : int = 3_20 , snake_case_ : int = 1_60 , ) -> Optional[int]:
'''simple docstring'''
__lowerCAmelCase = AutoTokenizer.from_pretrained(snake_case_ )
__lowerCAmelCase = load_dataset(
"""glue""" , """mrpc""" , split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""} )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case_ , max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowerCAmelCase = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=snake_case_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(snake_case_ : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case_ , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(snake_case_ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
__lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
def UpperCamelCase_ ( snake_case_ : List[Any] , snake_case_ : Tuple ) -> Optional[int]:
'''simple docstring'''
__lowerCAmelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase = config["""lr"""]
__lowerCAmelCase = int(config["""num_epochs"""] )
__lowerCAmelCase = int(config["""seed"""] )
__lowerCAmelCase = int(config["""batch_size"""] )
__lowerCAmelCase = args.model_name_or_path
set_seed(snake_case_ )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(snake_case_ , snake_case_ , snake_case_ , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(snake_case_ , return_dict=snake_case_ )
# Instantiate optimizer
__lowerCAmelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowerCAmelCase = optimizer_cls(params=model.parameters() , lr=snake_case_ )
if accelerator.state.deepspeed_plugin is not None:
__lowerCAmelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
__lowerCAmelCase = 1
__lowerCAmelCase = (len(snake_case_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=0 , num_training_steps=snake_case_ , )
else:
__lowerCAmelCase = DummyScheduler(snake_case_ , total_num_steps=snake_case_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# We need to keep track of how many total steps we have iterated over
__lowerCAmelCase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowerCAmelCase = 0
# Now we train the model
__lowerCAmelCase = {}
for epoch in range(snake_case_ , snake_case_ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(snake_case_ ):
__lowerCAmelCase = model(**snake_case_ )
__lowerCAmelCase = outputs.loss
__lowerCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(snake_case_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
__lowerCAmelCase = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """peak_memory_utilization.json""" ) , """w""" ) as f:
json.dump(snake_case_ , snake_case_ )
def UpperCamelCase_ ( ) -> Any:
'''simple docstring'''
__lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=snake_case_ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=snake_case_ , )
parser.add_argument(
"""--output_dir""" , type=snake_case_ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--peak_memory_upper_bound""" , type=snake_case_ , default=snake_case_ , help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" , )
parser.add_argument(
"""--n_train""" , type=snake_case_ , default=3_20 , help="""Number of training examples to use.""" , )
parser.add_argument(
"""--n_val""" , type=snake_case_ , default=1_60 , help="""Number of validation examples to use.""" , )
parser.add_argument(
"""--num_epochs""" , type=snake_case_ , default=1 , help="""Number of train epochs.""" , )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 229
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase : Union[str, Any] = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Tuple = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : int = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
lowercase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 36
|
import numpy as np
def lowerCAmelCase__ ( _a : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
| 1
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __A ( a , a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =StableDiffusionDiffEditPipeline
UpperCamelCase__ : str =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
UpperCamelCase__ : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
UpperCamelCase__ : Dict =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase__ : Any =frozenset([] )
def __lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : Dict =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowerCamelCase__ , )
__UpperCamelCase : List[str] =DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase__ , set_alpha_to_one=lowerCamelCase__ , )
__UpperCamelCase : Union[str, Any] =DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase__ , set_alpha_to_zero=lowerCamelCase__ , )
torch.manual_seed(0 )
__UpperCamelCase : Optional[int] =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__UpperCamelCase : Tuple =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
__UpperCamelCase : Any =CLIPTextModel(lowerCamelCase__ )
__UpperCamelCase : int =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__UpperCamelCase : Union[str, Any] ={
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : int =floats_tensor((1, 16, 16) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : Any =floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : Any =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : Optional[int] =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Dict ={
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : Tuple =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCamelCase : Optional[Any] =Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert('RGB' )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : List[Any] =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : Any =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Optional[int] ={
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : str =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCamelCase : int =Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert('RGB' )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : Any =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : int =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Optional[int] ={
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class , '_optional_components' ):
return
__UpperCamelCase : Optional[Any] =self.get_dummy_components()
__UpperCamelCase : List[str] =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
__UpperCamelCase : Union[str, Any] =self.get_dummy_inputs(lowerCamelCase__ )
__UpperCamelCase : List[Any] =pipe(**lowerCamelCase__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase__ )
__UpperCamelCase : Tuple =self.pipeline_class.from_pretrained(lowerCamelCase__ )
pipe_loaded.to(lowerCamelCase__ )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase__ , lowerCamelCase__ ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
__UpperCamelCase : str =self.get_dummy_inputs(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =pipe_loaded(**lowerCamelCase__ )[0]
__UpperCamelCase : Tuple =np.abs(output - output_loaded ).max()
self.assertLess(lowerCamelCase__ , 1E-4 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any ='cpu'
__UpperCamelCase : Union[str, Any] =self.get_dummy_components()
__UpperCamelCase : Any =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : int =self.get_dummy_mask_inputs(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =pipe.generate_mask(**lowerCamelCase__ )
__UpperCamelCase : int =mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
__UpperCamelCase : Tuple =np.array([0] * 9 )
__UpperCamelCase : str =np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int ='cpu'
__UpperCamelCase : Union[str, Any] =self.get_dummy_components()
__UpperCamelCase : Optional[Any] =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Dict =self.get_dummy_inversion_inputs(lowerCamelCase__ )
__UpperCamelCase : List[Any] =pipe.invert(**lowerCamelCase__ ).images
__UpperCamelCase : Optional[Any] =image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__UpperCamelCase : List[str] =np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__UpperCamelCase : int =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
def __lowercase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] ='cpu'
__UpperCamelCase : int =self.get_dummy_components()
__UpperCamelCase : str ={'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
__UpperCamelCase : str =DPMSolverMultistepScheduler(**lowerCamelCase__ )
__UpperCamelCase : Dict =DPMSolverMultistepInverseScheduler(**lowerCamelCase__ )
__UpperCamelCase : Any =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Tuple =self.get_dummy_inversion_inputs(lowerCamelCase__ )
__UpperCamelCase : str =pipe.invert(**lowerCamelCase__ ).images
__UpperCamelCase : List[Any] =image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__UpperCamelCase : List[str] =np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__UpperCamelCase : Optional[Any] =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
@require_torch_gpu
@slow
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def __lowercase ( cls ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )
__UpperCamelCase : Union[str, Any] =raw_image.convert('RGB' ).resize((768, 768) )
__UpperCamelCase : List[Any] =raw_image
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =torch.manual_seed(0 )
__UpperCamelCase : Dict =StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=lowerCamelCase__ , torch_dtype=torch.floataa )
__UpperCamelCase : List[str] =DDIMScheduler.from_config(pipe.scheduler.config )
__UpperCamelCase : List[str] =DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[str] ='a bowl of fruit'
__UpperCamelCase : Dict ='a bowl of pears'
__UpperCamelCase : Tuple =pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase__ , target_prompt=lowerCamelCase__ , generator=lowerCamelCase__ , )
__UpperCamelCase : int =pipe.invert(
prompt=lowerCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase__ ).latents
__UpperCamelCase : Dict =pipe(
prompt=lowerCamelCase__ , mask_image=lowerCamelCase__ , image_latents=lowerCamelCase__ , generator=lowerCamelCase__ , negative_prompt=lowerCamelCase__ , inpaint_strength=0.7 , output_type='numpy' , ).images[0]
__UpperCamelCase : str =(
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =torch.manual_seed(0 )
__UpperCamelCase : List[Any] =StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=lowerCamelCase__ , torch_dtype=torch.floataa )
__UpperCamelCase : Optional[Any] =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__UpperCamelCase : Optional[int] =DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Optional[Any] ='a bowl of fruit'
__UpperCamelCase : int ='a bowl of pears'
__UpperCamelCase : str =pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase__ , target_prompt=lowerCamelCase__ , generator=lowerCamelCase__ , )
__UpperCamelCase : List[str] =pipe.invert(
prompt=lowerCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase__ , num_inference_steps=25 , ).latents
__UpperCamelCase : List[str] =pipe(
prompt=lowerCamelCase__ , mask_image=lowerCamelCase__ , image_latents=lowerCamelCase__ , generator=lowerCamelCase__ , negative_prompt=lowerCamelCase__ , inpaint_strength=0.7 , num_inference_steps=25 , output_type='numpy' , ).images[0]
__UpperCamelCase : Tuple =(
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 71
|
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75
| 0
|
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_SCREAMING_SNAKE_CASE = [
{"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
{"""dataset""": """snli""", """config_name""": """plain_text"""},
{"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
{"""dataset""": """wiki40b""", """config_name""": """en"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
{"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def SCREAMING_SNAKE_CASE__ ( __a=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case_ ) )
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
__magic_name__: List[Any] = None
__magic_name__: List[Any] = None
def UpperCAmelCase_ ( self : int , _A : Dict , _A : Any ) -> List[str]:
"""simple docstring"""
with TemporaryDirectory() as tmp_dir:
snake_case_ : str = dataset_module_factory(_A , cache_dir=_A )
snake_case_ : Union[str, Any] = import_main_class(dataset_module.module_path , dataset=_A )
snake_case_ : DatasetBuilder = builder_cls(
cache_dir=_A , config_name=_A , hash=dataset_module.hash , )
snake_case_ : Union[str, Any] = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=_A ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
snake_case_ : str = cached_path(_A , cache_dir=_A )
self.assertTrue(os.path.exists(_A ) )
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Optional[int] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
snake_case_ : Any = dataset_module_factory('wikipedia' , cache_dir=__a )
snake_case_ : Union[str, Any] = import_main_class(dataset_module.module_path )
snake_case_ : DatasetBuilder = builder_cls(
cache_dir=__a , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case_ : int = None
builder_instance.download_and_prepare()
snake_case_ : List[Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : int = dataset_module_factory('wikipedia' , cache_dir=__a )
snake_case_ : Optional[int] = import_main_class(dataset_module.module_path , dataset=__a )
snake_case_ : DatasetBuilder = builder_cls(
cache_dir=__a , config_name='20220301.frr' , hash=dataset_module.hash , )
snake_case_ : Dict = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(__a , __a )
assert "train" in ds
assert isinstance(ds['train'] , __a )
assert next(iter(ds['train'] ) )
| 88
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMForMultipleChoice""",
"""XLMForQuestionAnswering""",
"""XLMForQuestionAnsweringSimple""",
"""XLMForSequenceClassification""",
"""XLMForTokenClassification""",
"""XLMModel""",
"""XLMPreTrainedModel""",
"""XLMWithLMHeadModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLMForMultipleChoice""",
"""TFXLMForQuestionAnsweringSimple""",
"""TFXLMForSequenceClassification""",
"""TFXLMForTokenClassification""",
"""TFXLMMainLayer""",
"""TFXLMModel""",
"""TFXLMPreTrainedModel""",
"""TFXLMWithLMHeadModel""",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 88
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : int = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[Any] = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Dict = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : int = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[Any] = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
lowerCamelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 47
|
'''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _lowerCAmelCase ( _UpperCamelCase : Dict , _UpperCamelCase : Any=False ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =OmegaConf.load(_UpperCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(_UpperCamelCase ) ) )
return config
def _lowerCAmelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : Union[str, Any]=None ) -> Optional[Any]:
"""simple docstring"""
if conf_path is None:
_SCREAMING_SNAKE_CASE ='./model_checkpoints/vqgan_only.yaml'
_SCREAMING_SNAKE_CASE =load_config(_UpperCamelCase , display=_UpperCamelCase )
_SCREAMING_SNAKE_CASE =VQModel(**config.model.params )
if ckpt_path is None:
_SCREAMING_SNAKE_CASE ='./model_checkpoints/vqgan_only.pt'
_SCREAMING_SNAKE_CASE =torch.load(_UpperCamelCase , map_location=_UpperCamelCase )
if ".ckpt" in ckpt_path:
_SCREAMING_SNAKE_CASE =sd['state_dict']
model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase )
model.to(_UpperCamelCase )
del sd
return model
def _lowerCAmelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : int ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =model.encode(_UpperCamelCase )
print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
_SCREAMING_SNAKE_CASE =model.decode(_UpperCamelCase )
return xrec
def _lowerCAmelCase ( _UpperCamelCase : Dict , _UpperCamelCase : List[str]=False ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =string.rsplit('.' , 1 )
if reload:
_SCREAMING_SNAKE_CASE =importlib.import_module(_UpperCamelCase )
importlib.reload(_UpperCamelCase )
return getattr(importlib.import_module(_UpperCamelCase , package=_UpperCamelCase ) , cls )
def _lowerCAmelCase ( _UpperCamelCase : str ) -> List[str]:
"""simple docstring"""
if "target" not in config:
raise KeyError('Expected key `target` to instantiate.' )
return get_obj_from_str(config['target'] )(**config.get('params' , {} ) )
def _lowerCAmelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] , _UpperCamelCase : List[str]=True , _UpperCamelCase : int=True ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =instantiate_from_config(_UpperCamelCase )
if sd is not None:
model.load_state_dict(_UpperCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
if ckpt:
_SCREAMING_SNAKE_CASE =torch.load(_UpperCamelCase , map_location='cpu' )
_SCREAMING_SNAKE_CASE =pl_sd['global_step']
print(f"loaded model from global step {global_step}." )
else:
_SCREAMING_SNAKE_CASE ={'state_dict': None}
_SCREAMING_SNAKE_CASE =None
_SCREAMING_SNAKE_CASE =load_model_from_config(config.model , pl_sd['state_dict'] , gpu=_UpperCamelCase , eval_mode=_UpperCamelCase )['model']
return model, global_step
| 47
| 1
|
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
lowerCAmelCase , lowerCAmelCase : Tuple = head.next, head
while fast and fast.next:
lowerCAmelCase : List[str] = fast.next.next
lowerCAmelCase : Dict = slow.next
lowerCAmelCase : Any = slow.next
lowerCAmelCase : int = None # Don't forget here! But forget still works!
# reverse the second part
lowerCAmelCase : Optional[int] = None
while second:
lowerCAmelCase : Union[str, Any] = second.next
lowerCAmelCase : Any = node
lowerCAmelCase : Any = second
lowerCAmelCase : Union[str, Any] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
lowerCAmelCase : List[str] = node.next
lowerCAmelCase : List[str] = head.next
return True
def a__ ( SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
lowerCAmelCase : str = head
while fast and fast.next:
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = fast.next.next, slow.next
# 2. Push the second half into the stack
lowerCAmelCase : str = [slow.val]
while slow.next:
lowerCAmelCase : Optional[Any] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
lowerCAmelCase : Any = cur.next
return True
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if not head or not head.next:
return True
lowerCAmelCase : Any = {}
lowerCAmelCase : Optional[Any] = 0
while head:
if head.val in d:
d[head.val].append(SCREAMING_SNAKE_CASE )
else:
lowerCAmelCase : int = [pos]
lowerCAmelCase : Union[str, Any] = head.next
pos += 1
lowerCAmelCase : Optional[Any] = pos - 1
lowerCAmelCase : Union[str, Any] = 0
for v in d.values():
if len(SCREAMING_SNAKE_CASE ) % 2 != 0:
middle += 1
else:
lowerCAmelCase : Any = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE ) ):
if v[i] + v[len(SCREAMING_SNAKE_CASE ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 133
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowerCAmelCase__ = get_logger()
lowerCAmelCase__ = None
class SCREAMING_SNAKE_CASE__ ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
"""simple docstring"""
def __init__( self , snake_case__=None , snake_case__=None , **snake_case__ ):
"""simple docstring"""
super().__init__(features=snake_case__ )
import jax
from jaxlib.xla_client import Device
if isinstance(snake_case__ , snake_case__ ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(snake_case__ )}, as `jaxlib.xla_extension.Device` """
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
lowerCAmelCase : List[str] = device if isinstance(snake_case__ , snake_case__ ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowerCAmelCase : Optional[Any] = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
lowerCAmelCase : Dict = str(jax.devices()[0] )
lowerCAmelCase : Union[str, Any] = jnp_array_kwargs
@staticmethod
def lowercase__ ( ):
"""simple docstring"""
import jax
return {str(snake_case__ ): device for device in jax.devices()}
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(snake_case__ , snake_case__ ) and column:
if all(
isinstance(snake_case__ , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(snake_case__ , axis=0 )
return column
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(snake_case__ , (str, bytes, type(snake_case__ )) ):
return value
elif isinstance(snake_case__ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowerCAmelCase : Tuple = {}
if isinstance(snake_case__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
lowerCAmelCase : List[Any] = {"dtype": jnp.intaa}
else:
lowerCAmelCase : Optional[Any] = {"dtype": jnp.intaa}
elif isinstance(snake_case__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowerCAmelCase : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(snake_case__ , PIL.Image.Image ):
lowerCAmelCase : Dict = np.asarray(snake_case__ )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowerCAmelCase : str = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(snake_case__ , **{**default_dtype, **self.jnp_array_kwargs} )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(snake_case__ , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(snake_case__ , "__array__" ) and not isinstance(snake_case__ , jax.Array ):
lowerCAmelCase : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(snake_case__ , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(snake_case__ ) for substruct in data_struct] )
elif isinstance(snake_case__ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(snake_case__ ) for substruct in data_struct] )
return self._tensorize(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
return map_nested(self._recursive_tensorize , snake_case__ , map_list=snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[Any] = self.numpy_arrow_extractor().extract_row(snake_case__ )
lowerCAmelCase : Tuple = self.python_features_decoder.decode_row(snake_case__ )
return self.recursive_tensorize(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = self.numpy_arrow_extractor().extract_column(snake_case__ )
lowerCAmelCase : Dict = self.python_features_decoder.decode_column(snake_case__ , pa_table.column_names[0] )
lowerCAmelCase : Optional[Any] = self.recursive_tensorize(snake_case__ )
lowerCAmelCase : Dict = self._consolidate(snake_case__ )
return column
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = self.numpy_arrow_extractor().extract_batch(snake_case__ )
lowerCAmelCase : str = self.python_features_decoder.decode_batch(snake_case__ )
lowerCAmelCase : Tuple = self.recursive_tensorize(snake_case__ )
for column_name in batch:
lowerCAmelCase : Optional[int] = self._consolidate(batch[column_name] )
return batch
| 133
| 1
|
from __future__ import annotations
class __snake_case :
def __init__( self : List[Any] , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = order
# a_{0} ... a_{k}
UpperCAmelCase_ = [1.0] + [0.0] * order
# b_{0} ... b_{k}
UpperCAmelCase_ = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
UpperCAmelCase_ = [0.0] * self.order
# y[n-1] ... y[n-k]
UpperCAmelCase_ = [0.0] * self.order
def lowerCamelCase ( self : Tuple , _snake_case : list[float] , _snake_case : list[float]):
"""simple docstring"""
if len(_snake_case) < self.order:
UpperCAmelCase_ = [1.0, *a_coeffs]
if len(_snake_case) != self.order + 1:
UpperCAmelCase_ = (
F"""Expected a_coeffs to have {self.order + 1} elements """
F"""for {self.order}-order filter, got {len(_snake_case)}"""
)
raise ValueError(_snake_case)
if len(_snake_case) != self.order + 1:
UpperCAmelCase_ = (
F"""Expected b_coeffs to have {self.order + 1} elements """
F"""for {self.order}-order filter, got {len(_snake_case)}"""
)
raise ValueError(_snake_case)
UpperCAmelCase_ = a_coeffs
UpperCAmelCase_ = b_coeffs
def lowerCamelCase ( self : List[Any] , _snake_case : float):
"""simple docstring"""
UpperCAmelCase_ = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
UpperCAmelCase_ = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
UpperCAmelCase_ = self.input_history[:-1]
UpperCAmelCase_ = self.output_history[:-1]
UpperCAmelCase_ = sample
UpperCAmelCase_ = result
return result
| 51
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__ ( _UpperCAmelCase ):
a_ =["""image_processor""", """tokenizer"""]
a_ ="""LayoutLMv2ImageProcessor"""
a_ =("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase )-> Tuple:
'''simple docstring'''
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs.pop("feature_extractor" )
lowerCAmelCase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = 0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , **__UpperCAmelCase , )-> BatchEncoding:
'''simple docstring'''
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes "
"if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping." )
# first, apply the image processor
lowerCAmelCase__ = self.image_processor(images=__UpperCAmelCase , return_tensors=__UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowerCAmelCase__ = features["words"]
lowerCAmelCase__ = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
# add pixel values
lowerCAmelCase__ = features.pop("pixel_values" )
if return_overflowing_tokens is True:
lowerCAmelCase__ = self.get_overflowing_images(__UpperCAmelCase , encoded_inputs["overflow_to_sample_mapping"] )
lowerCAmelCase__ = images
return encoded_inputs
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
F" {len(__UpperCAmelCase )} and {len(__UpperCAmelCase )}" )
return images_with_overflow
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> Dict:
'''simple docstring'''
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __UpperCAmelCase , )
return self.image_processor
| 340
| 0
|
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(__UpperCamelCase )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 161
|
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : List[str] , a_ : Any , a_ : Any=None , a_ : int=None , a_ : str=None , a_ : Optional[int]="resnet50" , a_ : str=3 , a_ : str=32 , a_ : Union[str, Any]=3 , a_ : Tuple=True , a_ : List[str]=True , ):
lowerCAmelCase_ : Optional[int] = parent
lowerCAmelCase_ : Dict = out_indices if out_indices is not None else [4]
lowerCAmelCase_ : int = stage_names
lowerCAmelCase_ : Optional[Any] = out_features
lowerCAmelCase_ : Tuple = backbone
lowerCAmelCase_ : List[str] = batch_size
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : List[Any] = num_channels
lowerCAmelCase_ : Optional[int] = use_pretrained_backbone
lowerCAmelCase_ : List[Any] = is_training
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Optional[int] = self.get_config()
return config, pixel_values
def lowerCamelCase ( self : Dict ):
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def lowerCamelCase ( self : Union[str, Any] , a_ : str , a_ : Optional[int] ):
lowerCAmelCase_ : Union[str, Any] = TimmBackbone(config=a_ )
model.to(a_ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : int = model(a_ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = config_and_inputs
lowerCAmelCase_ : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class __lowerCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
'''simple docstring'''
a_ : Union[str, Any] = (TimmBackbone,) if is_torch_available() else ()
a_ : int = {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
a_ : Union[str, Any] = False
a_ : str = False
a_ : List[Any] = False
a_ : Dict = False
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Union[str, Any] = TimmBackboneModelTester(self )
lowerCAmelCase_ : List[str] = ConfigTester(self , config_class=a_ , has_text_modality=a_ )
def lowerCamelCase ( self : Dict ):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Optional[int] = "resnet18"
lowerCAmelCase_ : List[Any] = "microsoft/resnet-18"
lowerCAmelCase_ : Tuple = AutoBackbone.from_pretrained(a_ , use_timm_backbone=a_ )
lowerCAmelCase_ : str = AutoBackbone.from_pretrained(a_ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
lowerCAmelCase_ : Dict = AutoBackbone.from_pretrained(a_ , use_timm_backbone=a_ , out_indices=[1, 2, 3] )
lowerCAmelCase_ : Any = AutoBackbone.from_pretrained(a_ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("TimmBackbone doesn't support feed forward chunking" )
def lowerCamelCase ( self : Optional[int] ):
pass
@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
def lowerCamelCase ( self : Dict ):
pass
@unittest.skip("TimmBackbone initialization is managed on the timm side" )
def lowerCamelCase ( self : List[Any] ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def lowerCamelCase ( self : Dict ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def lowerCamelCase ( self : Any ):
pass
@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
def lowerCamelCase ( self : Tuple ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowerCamelCase ( self : str ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def lowerCamelCase ( self : Any ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def lowerCamelCase ( self : List[str] ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowerCamelCase ( self : Tuple ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowerCamelCase ( self : Optional[int] ):
pass
@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
def lowerCamelCase ( self : Dict ):
pass
@unittest.skip("TimmBackbone doesn't support output_attentions." )
def lowerCamelCase ( self : int ):
pass
@unittest.skip("Safetensors is not supported by timm." )
def lowerCamelCase ( self : Union[str, Any] ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCamelCase ( self : Union[str, Any] ):
pass
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : str = model_class(a_ )
lowerCAmelCase_ : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : str = [*signature.parameters.keys()]
lowerCAmelCase_ : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a_ )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : List[Any] = self.has_attentions
# no need to test all models as different heads yield the same functionality
lowerCAmelCase_ : int = self.all_model_classes[0]
lowerCAmelCase_ : Optional[int] = model_class(a_ )
model.to(a_ )
lowerCAmelCase_ : Union[str, Any] = self._prepare_for_class(a_ , a_ )
lowerCAmelCase_ : str = model(**a_ )
lowerCAmelCase_ : Any = outputs[0][-1]
# Encoder-/Decoder-only models
lowerCAmelCase_ : Optional[int] = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
lowerCAmelCase_ : Optional[int] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=a_ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Dict = model_class(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Tuple = model(**a_ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
lowerCAmelCase_ : Optional[int] = copy.deepcopy(a_ )
lowerCAmelCase_ : Tuple = None
lowerCAmelCase_ : Any = model_class(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : int = model(**a_ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
lowerCAmelCase_ : str = copy.deepcopy(a_ )
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : Optional[int] = model_class(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Optional[Any] = model(**a_ )
| 161
| 1
|
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